diff --git a/.circleci/config.yml b/.circleci/config.yml
index a67ad414e2..711c24dc80 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -253,6 +253,12 @@ jobs:
             cd security-content
             source venv/bin/activate
             python bin/doc-gen.py --path . --output docs -v
+      - run:
+          name: make yamls pretty
+          command: |
+            cd security-content
+            source venv/bin/activate
+            python bin/pretty_yaml.py --path . -v
       - run:
           name: get cti repo for mitre-maps
           command: |
@@ -283,6 +289,7 @@ jobs:
             git config --global push.default simple
             git add package/*
             git add docs/*
+            git add detections/*
             git commit --allow-empty -m "updating docs and package bits [ci skip]"
             # Push quietly to prevent showing the token in log
             git push https://${GITHUB_TOKEN}@github.com/splunk/security-content.git ${CIRCLE_BRANCH}
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index ba42cb9bc7..c8f383cff2 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -13,4 +13,4 @@ validate:
   script:
     - python3 bin/ssa_validate.py --skip-error detections/*/*
     - python3 bin/ssa_test.py --skip-error tests/*/*
-    - python3 bin/testing_coverage.py --type SSA --min-coverage 1.0
+    - python3 bin/testing_coverage.py --type streaming --min-coverage 1.0
diff --git a/baselines/baseline_of_api_calls_per_user_arn.yml b/baselines/baseline_of_api_calls_per_user_arn.yml
index 5a8ac4f66d..c29df505a0 100644
--- a/baselines/baseline_of_api_calls_per_user_arn.yml
+++ b/baselines/baseline_of_api_calls_per_user_arn.yml
@@ -1,21 +1,25 @@
-author: David Dorsey, Splunk
+name: Baseline of API Calls per User ARN
+id: fc0edc96-ff2b-48b0-9f6f-63da3783fd63
+version: 1
 date: '2018-04-09'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search establishes, on a per-hour basis, the average and the standard
   deviation of the number of API calls made by each user. Also recorded is the number
   of data points for each user. This table is then outputted to a lookup file to allow
   the detection search to operate quickly.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs.
-id: fc0edc96-ff2b-48b0-9f6f-63da3783fd63
-name: Baseline of API Calls per User ARN
 search: '`cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn
   | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls)
   as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls,
   stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints,
   avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   detections:
   - Detect Spike in AWS API Activity
@@ -23,5 +27,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml b/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml
index cfec692249..ecd04d8b11 100644
--- a/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml
+++ b/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml
@@ -1,15 +1,15 @@
-author: Bhavin Patel, Splunk
+name: Baseline of blocked outbound traffic from AWS
+id: fc0edd96-ff2b-48b0-9f1f-63da3782fd63
+version: 1
 date: '2018-05-07'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search establishes, on a per-hour basis, the average and the standard
   deviation of the number of outbound connections blocked in your VPC flow logs by
   each source IP address (IP address of your EC2 instances). Also recorded is the
   number of data points for each source IP. This table outputs to a lookup file to
   allow the detection search to operate quickly.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow
-  logs.`.
-id: fc0edd96-ff2b-48b0-9f1f-63da3782fd63
-name: Baseline of blocked outbound traffic from AWS
 search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12
   OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)
   | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip
@@ -18,8 +18,12 @@ search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=17
   as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints,
   avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections
   | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow
+  logs.`.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Command and Control
   - Suspicious AWS Traffic
@@ -29,5 +33,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml b/baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml
index d1f2df8633..f11949b56a 100644
--- a/baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml
+++ b/baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml
@@ -1,11 +1,25 @@
-author: David Dorsey, Splunk
+name: Baseline Of Cloud Infrastructure API Calls Per User
+id: 1da5d5ea-4382-447d-98a9-87c358c95fcb
+version: 1
 date: '2020-09-07'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   for how many API calls are performed by each user. By default, the search uses the
   last 90 days of data to build the model and the model is rebuilt weekly. The model
   created by this search is then used in the corresponding detection search, which
   identifies subsequent outliers in the number of instances created in a small time
   window.
+search: '| tstats count as api_calls from datamodel=Change where All_Changes.user!=unknown
+  All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")`
+  | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval
+  DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek
+  <= 5, 0, 1) | table _time api_calls, user, HourOfDay, isWeekend | eventstats dc(api_calls)
+  as api_calls by user, HourOfDay, isWeekend | where api_calls >= 1 | fit DensityFunction
+  api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_api_calls_v1 dist=norm
+  show_density=true'
 how_to_implement: You must have Enterprise Security 6.0 or later, if not you will
   need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
   installed, along with any required dependencies. Depending on the number of users
@@ -15,18 +29,9 @@ how_to_implement: You must have Enterprise Security 6.0 or later, if not you wil
   90 days of data. You can modify the search window to build the model over a longer
   period of time, which may give you better results. You may also want to periodically
   re-run this search to rebuild the model with the latest data.
-id: 1da5d5ea-4382-447d-98a9-87c358c95fcb
-name: Baseline Of Cloud Infrastructure API Calls Per User
-search: '| tstats count as api_calls from datamodel=Change where All_Changes.user!=unknown
-  All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")`
-  | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval
-  DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek
-  <= 5, 0, 1) | table _time api_calls, user, HourOfDay, isWeekend | eventstats dc(api_calls)
-  as api_calls by user, HourOfDay, isWeekend | where api_calls >= 1 | fit DensityFunction
-  api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_api_calls_v1 dist=norm
-  show_density=true'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud User Activities
   deployments:
   - Weekly Model Rebuild 90 Day Lookback
@@ -36,5 +41,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_cloud_instances_destroyed.yml b/baselines/baseline_of_cloud_instances_destroyed.yml
index 4567ae5e5a..b122c9c1c1 100644
--- a/baselines/baseline_of_cloud_instances_destroyed.yml
+++ b/baselines/baseline_of_cloud_instances_destroyed.yml
@@ -1,11 +1,25 @@
-author: David Dorsey, Splunk
+name: Baseline Of Cloud Instances Destroyed
+id: a2f701f8-5296-4d74-829c-0b7eb346d549
+version: 1
 date: '2020-08-25'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   for how many instances are destroyed in the environment. By default, the search
   uses the last 90 days of data to build the model and the model is rebuilt weekly.
   The model created by this search is then used in the corresponding detection search,
   which identifies subsequent outliers in the number of instances destroyed in a small
   time window.
+search: '| tstats count as instances_destroyed from datamodel=Change where All_Changes.action=deleted
+  AND All_Changes.status=success AND All_Changes.object_category=instance by _time
+  span=1h | makecontinuous span=1h _time | eval instances_destroyed=coalesce(instances_destroyed,
+  (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4
+  | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek
+  <= 5, 0, 1) | table _time instances_destroyed, HourOfDay, isWeekend | fit DensityFunction
+  instances_destroyed by "HourOfDay,isWeekend" into cloud_excessive_instances_destroyed_v1
+  dist=expon show_density=true'
 how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you will
   need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
   installed, along with any required dependencies. Depending on the number of users
@@ -17,18 +31,9 @@ how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you wi
   re-run this search to rebuild the model with the latest data.\
 
   More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
-id: a2f701f8-5296-4d74-829c-0b7eb346d549
-name: Baseline Of Cloud Instances Destroyed
-search: '| tstats count as instances_destroyed from datamodel=Change where All_Changes.action=deleted
-  AND All_Changes.status=success AND All_Changes.object_category=instance by _time
-  span=1h | makecontinuous span=1h _time | eval instances_destroyed=coalesce(instances_destroyed,
-  (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4
-  | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek
-  <= 5, 0, 1) | table _time instances_destroyed, HourOfDay, isWeekend | fit DensityFunction
-  instances_destroyed by "HourOfDay,isWeekend" into cloud_excessive_instances_destroyed_v1
-  dist=expon show_density=true'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Instance Activities
   - Cloud Cryptomining
   deployments:
@@ -39,5 +44,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_cloud_instances_launched.yml b/baselines/baseline_of_cloud_instances_launched.yml
index acd4759a0d..4534c2fdf0 100644
--- a/baselines/baseline_of_cloud_instances_launched.yml
+++ b/baselines/baseline_of_cloud_instances_launched.yml
@@ -1,11 +1,25 @@
-author: David Dorsey, Splunk
+name: Baseline Of Cloud Instances Launched
+id: b01bd274-f661-4f9c-bd9f-cf23ff6ae0bc
+version: 1
 date: '2020-08-14'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   for how many instances are created in the environment. By default, the search uses
   the last 90 days of data to build the model and the model is rebuilt weekly. The
   model created by this search is then used in the corresponding detection search,
   which identifies subsequent outliers in the number of instances created in a small
   time window.
+search: '| tstats count as instances_launched from datamodel=Change where (All_Changes.action=created)
+  AND All_Changes.status=success AND All_Changes.object_category=instance by _time
+  span=1h | makecontinuous span=1h _time | eval instances_launched=coalesce(instances_launched,
+  (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4
+  | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek
+  <= 5, 0, 1) | table _time instances_launched, HourOfDay, isWeekend | fit DensityFunction
+  instances_launched by "HourOfDay,isWeekend" into cloud_excessive_instances_created_v1
+  dist=expon show_density=true'
 how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you will
   need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
   installed, along with any required dependencies. Depending on the number of users
@@ -17,18 +31,9 @@ how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you wi
   re-run this search to rebuild the model with the latest data.\
 
   More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
-id: b01bd274-f661-4f9c-bd9f-cf23ff6ae0bc
-name: Baseline Of Cloud Instances Launched
-search: '| tstats count as instances_launched from datamodel=Change where (All_Changes.action=created)
-  AND All_Changes.status=success AND All_Changes.object_category=instance by _time
-  span=1h | makecontinuous span=1h _time | eval instances_launched=coalesce(instances_launched,
-  (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4
-  | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek
-  <= 5, 0, 1) | table _time instances_launched, HourOfDay, isWeekend | fit DensityFunction
-  instances_launched by "HourOfDay,isWeekend" into cloud_excessive_instances_created_v1
-  dist=expon show_density=true'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   - Suspicious Cloud Instance Activities
   deployments:
@@ -39,5 +44,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_cloud_security_group_api_calls_per_user.yml b/baselines/baseline_of_cloud_security_group_api_calls_per_user.yml
index b877ab587d..fe20e6b2dc 100644
--- a/baselines/baseline_of_cloud_security_group_api_calls_per_user.yml
+++ b/baselines/baseline_of_cloud_security_group_api_calls_per_user.yml
@@ -1,20 +1,15 @@
-author: David Dorsey, Splunk
+name: Baseline Of Cloud Security Group API Calls Per User
+id: 67b84d51-8329-4909-849f-8d38ce54260a
+version: 1
 date: '2020-09-07'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   for how many API calls for security groups are performed by each user. By default,
   the search uses the last 90 days of data to build the model and the model is rebuilt
   weekly.
-how_to_implement: You must have Enterprise Security 6.0 or later, if not you will
-  need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
-  installed, along with any required dependencies. Depending on the number of users
-  in your environment, you may also need to adjust the value for max_inputs in the
-  MLTK settings for the DensityFunction algorithm, then ensure that the search completes
-  in a reasonable timeframe. By default, the search builds the model using the past
-  90 days of data. You can modify the search window to build the model over a longer
-  period of time, which may give you better results. You may also want to periodically
-  re-run this search to rebuild the model with the latest data.
-id: 67b84d51-8329-4909-849f-8d38ce54260a
-name: Baseline Of Cloud Security Group API Calls Per User
 search: '| tstats count as security_group_api_calls from datamodel=Change where All_Changes.object_category=firewall
   All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")`
   | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval
@@ -24,8 +19,18 @@ search: '| tstats count as security_group_api_calls from datamodel=Change where
   isWeekend | where security_group_api_calls >= 1 | fit DensityFunction security_group_api_calls
   by "user,HourOfDay,isWeekend" into cloud_excessive_security_group_api_calls_v1 dist=norm
   show_density=true'
+how_to_implement: You must have Enterprise Security 6.0 or later, if not you will
+  need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
+  installed, along with any required dependencies. Depending on the number of users
+  in your environment, you may also need to adjust the value for max_inputs in the
+  MLTK settings for the DensityFunction algorithm, then ensure that the search completes
+  in a reasonable timeframe. By default, the search builds the model using the past
+  90 days of data. You can modify the search window to build the model over a longer
+  period of time, which may give you better results. You may also want to periodically
+  re-run this search to rebuild the model with the latest data.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud User Activities
   deployments:
   - Weekly Model Rebuild 90 Day Lookback
@@ -35,5 +40,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_command_line_length___mltk.yml b/baselines/baseline_of_command_line_length___mltk.yml
index 05c75ca235..929bf94c16 100644
--- a/baselines/baseline_of_command_line_length___mltk.yml
+++ b/baselines/baseline_of_command_line_length___mltk.yml
@@ -1,10 +1,20 @@
-author: Rico Valdez, Splunk
+name: Baseline of Command Line Length - MLTK
+id: d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459
+version: 1
 date: '2019-05-08'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   to characterize the length of the command lines observed for each user in the environment.
   By default, the search uses the last 30 days of data to build the model. The model
   created by this search is then used in the corresponding detection search, which
   identifies outliers in the length of the command line.
+search: '| tstats `security_content_summariesonly` count min(_time) as start_time
+  max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest
+  Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | search
+  user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`|
+  eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel'
 how_to_implement: You must be ingesting endpoint data and populating the Endpoint
   data model. In addition, you must have the Machine Learning Toolkit (MLTK) version
   >= 4.2 installed, along with any required dependencies. Depending on the number
@@ -15,15 +25,9 @@ how_to_implement: You must be ingesting endpoint data and populating the Endpoin
   a longer period of time, which may give you better results. You may also want to
   periodically re-run this search to rebuild the model with the latest data. More
   information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
-id: d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459
-name: Baseline of Command Line Length - MLTK
-search: '| tstats `security_content_summariesonly` count min(_time) as start_time
-  max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest
-  Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | search
-  user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`|
-  eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
   - Ransomware
   - Suspicious Command-Line Executions
@@ -36,5 +40,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_dns_query_length___mltk.yml b/baselines/baseline_of_dns_query_length___mltk.yml
index ec67fb56f5..5862243550 100644
--- a/baselines/baseline_of_dns_query_length___mltk.yml
+++ b/baselines/baseline_of_dns_query_length___mltk.yml
@@ -1,10 +1,20 @@
-author: Rico Valdez, Splunk
+name: Baseline of DNS Query Length - MLTK
+id: c914844c-0ff5-4efc-8d44-c063443129ba
+version: 1
 date: '2019-05-08'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   to characterize the length of the DNS queries for each DNS record type observed
   in the environment. By default, the search uses the last 30 days of data to build
   the model. The model created by this search is then used in the corresponding detection
   search, which uses it to identify outliers in the length of the DNS query.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
+  by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")`
+  | eval query_length = len(query) | fit DensityFunction query_length by record_type
+  into dns_query_pdfmodel'
 how_to_implement: To successfully implement this search, you will need to ensure that
   DNS data is populating the Network_Resolution data model. In addition, you must
   have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any
@@ -13,14 +23,9 @@ how_to_implement: To successfully implement this search, you will need to ensure
   period of time, which may give you better results. You may also want to periodically
   re-run this search to rebuild the model with the latest data. More information on
   the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
-id: c914844c-0ff5-4efc-8d44-c063443129ba
-name: Baseline of DNS Query Length - MLTK
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
-  by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")`
-  | eval query_length = len(query) | fit DensityFunction query_length by record_type
-  into dns_query_pdfmodel'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Command and Control
   - Hidden Cobra Malware
   - Suspicious DNS Traffic
@@ -30,5 +35,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml b/baselines/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml
index 150678ac10..89b19ba4ec 100644
--- a/baselines/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml
+++ b/baselines/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml
@@ -1,10 +1,18 @@
-author: Jason Brewer, Splunk
+name: Baseline of Excessive AWS Instances Launched by User - MLTK
+id: fa5634df-fb05-4b4b-aba0-6115138bb1ba
+version: 1
 date: '2019-11-14'
+author: Jason Brewer, Splunk
+type: batch
+datamodel: []
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   for how many RunInstances users do in the environment. By default, the search uses
   the last 90 days of data to build the model. The model created by this search is
   then used in the corresponding detection search, which identifies subsequent outliers
   in the number of RunInstances performed by a user in a small time window.
+search: '`cloudtrail` eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter`
+  | bucket span=10m _time | stats count as instances_launched by _time src_user |
+  fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1'
 how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.\
@@ -19,13 +27,9 @@ how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or lat
   search to rebuild the model with the latest data.\
 
   More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
-id: fa5634df-fb05-4b4b-aba0-6115138bb1ba
-name: Baseline of Excessive AWS Instances Launched by User - MLTK
-search: '`cloudtrail` eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter`
-  | bucket span=10m _time | stats count as instances_launched by _time src_user |
-  fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Suspicious AWS EC2 Activities
   detections:
@@ -34,5 +38,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml b/baselines/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml
index 62c3c11778..2ae61114e3 100644
--- a/baselines/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml
+++ b/baselines/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml
@@ -1,11 +1,19 @@
-author: Jason Brewer, Splunk
+name: Baseline of Excessive AWS Instances Terminated by User - MLTK
+id: b28ed6de-e4ba-40f7-ae0a-93a088c774ab
+version: 1
 date: '2019-11-14'
+author: Jason Brewer, Splunk
+type: batch
+datamodel: []
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   for how many TerminateInstances users do in the environment. By default, the search
   uses the last 90 days of data to build the model. The model created by this search
   is then used in the corresponding detection search, which identifies subsequent
   outliers in the number of TerminateInstances performed by a user in a small time
   window.
+search: '`cloudtrail` eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter`
+  | bucket span=10m _time | stats count as instances_terminated by _time src_user
+  | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1'
 how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.\
@@ -20,13 +28,9 @@ how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or lat
   search to rebuild the model with the latest data.\
 
   More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
-id: b28ed6de-e4ba-40f7-ae0a-93a088c774ab
-name: Baseline of Excessive AWS Instances Terminated by User - MLTK
-search: '`cloudtrail` eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter`
-  | bucket span=10m _time | stats count as instances_terminated by _time src_user
-  | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS EC2 Activities
   detections:
   - Abnormally High AWS Instances Terminated by User - MLTK
@@ -34,5 +38,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_network_acl_activity_by_arn.yml b/baselines/baseline_of_network_acl_activity_by_arn.yml
index ba683ec22a..40fdcb8dc9 100644
--- a/baselines/baseline_of_network_acl_activity_by_arn.yml
+++ b/baselines/baseline_of_network_acl_activity_by_arn.yml
@@ -1,22 +1,26 @@
-author: Bhavin Patel, Splunk
+name: Baseline of Network ACL Activity by ARN
+id: fc0edd96-ff2b-4810-9f1f-63da3783fd63
+version: 1
 date: '2018-05-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search establishes, on a per-hour basis, the average and the standard
   deviation of the number of API calls that were related to network ACLs made by each
   user. Also recorded is the number of data points for each user. This table is then
   outputted to a lookup file to allow the detection search to operate quickly.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`.
-id: fc0edd96-ff2b-4810-9f1f-63da3783fd63
-name: Baseline of Network ACL Activity by ARN
 search: '`cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn
   | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls)
   as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls,
   stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints,
   avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats
   count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   detections:
   - Detect Spike in Network ACL Activity
@@ -24,5 +28,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml b/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml
index 83260019dd..23c168289d 100644
--- a/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml
+++ b/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml
@@ -1,21 +1,25 @@
-author: Bhavin Patel, Splunk
+name: Baseline of S3 Bucket deletion activity by ARN
+id: fc0edd96-ff2b-48b0-9f1f-63eq3783fd63
+version: 1
 date: '2018-07-17'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search establishes, on a per-hour basis, the average and standard
   deviation for the number of API calls related to deleting an S3 bucket by each user.
   Also recorded is the number of data points for each user. This table is then outputted
   to a lookup file to allow the detection search to operate quickly.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs.
-id: fc0edd96-ff2b-48b0-9f1f-63eq3783fd63
-name: Baseline of S3 Bucket deletion activity by ARN
 search: '`cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn
   | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls)
   as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls,
   stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints,
   avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS S3 Activities
   detections:
   - Detect Spike in S3 Bucket deletion
@@ -23,5 +27,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_security_group_activity_by_arn.yml b/baselines/baseline_of_security_group_activity_by_arn.yml
index 8b05c09949..78c5e7356a 100644
--- a/baselines/baseline_of_security_group_activity_by_arn.yml
+++ b/baselines/baseline_of_security_group_activity_by_arn.yml
@@ -1,22 +1,26 @@
-author: Bhavin Patel, Splunk
+name: Baseline of Security Group Activity by ARN
+id: fc0edd96-ff2b-48b0-9f1f-63da3783fd63
+version: 1
 date: '2018-04-17'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search establishes, on a per-hour basis, the average and the standard
   deviation for the number of API calls related to security groups made by each user.
   Also recorded is the number of data points for each user. This table is then outputted
   to a lookup file to allow the detection search to operate quickly.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`.
-id: fc0edd96-ff2b-48b0-9f1f-63da3783fd63
-name: Baseline of Security Group Activity by ARN
 search: '`cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn
   | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls)
   as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls,
   stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints,
   avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats
   count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   detections:
   - Detect Spike in Security Group Activity
@@ -24,5 +28,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/baseline_of_smb_traffic___mltk.yml b/baselines/baseline_of_smb_traffic___mltk.yml
index ad650af721..bf066b6b19 100644
--- a/baselines/baseline_of_smb_traffic___mltk.yml
+++ b/baselines/baseline_of_smb_traffic___mltk.yml
@@ -1,11 +1,22 @@
-author: Rico Valdez, Splunk
+name: Baseline of SMB Traffic - MLTK
+id: df98763b-0b08-4281-8ef9-08db7ac572a9
+version: 1
 date: '2019-05-08'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search is used to build a Machine Learning Toolkit (MLTK) model
   to characterize the number of SMB connections observed each hour for every day of
   week. By default, the search uses the last 30 days of data to build the model. The
   model created by this search is then used in the corresponding detection search
   to identify outliers in the number of SMB connections for that hour and day of the
   week.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic
+  where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb
+  by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval
+  DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction
+  count by "HourOfDay,DayOfWeek" into smb_pdfmodel'
 how_to_implement: You must be ingesting network traffic and populating the Network_Traffic
   data model. In addition, you must have the Machine Learning Toolkit (MLTK) version
   >= 4.2 installed, along with any required dependencies. To improve your results,
@@ -19,15 +30,9 @@ how_to_implement: You must be ingesting network traffic and populating the Netwo
   which may give you better results. You may also want to periodically re-run this
   search to rebuild the model with the latest data. More information on the algorithm
   used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
-id: df98763b-0b08-4281-8ef9-08db7ac572a9
-name: Baseline of SMB Traffic - MLTK
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic
-  where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb
-  by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval
-  DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction
-  count by "HourOfDay,DayOfWeek" into smb_pdfmodel'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   - Disabling Security Tools
   - 'Emotet Malware  DHS Report TA18-201A '
@@ -41,5 +46,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/count_of_assets_by_category.yml b/baselines/count_of_assets_by_category.yml
index 8961582b92..ceabf9c8ca 100644
--- a/baselines/count_of_assets_by_category.yml
+++ b/baselines/count_of_assets_by_category.yml
@@ -1,18 +1,22 @@
-author: Bhavin Patel, Splunk
+name: Count of assets by category
+id: dcfd6b40-42f9-469d-a433-2e53f7489ff9
+version: 1
 date: '2017-09-13'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search shows you every asset category you have and the assets that
   belong to those categories.
+search: '| from datamodel Identity_Management.All_Assets | stats count values(nt_host)
+  by category | sort -count'
 how_to_implement: To successfully implement this search you must first leverage the
   Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv
   file which should then be mapped to the Identity_Management data model. The Identity_Management
   data model will contain a list of known authorized company assets. Ensure that all
   inventoried systems are constantly vetted and updated.
-id: dcfd6b40-42f9-469d-a433-2e53f7489ff9
-name: Count of assets by category
-search: '| from datamodel Identity_Management.All_Assets | stats count values(nt_host)
-  by category | sort -count'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Asset Tracking
   detections:
   - Detect Unauthorized Assets by MAC address
@@ -20,5 +24,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/count_of_unique_ips_connecting_to_ports.yml b/baselines/count_of_unique_ips_connecting_to_ports.yml
index df6840a21d..0a8511f52f 100644
--- a/baselines/count_of_unique_ips_connecting_to_ports.yml
+++ b/baselines/count_of_unique_ips_connecting_to_ports.yml
@@ -1,18 +1,21 @@
-author: David Dorsey, Splunk
+name: Count of Unique IPs Connecting to Ports
+id: 9f3bae5a-9fe3-49df-8c84-5edc51d84b7f
+version: 1
 date: '2017-09-13'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: The search counts the number of times a connection was observed to each
   destination port, and the number of unique source IPs connecting to them.
-how_to_implement: To successfully implement this search, you must be ingesting network
-  traffic, and populating the Network_Traffic data model.
-id: 9f3bae5a-9fe3-49df-8c84-5edc51d84b7f
-name: Count of Unique IPs Connecting to Ports
 search: '| tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts
   from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")`
   | sort - count'
+how_to_implement: To successfully implement this search, you must be ingesting network
+  traffic, and populating the Network_Traffic data model.
+references: []
 tags:
   product:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/create_a_list_of_approved_aws_service_accounts.yml b/baselines/create_a_list_of_approved_aws_service_accounts.yml
index a692e761eb..c7d9285e4a 100644
--- a/baselines/create_a_list_of_approved_aws_service_accounts.yml
+++ b/baselines/create_a_list_of_approved_aws_service_accounts.yml
@@ -1,20 +1,24 @@
-author: Bhavin Patel, Splunk
+name: Create a list of approved AWS service accounts
+id: fc0edc95-ff2b-48b1-5f6f-63ga3789fd43
+version: 2
 date: '2018-12-03'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for successful API activity in CloudTrail within the
   last 30 days, filters out known users from the identity table, and outputs values
   of users into `aws_service_accounts.csv` lookup file.
+search: '`cloudtrail` errorCode=success | rename userName as identity | search NOT
+  [inputlookup identity_lookup_expanded | fields identity] | stats count by identity
+  | table identity | outputlookup aws_service_accounts | stats count'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs. Please validate the service account entires in `aws_service_accounts.csv`,
   which is a lookup file created as a result of running this support search. Please
   remove the entries of service accounts that are not legitimate.
-id: fc0edc95-ff2b-48b1-5f6f-63ga3789fd43
-name: Create a list of approved AWS service accounts
-search: '`cloudtrail` errorCode=success | rename userName as identity | search NOT
-  [inputlookup identity_lookup_expanded | fields identity] | stats count by identity
-  | table identity | outputlookup aws_service_accounts | stats count'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   detections:
   - Detect AWS API Activities From Unapproved Accounts
@@ -22,5 +26,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 2
diff --git a/baselines/deprecated/add_prohibited_processes_to_enterprise_security.yml b/baselines/deprecated/add_prohibited_processes_to_enterprise_security.yml
index e4b743217b..e24ce8914c 100644
--- a/baselines/deprecated/add_prohibited_processes_to_enterprise_security.yml
+++ b/baselines/deprecated/add_prohibited_processes_to_enterprise_security.yml
@@ -1,17 +1,21 @@
-author: David Dorsey, Splunk
+name: Add Prohibited Processes to Enterprise Security
+id: 251930a5-1451-4428-bb13-eed5775be0ce
+version: 1
 date: '2017-09-15'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search takes the existing interesting process table from ES, filters
   out any existing additions added by ESCU and then updates the table with processes
   identified by ESCU that should be prohibited on your endpoints.
-how_to_implement: This search should be run on each new install of ESCU.
-id: 251930a5-1451-4428-bb13-eed5775be0ce
-name: Add Prohibited Processes to Enterprise Security
 search: '| inputlookup prohibited_processes | search note!=ESCU* | inputlookup append=T
   prohibited_processes | fillnull value=* dest dest_pci_domain | fillnull value=false
   is_required is_secure | fillnull value=true is_prohibited | outputlookup prohibited_processes
   | stats count'
+how_to_implement: This search should be run on each new install of ESCU.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Monitor for Unauthorized Software
   - SamSam Ransomware
@@ -21,5 +25,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/discover_dns_records.yml b/baselines/discover_dns_records.yml
index 7cf3eee981..80821f7d93 100644
--- a/baselines/discover_dns_records.yml
+++ b/baselines/discover_dns_records.yml
@@ -1,15 +1,15 @@
-author: Jose Hernandez, Splunk
+name: Discover DNS records
+id: c096f721-8842-42ce-bfc7-74bd8c72b7c3
+version: 1
 date: '2019-02-14'
+author: Jose Hernandez, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: The search takes corporate and common cloud provider domains configured
   under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv`
   finds their responses across the last 30 days from data in the `Network_Resolution
   ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup
-how_to_implement: To successfully implement this search, you must be ingesting DNS
-  logs, and populating the Network_Resolution data model. Also make sure that the
-  cim_corporate_web_domains and cim_corporate_email_domains lookups are populated
-  with the domains owned by your corporation
-id: c096f721-8842-42ce-bfc7-74bd8c72b7c3
-name: Discover DNS records
 search: '| inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv
   | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain,
   "\*", "")) | join domain [|tstats `security_content_summariesonly` count values(DNS.record_type)
@@ -18,8 +18,13 @@ search: '| inputlookup cim_corporate_email_domains.csv | inputlookup append=T ci
   where query!="unknown" | rex field=query "(?<domain>\w+\.\w+?)(?:$|/)"] | makemv
   delim=" " answer |  makemv delim=" " type | sort -count | table count,domain,type,query,answer
   | outputlookup createinapp=true discovered_dns_records'
+how_to_implement: To successfully implement this search, you must be ingesting DNS
+  logs, and populating the Network_Resolution data model. Also make sure that the
+  cim_corporate_web_domains and cim_corporate_email_domains lookups are populated
+  with the domains owned by your corporation
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DNS Hijacking
   detections:
   - DNS record changed
@@ -27,5 +32,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/dnstwist_domain_names.yml b/baselines/dnstwist_domain_names.yml
index 9214374287..9b2212d511 100644
--- a/baselines/dnstwist_domain_names.yml
+++ b/baselines/dnstwist_domain_names.yml
@@ -1,17 +1,21 @@
-author: David Dorsey, Splunk
+name: DNSTwist Domain Names
+id: 19f7d2ec-6028-4d01-bcdb-bda9a034c17f
+version: 2
 date: '2018-10-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search creates permutations of your existing domains, removes the
   valid domain names and stores them in a specified lookup file so they can be checked
   for in the associated detection searches.
+search: '| dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true"
+  | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count'
 how_to_implement: To successfully implement this search you need to update the file
   called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv`
   and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**.
-id: 19f7d2ec-6028-4d01-bcdb-bda9a034c17f
-name: DNSTwist Domain Names
-search: '| dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true"
-  | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Brand Monitoring
   - Suspicious Emails
   detections:
@@ -22,5 +26,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 2
diff --git a/baselines/identify_systems_creating_remote_desktop_traffic.yml b/baselines/identify_systems_creating_remote_desktop_traffic.yml
index 076fb32988..6e90a11fc0 100644
--- a/baselines/identify_systems_creating_remote_desktop_traffic.yml
+++ b/baselines/identify_systems_creating_remote_desktop_traffic.yml
@@ -1,18 +1,21 @@
-author: David Dorsey, Splunk
+name: Identify Systems Creating Remote Desktop Traffic
+id: 5cdda34f-4caf-4128-a713-0837fc48b67a
+version: 1
 date: '2017-09-15'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search counts the numbers of times the system has generated remote
   desktop traffic.
-how_to_implement: To successfully implement this search, you must ingest network traffic
-  and populate the Network_Traffic data model.
-id: 5cdda34f-4caf-4128-a713-0837fc48b67a
-name: Identify Systems Creating Remote Desktop Traffic
 search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic
   where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")`
   | sort - count'
+how_to_implement: To successfully implement this search, you must ingest network traffic
+  and populate the Network_Traffic data model.
+references: []
 tags:
   product:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/identify_systems_receiving_remote_desktop_traffic.yml b/baselines/identify_systems_receiving_remote_desktop_traffic.yml
index 22992acd1b..91daa4eb10 100644
--- a/baselines/identify_systems_receiving_remote_desktop_traffic.yml
+++ b/baselines/identify_systems_receiving_remote_desktop_traffic.yml
@@ -1,19 +1,22 @@
-author: David Dorsey, Splunk
+name: Identify Systems Receiving Remote Desktop Traffic
+id: baaeea15-fe8a-4090-92c2-5b60943bb608
+version: 1
 date: '2017-09-15'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search counts the numbers of times the system has created remote
   desktop traffic
-how_to_implement: To successfully implement this search you must ingest network traffic
-  and populate the Network_Traffic data model. If a system receives a lot of remote
-  desktop traffic, you can apply the category common_rdp_destination to it.
-id: baaeea15-fe8a-4090-92c2-5b60943bb608
-name: Identify Systems Receiving Remote Desktop Traffic
 search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic
   where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")`
   | sort - count'
+how_to_implement: To successfully implement this search you must ingest network traffic
+  and populate the Network_Traffic data model. If a system receives a lot of remote
+  desktop traffic, you can apply the category common_rdp_destination to it.
+references: []
 tags:
   product:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/identify_systems_using_remote_desktop.yml b/baselines/identify_systems_using_remote_desktop.yml
index 15d1e1dfb2..f1ba95bb24 100644
--- a/baselines/identify_systems_using_remote_desktop.yml
+++ b/baselines/identify_systems_using_remote_desktop.yml
@@ -1,18 +1,21 @@
-author: David Dorsey, Splunk
+name: Identify Systems Using Remote Desktop
+id: 063dfe9f-b1d7-4254-a16d-1e2e7eadd6a8
+version: 1
 date: '2019-04-01'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search counts the numbers of times the remote desktop process, mstsc.exe,
   has run on each system.
-how_to_implement: To successfully implement this search you must be ingesting endpoint
-  data that records process activity.
-id: 063dfe9f-b1d7-4254-a16d-1e2e7eadd6a8
-name: Identify Systems Using Remote Desktop
 search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes
   where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name
   | `drop_dm_object_name(Processes)` | sort - count'
+how_to_implement: To successfully implement this search you must be ingesting endpoint
+  data that records process activity.
+references: []
 tags:
   product:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/monitor_successful_backups.yml b/baselines/monitor_successful_backups.yml
index 6dcce241b4..d9a0ebaecc 100644
--- a/baselines/monitor_successful_backups.yml
+++ b/baselines/monitor_successful_backups.yml
@@ -1,17 +1,21 @@
-author: David Dorsey, Splunk
+name: Monitor Successful Backups
+id: b4d0dfb2-2195-4f6e-93a3-48468ed9734e
+version: 1
 date: '2017-09-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search is intended to give you a feel for how often successful backups
   are conducted in your environment. Fluctuations in these numbers will allow you
   to determine when you should investigate.
-how_to_implement: To successfully implement this search you must be ingesting your
-  backup logs.
-id: b4d0dfb2-2195-4f6e-93a3-48468ed9734e
-name: Monitor Successful Backups
 search: '`netbackup` "Disk/Partition backup completed successfully." | bucket _time
   span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time,
   MESSAGE'
+how_to_implement: To successfully implement this search you must be ingesting your
+  backup logs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Monitor Backup Solution
   detections:
   - Unsuccessful Netbackup backups
@@ -19,5 +23,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/monitor_unsuccessful_backups.yml b/baselines/monitor_unsuccessful_backups.yml
index 2cbf322a96..0ea3dcac07 100644
--- a/baselines/monitor_unsuccessful_backups.yml
+++ b/baselines/monitor_unsuccessful_backups.yml
@@ -1,16 +1,20 @@
-author: David Dorsey, Splunk
+name: Monitor Unsuccessful Backups
+id: b2178fed-592f-492b-b851-74161678aa56
+version: 1
 date: '2017-09-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search is intended to give you a feel for how often backup failures
   happen in your environments.  Fluctuations in these numbers will allow you to determine
   when you should investigate.
-how_to_implement: To successfully implement this search you must be ingesting your
-  backup logs.
-id: b2178fed-592f-492b-b851-74161678aa56
-name: Monitor Unsuccessful Backups
 search: '`netbackup` "An error occurred, failed to backup." | bucket _time span=1d
   | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE'
+how_to_implement: To successfully implement this search you must be ingesting your
+  backup logs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Monitor Backup Solution
   detections:
   - Unsuccessful Netbackup backups
@@ -18,5 +22,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_api_call_per_user_roles_in_cloudtrail.yml b/baselines/previously_seen_api_call_per_user_roles_in_cloudtrail.yml
index ab07bbf340..6e2fed12ec 100644
--- a/baselines/previously_seen_api_call_per_user_roles_in_cloudtrail.yml
+++ b/baselines/previously_seen_api_call_per_user_roles_in_cloudtrail.yml
@@ -1,21 +1,25 @@
-author: Bhavin Patel, Splunk
+name: Previously seen API call per user roles in CloudTrail
+id: fc0edc95-fq2c-48b0-9f6f-63da3289fd03
+version: 1
 date: '2018-04-16'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for successful API calls made by different user roles,
   then creates a baseline of the earliest and latest times we have encountered this
   user role. It also returns the name of the API call in our dataset--grouped by user
   role and name of the API call--that occurred within the last 30 days. In this support
   search, we are only looking for events where the user identity is Assumed Role.
+search: '`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole
+  | stats earliest(_time) as earliest latest(_time) as latest by userName eventName
+  | outputlookup previously_seen_api_calls_from_user_roles | stats count'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`,
   which is a lookup file created as a result of running this support search.
-id: fc0edc95-fq2c-48b0-9f6f-63da3289fd03
-name: Previously seen API call per user roles in CloudTrail
-search: '`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole
-  | stats earliest(_time) as earliest latest(_time) as latest by userName eventName
-  | outputlookup previously_seen_api_calls_from_user_roles | stats count'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   detections:
   - Detect new API calls from user roles
@@ -23,5 +27,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_aws_cross_account_activity.yml b/baselines/previously_seen_aws_cross_account_activity.yml
index 566cd5d5c5..7c067846c4 100644
--- a/baselines/previously_seen_aws_cross_account_activity.yml
+++ b/baselines/previously_seen_aws_cross_account_activity.yml
@@ -1,21 +1,25 @@
-author: David Dorsey, Splunk
+name: Previously Seen AWS Cross Account Activity
+id: 1cc22b09-c867-416e-a511-cb36ac44aee2
+version: 1
 date: '2018-06-04'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for **AssumeRole** events where the requesting account
   differs from the requested account, then writes these relationships to a lookup
   file.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`,
-  a lookup file created by this support search.
-id: 1cc22b09-c867-416e-a511-cb36ac44aee2
-name: Previously Seen AWS Cross Account Activity
 search: '`cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId
   | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=*
   | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime
   latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup
   previously_seen_aws_cross_account_activity | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`,
+  a lookup file created by this support search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
   detections:
   - AWS Cross Account Activity From Previously Unseen Account
@@ -23,5 +27,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_aws_cross_account_activity_initial.yml b/baselines/previously_seen_aws_cross_account_activity_initial.yml
index f225d38704..82c4ee7c50 100644
--- a/baselines/previously_seen_aws_cross_account_activity_initial.yml
+++ b/baselines/previously_seen_aws_cross_account_activity_initial.yml
@@ -1,15 +1,14 @@
-author: Rico Valdez, Splunk
+name: Previously Seen AWS Cross Account Activity - Initial
+id: 82af2ed9-8f4b-4785-a152-ba61e6a23bbf
+version: 1
 date: '2020-08-15'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for **AssumeRole** events where the requesting account
   differs from the requested account, then writes these relationships to a lookup
   file.
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version
-  5.1.0 or later)and Enterprise Security 6.2, which contains the required updates
-  to the Authentication data model for cloud use cases. Validate the user name entries
-  in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this
-  support search.
-id: 82af2ed9-8f4b-4785-a152-ba61e6a23bbf
-name: Previously Seen AWS Cross Account Activity - Initial
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user
   Authentication.src Authentication.user_role |  `drop_dm_object_name(Authentication)`
@@ -17,8 +16,14 @@ search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from da
   != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId
   | table requestingAccountId requestedAccountId firstTime lastTime | outputlookup
   previously_seen_aws_cross_account_activity'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later)and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Validate the user name entries
+  in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this
+  support search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Authentication Activities
   deployments:
   - 90 Day Baseline
@@ -28,5 +33,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_aws_cross_account_activity_update.yml b/baselines/previously_seen_aws_cross_account_activity_update.yml
index 8b447a5064..af68491886 100644
--- a/baselines/previously_seen_aws_cross_account_activity_update.yml
+++ b/baselines/previously_seen_aws_cross_account_activity_update.yml
@@ -1,15 +1,14 @@
-author: Rico Valdez, Splunk
+name: Previously Seen AWS Cross Account Activity - Update
+id: dd6fb3a9-4906-48cb-8626-c88a25a056c3
+version: 1
 date: '2020-08-15'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for **AssumeRole** events where the requesting account
   differs from the requested account, then writes these relationships to a lookup
   file.
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version
-  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
-  to the Authentication data model for cloud use cases. Validate the user name entries
-  in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this
-  support search.
-id: dd6fb3a9-4906-48cb-8626-c88a25a056c3
-name: Previously Seen AWS Cross Account Activity - Update
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user
   Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)`
@@ -18,8 +17,14 @@ search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from da
   | inputlookup append=t previously_seen_aws_cross_account_activity | stats min(firstTime)
   as firstTime max(lastTime) as lastTime by requestingAccountId requestedAccountId
   | outputlookup previously_seen_aws_cross_account_activity'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Validate the user name entries
+  in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this
+  support search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Authentication Activities
   deployments:
   - Daily Cache Updates
@@ -29,5 +34,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_aws_provisioning_activity_sources.yml b/baselines/previously_seen_aws_provisioning_activity_sources.yml
index 5534e51bbe..60d6b7aa8a 100644
--- a/baselines/previously_seen_aws_provisioning_activity_sources.yml
+++ b/baselines/previously_seen_aws_provisioning_activity_sources.yml
@@ -1,19 +1,23 @@
-author: David Dorsey, Splunk
+name: Previously Seen AWS Provisioning Activity Sources
+id: ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee
+version: 1
 date: '2018-03-16'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search builds a table of the first and last times seen for every
   IP address (along with its physical location) previously associated with cloud-provisioning
   activity. This is broadly defined as any event that runs or creates something.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs.
-id: ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee
-name: Previously Seen AWS Provisioning Activity Sources
 search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
   | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress,
   City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv
   | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Suspicious Provisioning Activities
   detections:
   - AWS Cloud Provisioning From Previously Unseen IP Address
@@ -24,5 +28,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_aws_regions.yml b/baselines/previously_seen_aws_regions.yml
index 6d79826229..1df505e38b 100644
--- a/baselines/previously_seen_aws_regions.yml
+++ b/baselines/previously_seen_aws_regions.yml
@@ -1,18 +1,22 @@
-author: Bhavin Patel, Splunk
+name: Previously Seen AWS Regions
+id: fc0edc95-ff2b-48b0-9f6f-63da3789fd63
+version: 1
 date: '2018-01-08'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where an AWS instance is started
   and creates a baseline of most recent time (latest) and the first time (earliest)
   we've seen this region in our dataset grouped by the value awsRegion for the last
   30 days
+search: '`cloudtrail` StartInstances | stats earliest(_time) as earliest latest(_time)
+  as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
   inputs.
-id: fc0edc95-ff2b-48b0-9f6f-63da3789fd63
-name: Previously Seen AWS Regions
-search: '`cloudtrail` StartInstances | stats earliest(_time) as earliest latest(_time)
-  as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Suspicious AWS EC2 Activities
   detections:
@@ -21,5 +25,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_api_calls_per_user_role_initial.yml b/baselines/previously_seen_cloud_api_calls_per_user_role_initial.yml
index 5a14742bb1..f853de53bf 100644
--- a/baselines/previously_seen_cloud_api_calls_per_user_role_initial.yml
+++ b/baselines/previously_seen_cloud_api_calls_per_user_role_initial.yml
@@ -1,20 +1,25 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud API Calls Per User Role - Initial
+id: 69d75f4b-b794-4a66-a777-730357b886b4
+version: 1
 date: '2020-09-03'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of the first and last times seen for every
   user role and command combination. This is broadly defined as any event that runs
   or creates something. This table is then cached.
-how_to_implement: You must be ingesting Cloud infrastructure logs from your cloud
-  provider.
-id: 69d75f4b-b794-4a66-a777-730357b886b4
-name: Previously Seen Cloud API Calls Per User Role - Initial
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success
   by All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")`
   | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime
   <= relative_time(now(), "-7d@d"), 1, 0) | table user, command, firstTimeSeen, lastTimeSeen,
   enough_data | outputlookup previously_seen_cloud_api_calls_per_user_role'
+how_to_implement: You must be ingesting Cloud infrastructure logs from your cloud
+  provider.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud User Activities
   deployments:
   - 90 Day Baseline
@@ -24,5 +29,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_api_calls_per_user_role_update.yml b/baselines/previously_seen_cloud_api_calls_per_user_role_update.yml
index 3d2f0f81dd..00fca81f4d 100644
--- a/baselines/previously_seen_cloud_api_calls_per_user_role_update.yml
+++ b/baselines/previously_seen_cloud_api_calls_per_user_role_update.yml
@@ -1,11 +1,13 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud API Calls Per User Role - Update
+id: c4b760a0-6a97-47e9-b089-8ae9e57f210e
+version: 1
 date: '2020-09-03'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search updates the table of the first and last times seen for every
   user role and command combination.
-how_to_implement: You must be ingesting Cloud infrastructure logs from your cloud
-  provider.
-id: c4b760a0-6a97-47e9-b089-8ae9e57f210e
-name: Previously Seen Cloud API Calls Per User Role - Update
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success
   by All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")`
@@ -15,8 +17,11 @@ search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSee
   | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime
   <= relative_time(now(), "-7d@d"), 1, 0) | table user, command, firstTimeSeen, lastTimeSeen,
   enough_data | outputlookup previously_seen_cloud_api_calls_per_user_role'
+how_to_implement: You must be ingesting Cloud infrastructure logs from your cloud
+  provider.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud User Activities
   deployments:
   - Daily Cache Updates
@@ -26,5 +31,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_compute_creations_by_user_initial.yml b/baselines/previously_seen_cloud_compute_creations_by_user_initial.yml
index 1c1b5b820b..e309c2e0df 100644
--- a/baselines/previously_seen_cloud_compute_creations_by_user_initial.yml
+++ b/baselines/previously_seen_cloud_compute_creations_by_user_initial.yml
@@ -1,17 +1,22 @@
-author: Rico Valdez, Splunk
+name: Previously Seen Cloud Compute Creations By User - Initial
+id: dd4ced8a-15a9-4285-94ac-7e4134673bf8
+version: 1
 date: '2020-08-15'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of previously seen users that have launched
   a cloud compute instance.
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-  and have the proper TAs installed.
-id: dd4ced8a-15a9-4285-94ac-7e4134673bf8
-name: Previously Seen Cloud Compute Creations By User - Initial
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance
   by All_Changes.user | `drop_dm_object_name("All_Changes")` | outputlookup previously_seen_cloud_compute_creations_by_user
   | stats count'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+  and have the proper TAs installed.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   deployments:
   - Hourly Cache Updates
@@ -21,5 +26,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_compute_creations_by_user_update.yml b/baselines/previously_seen_cloud_compute_creations_by_user_update.yml
index 1979c97fd0..334686f861 100644
--- a/baselines/previously_seen_cloud_compute_creations_by_user_update.yml
+++ b/baselines/previously_seen_cloud_compute_creations_by_user_update.yml
@@ -1,11 +1,13 @@
-author: Rico Valdez, Splunk
+name: Previously Seen Cloud Compute Creations By User - Update
+id: 6bf75d69-7766-47bc-8097-e41696807a6f
+version: 1
 date: '2020-08-15'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of previously seen users that have launched
   a cloud compute instance.
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-  and have the proper TAs installed.
-id: 6bf75d69-7766-47bc-8097-e41696807a6f
-name: Previously Seen Cloud Compute Creations By User - Update
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance
   by All_Changes.user| `drop_dm_object_name("All_Changes")` | inputlookup append=t
@@ -13,8 +15,11 @@ search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSee
   max(lastTimeSeen) as lastTimeSeen by user | where lastTimeSeen > relative_time(now(),
   "-90d@d") | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data
   = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_creations_by_user'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+  and have the proper TAs installed.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   deployments:
   - Daily Cache Updates
@@ -24,5 +29,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_compute_images_initial.yml b/baselines/previously_seen_cloud_compute_images_initial.yml
index 8802720595..af3ac8e571 100644
--- a/baselines/previously_seen_cloud_compute_images_initial.yml
+++ b/baselines/previously_seen_cloud_compute_images_initial.yml
@@ -1,19 +1,24 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud Compute Images - Initial
+id: 7744597f-d07a-4cea-94a7-e0f8aaebc410
+version: 1
 date: '2020-10-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of previously seen images used to launch cloud
   compute instances
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-  and have the latest Change Datamodel accelerated
-id: 7744597f-d07a-4cea-94a7-e0f8aaebc410
-name: Previously Seen Cloud Compute Images - Initial
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=created  by All_Changes.Instance_Changes.image_id
   | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")`
   | where image_id != "unknown" | eventstats min(firstTimeSeen) as globalFirstTime
   | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0)
   | outputlookup previously_seen_cloud_compute_images'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+  and have the latest Change Datamodel accelerated
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   deployments:
   - 90 Day Baseline
@@ -23,5 +28,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_compute_images_update.yml b/baselines/previously_seen_cloud_compute_images_update.yml
index b21c9e1658..dd1c48d763 100644
--- a/baselines/previously_seen_cloud_compute_images_update.yml
+++ b/baselines/previously_seen_cloud_compute_images_update.yml
@@ -1,10 +1,13 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud Compute Images - Update
+id: 6f1ca5dc-e445-401c-9845-a96d2b6ba184
+version: 1
 date: '2020-08-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of previously seen images used to launch cloud
   compute instances
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-id: 6f1ca5dc-e445-401c-9845-a96d2b6ba184
-name: Previously Seen Cloud Compute Images - Update
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id
   | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")`
@@ -13,8 +16,10 @@ search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSee
   image_id | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`)
   | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime
   <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_images'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   deployments:
   - Daily Cache Updates
@@ -24,5 +29,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_compute_instance_types_initial.yml b/baselines/previously_seen_cloud_compute_instance_types_initial.yml
index a596f29664..93dd8ad01e 100644
--- a/baselines/previously_seen_cloud_compute_instance_types_initial.yml
+++ b/baselines/previously_seen_cloud_compute_instance_types_initial.yml
@@ -1,18 +1,23 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud Compute Instance Types - Initial
+id: 3c78025c-1ffe-4976-a640-75ef604842be
+version: 1
 date: 2020-9-03
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of previously seen cloud compute instance
   types
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-  and have the Security Research cloud data model installed.
-id: 3c78025c-1ffe-4976-a640-75ef604842be
-name: Previously Seen Cloud Compute Instance Types - Initial
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type
   | `drop_dm_object_name("All_Changes.Instance_Changes")` | where instance_type !=
   "unknown" | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data
   = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_instance_types'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+  and have the Security Research cloud data model installed.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   deployments:
   - 90 Day Baseline
@@ -22,5 +27,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_compute_instance_types_update.yml b/baselines/previously_seen_cloud_compute_instance_types_update.yml
index f7290c8d76..d8d5642f1c 100644
--- a/baselines/previously_seen_cloud_compute_instance_types_update.yml
+++ b/baselines/previously_seen_cloud_compute_instance_types_update.yml
@@ -1,10 +1,13 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud Compute Instance Types - Update
+id: 7b7ef9ab-acb9-4e07-af76-4cf1e722885c
+version: 1
 date: 2020-9-03
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of previously seen cloud compute instance
   types
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-id: 7b7ef9ab-acb9-4e07-af76-4cf1e722885c
-name: Previously Seen Cloud Compute Instance Types - Update
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type
   | `drop_dm_object_name("All_Changes.Instance_Changes")` | where instance_type !=
@@ -13,8 +16,10 @@ search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSee
   | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_instance_type_forget_window`)
   | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime
   <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_instance_types'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   deployments:
   - Daily Cache Updates
@@ -24,5 +29,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_instance_modifications_by_user_initial.yml b/baselines/previously_seen_cloud_instance_modifications_by_user_initial.yml
index 3933d3359e..7dd2f703ef 100644
--- a/baselines/previously_seen_cloud_instance_modifications_by_user_initial.yml
+++ b/baselines/previously_seen_cloud_instance_modifications_by_user_initial.yml
@@ -1,18 +1,23 @@
-author: Rico Valdez, Splunk
+name: Previously Seen Cloud Instance Modifications By User - Initial
+id: f36dc403-739d-42f3-83a3-49237d8654c5
+version: 1
 date: '2020-07-29'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of previously seen users that have modified
   a cloud instance.
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-  and have the latest Change Datamodel accelerated.
-id: f36dc403-739d-42f3-83a3-49237d8654c5
-name: Previously Seen Cloud Instance Modifications By User - Initial
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2
   All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")`
   | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime
   <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_instance_modifications_by_user'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+  and have the latest Change Datamodel accelerated.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Instance Activities
   deployments:
   - 90 Day Baseline
@@ -22,5 +27,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_instance_modifications_by_user_update.yml b/baselines/previously_seen_cloud_instance_modifications_by_user_update.yml
index 7d33982a10..c1b8b7fea0 100644
--- a/baselines/previously_seen_cloud_instance_modifications_by_user_update.yml
+++ b/baselines/previously_seen_cloud_instance_modifications_by_user_update.yml
@@ -1,12 +1,13 @@
-author: Rico Valdez, Splunk
+name: Previously Seen Cloud Instance Modifications By User - Update
+id: 534b7d30-7b0c-4510-8f55-65439850d58d
+version: 1
 date: '2020-07-29'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Change
 description: This search updates a table of previously seen Cloud Instance modifications
   that have been made by a user
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
-id: 534b7d30-7b0c-4510-8f55-65439850d58d
-name: Previously Seen Cloud Instance Modifications By User - Update
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2
   All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")`
@@ -15,8 +16,12 @@ search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSee
   where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`)
   | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime
   <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_instance_modifications_by_user'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Instance Activities
   deployments:
   - Daily Cache Updates
@@ -26,5 +31,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_provisioning_activity_sources_initial.yml b/baselines/previously_seen_cloud_provisioning_activity_sources_initial.yml
index 00183b280e..452b894a83 100644
--- a/baselines/previously_seen_cloud_provisioning_activity_sources_initial.yml
+++ b/baselines/previously_seen_cloud_provisioning_activity_sources_initial.yml
@@ -1,13 +1,15 @@
-author: Rico Valdez, Splunk
+name: Previously Seen Cloud Provisioning Activity Sources - Initial
+id: 4ce865fc-f43e-4521-a8ed-ab8af99052d7
+version: 1
 date: '2020-08-19'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Change
 description: This search builds a table of the first and last times seen for every
   IP address (along with its physical location) previously associated with cloud-provisioning
   activity. This is broadly defined as any event that runs or creates something. This
   table is then cached.
-how_to_implement: You must be ingesting Cloud infrastructure logs from your cloud
-  provider.
-id: 4ce865fc-f43e-4521-a8ed-ab8af99052d7
-name: Previously Seen Cloud Provisioning Activity Sources - Initial
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created)
   All_Changes.status=success by All_Changes.src | `drop_dm_object_name("All_Changes")`
@@ -15,8 +17,11 @@ search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSee
   | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0)
   | table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data | outputlookup
   previously_seen_cloud_provisioning_activity_sources'
+how_to_implement: You must be ingesting Cloud infrastructure logs from your cloud
+  provider.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Provisioning Activities
   deployments:
   - 90 Day Baseline
@@ -29,5 +34,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_provisioning_activity_sources_update.yml b/baselines/previously_seen_cloud_provisioning_activity_sources_update.yml
index 5d142216b6..b7d5627ac8 100644
--- a/baselines/previously_seen_cloud_provisioning_activity_sources_update.yml
+++ b/baselines/previously_seen_cloud_provisioning_activity_sources_update.yml
@@ -1,15 +1,17 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud Provisioning Activity Sources - Update
+id: 9830abb9-be80-4563-b232-09bf1f628cf3
+version: 1
 date: '2020-08-20'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This returns the first and last times seen for every IP address (along
   with its physical location) previously associated with cloud-provisioning activity
   within the last day. Cloud provisioning is broadly defined as any event that runs
   or creates something.  It then updates this information with historical data and
   filters out locations that have not been seen within the specified time window.
   This updated table is then cached.
-how_to_implement: You must be ingesting Cloud infrastructure logs from your cloud
-  provider.
-id: 9830abb9-be80-4563-b232-09bf1f628cf3
-name: Previously Seen Cloud Provisioning Activity Sources - Update
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created)
   All_Changes.status=success by All_Changes.src | `drop_dm_object_name("All_Changes")`
@@ -20,8 +22,11 @@ search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSee
   | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime
   <= relative_time(now(), "-7d@d"), 1, 0) | table src, City, Country, Region, firstTimeSeen,
   lastTimeSeen, enough_data | outputlookup previously_seen_cloud_provisioning_activity_sources'
+how_to_implement: You must be ingesting Cloud infrastructure logs from your cloud
+  provider.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Provisioning Activities
   deployments:
   - Daily Cache Updates
@@ -34,5 +39,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_regions_initial.yml b/baselines/previously_seen_cloud_regions_initial.yml
index a9c521cb75..c4d47d8c07 100644
--- a/baselines/previously_seen_cloud_regions_initial.yml
+++ b/baselines/previously_seen_cloud_regions_initial.yml
@@ -1,20 +1,25 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud Regions - Initial
+id: b5e232db-dec6-4db8-aaa1-dd5474521e40
+version: 1
 date: '2020-09-02'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud compute events where a compute instance is
   started and creates a baseline of most recent time, `lastTime` and the first time
   `firstTime` we've seen this region in our dataset grouped by the region for the
   last 30 days
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-  and have the Security Research cloud data model installed.
-id: b5e232db-dec6-4db8-aaa1-dd5474521e40
-name: Previously Seen Cloud Regions - Initial
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region
   | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime
   | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0)
   | outputlookup previously_seen_cloud_regions'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+  and have the Security Research cloud data model installed.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   deployments:
   - 90 Day Baseline
@@ -24,5 +29,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_cloud_regions_update.yml b/baselines/previously_seen_cloud_regions_update.yml
index 6deb7d50b2..d7e15257bd 100644
--- a/baselines/previously_seen_cloud_regions_update.yml
+++ b/baselines/previously_seen_cloud_regions_update.yml
@@ -1,13 +1,15 @@
-author: David Dorsey, Splunk
+name: Previously Seen Cloud Regions - Update
+id: 512f928a-a461-41b4-8984-db4dd2c472e4
+version: 1
 date: '2020-09-02'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud compute events where a compute instance is
   started and creates a baseline of most recent time, `lastTime` and the first time
   `firstTime` we've seen this region in our dataset grouped by the region for the
   last 30 days
-how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
-  and have the Security Research cloud data model installed.
-id: 512f928a-a461-41b4-8984-db4dd2c472e4
-name: Previously Seen Cloud Regions - Update
 search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen
   from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region
   | `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_regions
@@ -16,8 +18,11 @@ search: '| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSee
   | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime
   <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_regions
   | stats count'
+how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
+  and have the Security Research cloud data model installed.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   deployments:
   - Daily Cache Updates
@@ -27,5 +32,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_command_line_arguments.yml b/baselines/previously_seen_command_line_arguments.yml
index 3c808c0439..bc4759a0de 100644
--- a/baselines/previously_seen_command_line_arguments.yml
+++ b/baselines/previously_seen_command_line_arguments.yml
@@ -1,20 +1,25 @@
-author: Bhavin Patel, Splunk
+name: Previously seen command line arguments
+id: 56059acf-50fe-4f60-98d1-b75b51b5c2f3
+version: 2
 date: '2019-03-01'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for command-line arguments where `cmd.exe /c` is used
   to execute a program, then creates a baseline of the earliest and latest times we
   have encountered this command-line argument in our dataset within the last 30 days.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe
+  AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)`'
 how_to_implement: You must be ingesting data that records process activity from your
   hosts to populate the Endpoint data model in the Processes node. You must be ingesting
   logs with both the process name and command line from your endpoints. The complete
   process name with command-line arguments are mapped to the "process" field in the
   Endpoint data model.
-id: 56059acf-50fe-4f60-98d1-b75b51b5c2f3
-name: Previously seen command line arguments
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe
-  AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)`'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   - Disabling Security Tools
   - Hidden Cobra Malware
@@ -31,5 +36,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 2
diff --git a/baselines/previously_seen_ec2_amis.yml b/baselines/previously_seen_ec2_amis.yml
index 1d7e760def..5617881387 100644
--- a/baselines/previously_seen_ec2_amis.yml
+++ b/baselines/previously_seen_ec2_amis.yml
@@ -1,17 +1,21 @@
-author: David Dorsey, Splunk
+name: Previously Seen EC2 AMIs
+id: bb1bd99d-1e93-45f1-9571-cfed42d372b9
+version: 1
 date: '2018-03-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search builds a table of previously seen AMIs used to launch EC2
   instances
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs.
-id: bb1bd99d-1e93-45f1-9571-cfed42d372b9
-name: Previously Seen EC2 AMIs
 search: '`cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId
   as amiID | stats earliest(_time) as firstTime latest(_time) as lastTime by amiID
   | outputlookup previously_seen_ec2_amis.csv | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   detections:
   - EC2 Instance Started With Previously Unseen AMI
@@ -19,5 +23,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_ec2_instance_types.yml b/baselines/previously_seen_ec2_instance_types.yml
index e5b36d2d16..f7d824b7bc 100644
--- a/baselines/previously_seen_ec2_instance_types.yml
+++ b/baselines/previously_seen_ec2_instance_types.yml
@@ -1,17 +1,21 @@
-author: David Dorsey, Splunk
-date: '2018-03-08'
-description: This search builds a table of previously seen EC2 instance types
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs.
-id: b8f029f2-65a6-4d76-be98-dad1c9d59c45
 name: Previously Seen EC2 Instance Types
+id: b8f029f2-65a6-4d76-be98-dad1c9d59c45
+version: 1
+date: '2018-03-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
+description: This search builds a table of previously seen EC2 instance types
 search: '`cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instanceType
   as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time)
   as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv
   | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   detections:
   - EC2 Instance Started With Previously Unseen Instance Type
@@ -19,5 +23,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_ec2_launches_by_user.yml b/baselines/previously_seen_ec2_launches_by_user.yml
index 4622e2a348..ac11dca07c 100644
--- a/baselines/previously_seen_ec2_launches_by_user.yml
+++ b/baselines/previously_seen_ec2_launches_by_user.yml
@@ -1,17 +1,21 @@
-author: David Dorsey, Splunk
+name: Previously Seen EC2 Launches By User
+id: 6c767ac0-0906-4355-9a83-927f5ee7bdad
+version: 1
 date: '2018-03-15'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search builds a table of previously seen ARNs that have launched
   a EC2 instance.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs.
-id: 6c767ac0-0906-4355-9a83-927f5ee7bdad
-name: Previously Seen EC2 Launches By User
 search: '`cloudtrail` eventName=RunInstances errorCode=success | rename userIdentity.arn
   as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup
   previously_seen_ec2_launches_by_user.csv | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Suspicious AWS EC2 Activities
   detections:
@@ -20,5 +24,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_ec2_modifications_by_user.yml b/baselines/previously_seen_ec2_modifications_by_user.yml
index 45320b31c7..ab0ee91c62 100644
--- a/baselines/previously_seen_ec2_modifications_by_user.yml
+++ b/baselines/previously_seen_ec2_modifications_by_user.yml
@@ -1,17 +1,21 @@
-author: David Dorsey, Splunk
+name: Previously Seen EC2 Modifications By User
+id: 4d69091b-d975-4267-85df-888bd41034eb
+version: 1
 date: '2018-04-05'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search builds a table of previously seen ARNs that have launched
   a EC2 instance.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
-  inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
-id: 4d69091b-d975-4267-85df-888bd41034eb
-name: Previously Seen EC2 Modifications By User
 search: '`cloudtrail` `ec2_modification_api_calls` errorCode=success | spath output=arn
   userIdentity.arn | stats earliest(_time) as firstTime latest(_time) as lastTime
   by arn | outputlookup previously_seen_ec2_modifications_by_user | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail
+  inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Unusual AWS EC2 Modifications
   detections:
   - EC2 Instance Modified With Previously Unseen User
@@ -19,5 +23,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_running_windows_services.yml b/baselines/previously_seen_running_windows_services.yml
index db35164f65..bc6496cf54 100644
--- a/baselines/previously_seen_running_windows_services.yml
+++ b/baselines/previously_seen_running_windows_services.yml
@@ -1,17 +1,21 @@
-author: David Dorsey, Splunk
+name: Previously Seen Running Windows Services - Initial
+id: 64ce0ade-cb01-4678-bddd-d31c0b175394
+version: 3
 date: '2020-06-23'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This collects the services that have been started across your entire
   enterprise.
-how_to_implement: While this search does not require you to adhere to Splunk CIM,
-  you must be ingesting your Windows security-event logs for it to execute successfully.
-  Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
-id: 64ce0ade-cb01-4678-bddd-d31c0b175394
-name: Previously Seen Running Windows Services - Initial
 search: '`wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+)
   service entered the (?<state>\w+) state" | where state="running" | stats earliest(_time)
   as firstTimeSeen, latest(_time) as lastTimeSeen by service | outputlookup previously_seen_running_windows_services'
+how_to_implement: While this search does not require you to adhere to Splunk CIM,
+  you must be ingesting your Windows security-event logs for it to execute successfully.
+  Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Orangeworm Attack Group
   - Windows Service Abuse
   - Sunburst Malware
@@ -23,5 +27,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 3
diff --git a/baselines/previously_seen_running_windows_services_update.yml b/baselines/previously_seen_running_windows_services_update.yml
index fa7321544c..9fada7ff36 100644
--- a/baselines/previously_seen_running_windows_services_update.yml
+++ b/baselines/previously_seen_running_windows_services_update.yml
@@ -1,22 +1,26 @@
-author: David Dorsey, Splunk
+name: Previously Seen Running Windows Services - Update
+id: 2e3bdd68-1863-46ee-81f8-87273eee7f1c
+version: 3
 date: '2020-06-23'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search returns the first and last time a Windows service was seen
   across your enterprise within the last hour. It then updates this information with
   historical data and filters out Windows services pairs that have not been seen within
   the specified time window. This updated table is then cached.
-how_to_implement: While this search does not require you to adhere to Splunk CIM,
-  you must be ingesting your Windows security-event logs for it to execute successfully.
-  Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
-id: 2e3bdd68-1863-46ee-81f8-87273eee7f1c
-name: Previously Seen Running Windows Services - Update
 search: '`wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+)
   service entered the (?<state>\w+) state" | where state="running" | stats earliest(_time)
   as firstTimeSeen, latest(_time) as lastTimeSeen by service | inputlookup previously_seen_running_windows_services
   append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen
   by service | where lastTimeSeen > relative_time(now(), "`previously_seen_windows_service_forget_window`")
   | outputlookup previously_seen_running_windows_services'
+how_to_implement: While this search does not require you to adhere to Splunk CIM,
+  you must be ingesting your Windows security-event logs for it to execute successfully.
+  Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Orangeworm Attack Group
   - Windows Service Abuse
   - Sunburst Malware
@@ -28,5 +32,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 3
diff --git a/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml b/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml
index bb9793ba8a..e233c7e139 100644
--- a/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml
+++ b/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml
@@ -1,20 +1,24 @@
-author: Bhavin Patel, Splunk
+name: Previously seen S3 bucket access by remote IP
+id: fc0edc15-fq2c-48b0-9f6f-63qa1281fd03
+version: 1
 date: '2018-06-28'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for successful access to S3 buckets from remote IP
   addresses, then creates a baseline of the earliest and latest times we have encountered
   this remote IP within the last 30 days. In this support search, we are only looking
   for S3 access events where the HTTP response code from AWS is "200"
+search: '`aws_s3_accesslogs` http_status=200  | stats  earliest(_time) as earliest
+  latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip
+  | stats count'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs
   inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`,
   which is a lookup file created as a result of running this support search.
-id: fc0edc15-fq2c-48b0-9f6f-63qa1281fd03
-name: Previously seen S3 bucket access by remote IP
-search: '`aws_s3_accesslogs` http_status=200  | stats  earliest(_time) as earliest
-  latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip
-  | stats count'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS S3 Activities
   detections:
   - Detect S3 access from a new IP
@@ -22,5 +26,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_users_in_cloudtrail.yml b/baselines/previously_seen_users_in_cloudtrail.yml
index f0dc9e2229..64061c014f 100644
--- a/baselines/previously_seen_users_in_cloudtrail.yml
+++ b/baselines/previously_seen_users_in_cloudtrail.yml
@@ -1,22 +1,26 @@
-author: Jason Brewer, Splunk
+name: Previously seen users in CloudTrail
+id: fc0edc95-ff2b-48b0-9f6f-63da3789fd03
+version: 1
 date: '2018-04-30'
+author: Jason Brewer, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where a user logs into the console,
   then creates a baseline of the latest and earliest times, City, Region, and Country
   we have encountered this user in our dataset, grouped by ARN, within the last 30
   days. NOTE - This baseline search is deprecated and has been updated to use the
   Authentication Datamodel
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`,
-  which is a lookup file created as a result of running this support search.
-id: fc0edc95-ff2b-48b0-9f6f-63da3789fd03
-name: Previously seen users in CloudTrail
 search: '`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation
   src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) |
   stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region
   Country | outputlookup previously_seen_users_console_logins_cloudtrail | stats count'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`,
+  which is a lookup file created as a result of running this support search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS Login Activities
   detections:
   - Detect AWS Console Login by User from New Country
@@ -27,5 +31,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_users_in_cloudtrail_initial.yml b/baselines/previously_seen_users_in_cloudtrail_initial.yml
index af42f4a7c2..7eec7f5fc1 100644
--- a/baselines/previously_seen_users_in_cloudtrail_initial.yml
+++ b/baselines/previously_seen_users_in_cloudtrail_initial.yml
@@ -1,23 +1,28 @@
-author: Rico Valdez, Splunk
+name: Previously Seen Users in CloudTrail - Initial
+id: 0a87ecf9-dc6a-43af-861a-205e75a09bf5
+version: 1
 date: '2020-05-28'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for CloudTrail events where a user logs into the console,
   then creates a baseline of the latest and earliest times, City, Region, and Country
   we have encountered this user in our dataset, grouped by username, within the last
   30 days.
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version
-  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
-  to the Authentication data model for cloud use cases. Validate the user name entries
-  in `previously_seen_users_console_logins`, which is a lookup file created by this
-  support search.
-id: 0a87ecf9-dc6a-43af-861a-205e75a09bf5
-name: Previously Seen Users in CloudTrail - Initial
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src
   | iplocation Authentication.src | rename Authentication.user as user Authentication.src
   as src | table user src City Region Country firstTime lastTime | outputlookup previously_seen_users_console_logins
   | stats count'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Validate the user name entries
+  in `previously_seen_users_console_logins`, which is a lookup file created by this
+  support search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Authentication Activities
   deployments:
   - 90 Day Baseline
@@ -30,5 +35,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_users_in_cloudtrail_update.yml b/baselines/previously_seen_users_in_cloudtrail_update.yml
index e1f5cb442b..a8b2d902c0 100644
--- a/baselines/previously_seen_users_in_cloudtrail_update.yml
+++ b/baselines/previously_seen_users_in_cloudtrail_update.yml
@@ -1,23 +1,28 @@
-author: Rico Valdez, Splunk
+name: Previously Seen Users In CloudTrail - Update
+id: 66ff71c2-7e01-47dd-a041-906688c9d322
+version: 1
 date: '2020-05-28'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for CloudTrail events where a user logs into the console,
   then updates the baseline of the latest and earliest times, City, Region, and Country
   we have encountered this user in our dataset, grouped by user, within the last hour.
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version
-  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
-  to the Authentication data model for cloud use cases. Validate the user name entries
-  in `previously_seen_users_console_logins`, which is a lookup file created by this
-  support search.
-id: 66ff71c2-7e01-47dd-a041-906688c9d322
-name: Previously Seen Users In CloudTrail - Update
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src
   | iplocation Authentication.src | rename Authentication.user as user Authentication.src
   as src | table user src City Region Country firstTime lastTime | inputlookup append=t
   previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime)
   as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Validate the user name entries
+  in `previously_seen_users_console_logins`, which is a lookup file created by this
+  support search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Authentication Activities
   deployments:
   - Daily Cache Updates
@@ -30,5 +35,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_zoom_child_processes_initial.yml b/baselines/previously_seen_zoom_child_processes_initial.yml
index fd84e4f457..18f30dcfe6 100644
--- a/baselines/previously_seen_zoom_child_processes_initial.yml
+++ b/baselines/previously_seen_zoom_child_processes_initial.yml
@@ -1,20 +1,25 @@
-author: David Dorsey, Splunk
+name: Previously Seen Zoom Child Processes - Initial
+id: 60b9c00f-a9d6-4e51-803c-5d63ea21b95b
+version: 1
 date: '2020-05-20'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search returns the first and last time a process was seen per endpoint
   with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then
   cached.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints, to populate the Endpoint
-  data model in the Processes node.
-id: 60b9c00f-a9d6-4e51-803c-5d63ea21b95b
-name: Previously Seen Zoom Child Processes - Initial
 search: '| tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time)
   as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe
   OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest|
   `drop_dm_object_name(Processes)` | table dest, process_name, firstTimeSeen, lastTimeSeen
   | outputlookup zoom_first_time_child_process'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints, to populate the Endpoint
+  data model in the Processes node.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Zoom Child Processes
   deployments:
   - 90 Day Baseline
@@ -24,5 +29,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/previously_seen_zoom_child_processes_update.yml b/baselines/previously_seen_zoom_child_processes_update.yml
index 3df94bac68..b24f5b9a95 100644
--- a/baselines/previously_seen_zoom_child_processes_update.yml
+++ b/baselines/previously_seen_zoom_child_processes_update.yml
@@ -1,15 +1,16 @@
-author: David Dorsey, Splunk
+name: Previously Seen Zoom Child Processes - Update
+id: 80aea7fd-5da2-4533-b3c2-560533bfbaee
+version: 1
 date: '2020-05-20'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search returns the first and last time a process was seen per endpoint
   with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour.
   It then updates this information with historical data and filters out proces_name
   and endpoint pairs that have not been seen within the specified time window. This
   updated table is outputed to disk.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints, to populate the Endpoint
-  data model in the Processes node.
-id: 80aea7fd-5da2-4533-b3c2-560533bfbaee
-name: Previously Seen Zoom Child Processes - Update
 search: '| tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time)
   as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe
   OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest|
@@ -18,8 +19,12 @@ search: '| tstats `security_content_summariesonly` min(_time) as firstTimeSeen m
   as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest | where
   lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`")
   | outputlookup zoom_first_time_child_process'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints, to populate the Endpoint
+  data model in the Processes node.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Zoom Child Processes
   deployments:
   - Hourly Cache Updates
@@ -29,5 +34,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml b/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml
index 8bea8031df..d2fb1acb11 100644
--- a/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml
+++ b/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml
@@ -1,22 +1,26 @@
-author: David Dorsey, Splunk
+name: Systems Ready for Spectre-Meltdown Windows Patch
+id: fc0edc95-ff2b-48b0-9f6f-63da3789fd61
+version: 1
 date: '2018-01-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: Some AV applications can cause the Spectre/Meltdown patch for Windows
   not to install successfully. This registry key is supposed to be created by the
   AV engine when it has been patched to be able to handle the Windows patch. If this
   key has been written, the system can then be patched for Spectre and Meltdown.
-how_to_implement: You need to be ingesting logs with both the process name and command-line
-  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4
-  of the Sysmon TA.
-id: fc0edc95-ff2b-48b0-9f6f-63da3789fd61
-name: Systems Ready for Spectre-Meltdown Windows Patch
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry
   AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*")
   by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object,
   All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   | `drop_dm_object_name("All_Changes")`'
+how_to_implement: You need to be ingesting logs with both the process name and command-line
+  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4
+  of the Sysmon TA.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Spectre And Meltdown Vulnerabilities
   detections:
   - Spectre and Meltdown Vulnerable Systems
@@ -24,5 +28,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/update_previously_seen_users_in_cloudtrail.yml b/baselines/update_previously_seen_users_in_cloudtrail.yml
index 7c28c533f3..ff05c11dae 100644
--- a/baselines/update_previously_seen_users_in_cloudtrail.yml
+++ b/baselines/update_previously_seen_users_in_cloudtrail.yml
@@ -1,24 +1,28 @@
-author: Jason Brewer, Splunk
+name: Update previously seen users in CloudTrail
+id: 06c036e6-d6d7-4daa-bd76-411c3d356031
+version: 1
 date: '2018-04-30'
+author: Jason Brewer, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where a user logs into the console,
   then updates the baseline of the latest and earliest times, City, Region, and Country
   we have encountered this user in our dataset, grouped by ARN, within the last hour.
   NOTE - This baseline search is deprecated and has been updated to use the Authentication
   Datamodel
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`,
-  which is a lookup file created as a result of running this support search.
-id: 06c036e6-d6d7-4daa-bd76-411c3d356031
-name: Update previously seen users in CloudTrail
 search: '`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation
   src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) |
   stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region
   Country | inputlookup append=t previously_seen_users_console_logins_cloudtrail |
   stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region
   Country | outputlookup previously_seen_users_console_logins_cloudtrail'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`,
+  which is a lookup file created as a result of running this support search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS Login Activities
   detections:
   - Detect AWS Console Login by User from New Country
@@ -29,5 +33,3 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/windows_updates_install_failures.yml b/baselines/windows_updates_install_failures.yml
index 8e439ff3ad..8eae1f6a7e 100644
--- a/baselines/windows_updates_install_failures.yml
+++ b/baselines/windows_updates_install_failures.yml
@@ -1,18 +1,20 @@
-author: David Dorsey, Splunk
+name: Windows Updates Install Failures
+id: 6a4dbd1b-4502-4a11-943a-82b5ae7a42d7
+version: 1
 date: '2017-09-14'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search is intended to give you a feel for how often Windows updates
   fail to install in your environment. Fluctuations in these numbers will allow you
   to determine when you should be concerned.
-how_to_implement: You must be ingesting your Windows Update Logs
-id: 6a4dbd1b-4502-4a11-943a-82b5ae7a42d7
-name: Windows Updates Install Failures
 search: '| tstats `security_content_summariesonly` dc(Updates.dest) as count FROM
   datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure
   by _time span=1d'
+how_to_implement: You must be ingesting your Windows Update Logs
+references: []
 tags:
   product:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/baselines/windows_updates_install_successes.yml b/baselines/windows_updates_install_successes.yml
index 44f338641b..e9ec9b9ae9 100644
--- a/baselines/windows_updates_install_successes.yml
+++ b/baselines/windows_updates_install_successes.yml
@@ -1,18 +1,20 @@
-author: David Dorsey, Splunk
+name: Windows Updates Install Successes
+id: 6a80535c-86a6-4b54-894c-4b446d0c701d
+version: 1
 date: '2017-09-14'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search is intended to give you a feel for how often successful Windows
   updates are applied in your environments. Fluctuations in these numbers will allow
   you to determine when you should be concerned.
-how_to_implement: You must be ingesting your Windows Update Logs
-id: 6a80535c-86a6-4b54-894c-4b446d0c701d
-name: Windows Updates Install Successes
 search: '| tstats `security_content_summariesonly` dc(Updates.dest) as count FROM
   datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed
   by _time span=1d'
+how_to_implement: You must be ingesting your Windows Update Logs
+references: []
 tags:
   product:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-type: batch
-version: 1
diff --git a/bin/doc-gen.py b/bin/doc-gen.py
index d2a288d6de..df97861c27 100644
--- a/bin/doc-gen.py
+++ b/bin/doc-gen.py
@@ -37,8 +37,8 @@ def prepare_content(stories, detections):
     sto_to_nists = {}
     sto_to_det = {}
     for detection in detections:
-        if 'analytics_story' in detection['tags']:
-            for story in detection['tags']['analytics_story']:
+        if 'analytic_story' in detection['tags']:
+            for story in detection['tags']['analytic_story']:
                 if story in sto_to_det.keys():
                     sto_to_det[story].add(detection['name'])
                 else:
diff --git a/bin/generate.py b/bin/generate.py
index af83d3079a..7c59709d12 100644
--- a/bin/generate.py
+++ b/bin/generate.py
@@ -105,7 +105,7 @@ def generate_savedsearches_conf(detections, response_tasks, baselines, deploymen
         # we are duplicating the code block above for now and just changing variable names to make future
         # changes to this data structure separate from the mappings generation
         # @todo expose the JSON data structure for newer risk type
-        annotation_keys = ['mitre_attack', 'kill_chain_phases', 'cis20', 'nist', 'analytics_story']
+        annotation_keys = ['mitre_attack', 'kill_chain_phases', 'cis20', 'nist', 'analytic_story']
         savedsearch_annotations = {}
         for key in annotation_keys:
             if key == 'mitre_attack':
@@ -154,7 +154,7 @@ def generate_savedsearches_conf(detections, response_tasks, baselines, deploymen
     return output_path
 
 
-def generate_analytics_story_conf(stories, detections, response_tasks, baselines, TEMPLATE_PATH, OUTPUT_PATH):
+def generate_analytic_story_conf(stories, detections, response_tasks, baselines, TEMPLATE_PATH, OUTPUT_PATH):
 
     sto_det = map_detection_to_stories(detections)
 
@@ -334,19 +334,19 @@ def get_deployments(object, deployments):
     matched_deployments = []
 
     for deployment in deployments:
-        if 'analytics_story' in deployment['tags']:
-            if type(deployment['tags']['analytics_story']) is str:
-                if 'analytics_story' in object['tags']:
-                    if deployment['tags']['analytics_story'] == object['tags']['analytics_story'] or deployment['tags']['analytics_story']=='all':
+        if 'analytic_story' in deployment['tags']:
+            if type(deployment['tags']['analytic_story']) is str:
+                if 'analytic_story' in object['tags']:
+                    if deployment['tags']['analytic_story'] == object['tags']['analytic_story'] or deployment['tags']['analytic_story']=='all':
                         matched_deployments.append(deployment)
 
             else:
-                for story in deployment['tags']['analytics_story']:
-                    if story == object['tags']['analytics_story']:
+                for story in deployment['tags']['analytic_story']:
+                    if story == object['tags']['analytic_story']:
                         matched_deployments.append(deployment)
                         continue
 
-        # Remove this check since deployment files are numbered and detections for Splunk Security Analytics for AWS will only get risk configs. 
+        # Remove this check since deployment files are numbered and detections for Splunk Security Analytics for AWS will only get risk configs.
 
         # if 'product' in deployment['tags']:
         #     if type(deployment['tags']['product']) is str:
@@ -431,10 +431,11 @@ def get_nes_fields(search, deployment):
 def map_detection_to_stories(detections):
     sto_det = {}
     for detection in detections:
-        if 'analytics_story' in detection['tags']:
-            for story in detection['tags']['analytics_story']:
+        if 'analytic_story' in detection['tags']:
+            for story in detection['tags']['analytic_story']:
                 if 'type' in detection.keys():
-                    rule_name = str(detection['type'] + ' - ' + detection['name'] + ' - Rule')
+                    if detection['type'] == 'batch':
+                        rule_name = str('ESCU - ' + detection['name'] + ' - Rule')
                 else:
                     rule_name = str('ESCU - ' + detection['name'] + ' - Rule')
                 if not (story in sto_det):
@@ -448,10 +449,11 @@ def map_response_tasks_to_stories(response_tasks):
     sto_res = {}
     for response_task in response_tasks:
         if 'tags' in response_task:
-            if 'analytics_story' in response_task['tags']:
-                for story in response_task['tags']['analytics_story']:
+            if 'analytic_story' in response_task['tags']:
+                for story in response_task['tags']['analytic_story']:
                     if 'type' in response_task.keys():
-                        task_name = str(response_task['type'] + ' - ' + response_task['name'] + ' - Response Task' )
+                        if response_task['type'] == 'response':
+                            task_name = str('ESCU - ' + response_task['name'] + ' - Response Task')
                     else:
                         task_name = str('ESCU - ' + response_task['name'] + ' - Response Task')
                     if not (story in sto_res):
@@ -465,10 +467,11 @@ def map_baselines_to_stories(baselines):
     sto_bas = {}
     for baseline in baselines:
         if 'tags' in baseline:
-            if 'analytics_story' in baseline['tags']:
-                for story in baseline['tags']['analytics_story']:
+            if 'analytic_story' in baseline['tags']:
+                for story in baseline['tags']['analytic_story']:
                     if 'type' in baseline.keys():
-                        baseline_name = str(baseline['type'] + ' - ' + baseline['name'])
+                        if baseline['type'] == 'batch':
+                            baseline_name = str('ESCU - ' + baseline['name'])
                     else:
                         baseline_name = str('ESCU - ' + baseline['name'])
                     if not (story in sto_bas):
@@ -499,10 +502,11 @@ def prepare_stories(stories, detections):
     sto_to_nists = {}
     sto_to_det = {}
     for detection in detections:
-        if 'analytics_story' in detection['tags']:
-            for story in detection['tags']['analytics_story']:
+        if 'analytic_story' in detection['tags']:
+            for story in detection['tags']['analytic_story']:
                 if 'type' in detection.keys():
-                    rule_name = str(detection['type'] + ' - ' + detection['name'] + ' - Rule')
+                    if detection['type'] == 'batch':
+                        rule_name = str('ESCU - ' + detection['name'] + ' - Rule')
                 else:
                     rule_name = str('ESCU - ' + detection['name'] + ' - Rule')
 
@@ -640,14 +644,14 @@ def main(args):
     detections = sorted(detections, key=lambda d: d['name'])
 
     # only use ESCU detections to the configurations
-    detections = [object for object in detections if object["type"].lower() == "escu"]
+    detections = [object for object in detections if object["type"].lower() == "batch"]
 
     response_tasks = sorted(response_tasks, key=lambda i: i['name'])
     baselines = sorted(baselines, key=lambda b: b['name'])
     detection_path = generate_savedsearches_conf(detections, response_tasks, baselines, deployments, TEMPLATE_PATH, OUTPUT_PATH)
 
     stories = sorted(stories, key=lambda s: s['name'])
-    story_path = generate_analytics_story_conf(stories, detections, response_tasks, baselines, TEMPLATE_PATH, OUTPUT_PATH)
+    story_path = generate_analytic_story_conf(stories, detections, response_tasks, baselines, TEMPLATE_PATH, OUTPUT_PATH)
 
     use_case_lib_path = generate_use_case_library_conf(stories, detections, response_tasks, baselines, TEMPLATE_PATH, OUTPUT_PATH)
 
diff --git a/bin/jinja2_templates/savedsearches.j2 b/bin/jinja2_templates/savedsearches.j2
index 39c670f201..b3ae7a23bf 100644
--- a/bin/jinja2_templates/savedsearches.j2
+++ b/bin/jinja2_templates/savedsearches.j2
@@ -8,7 +8,7 @@
 ### ESCU DETECTIONS ###
 
 {% for detection in detections %}
-[{{ detection.type | default('ESCU') }} - {{ detection.name }} - Rule]
+[ESCU - {{ detection.name }} - Rule]
 action.escu = 0
 action.escu.enabled = 1
 description = {{ detection.description }}
@@ -32,7 +32,7 @@ action.escu.known_false_positives = None
 action.escu.creation_date = {{ detection.date }}
 action.escu.modification_date = {{ detection.date }}
 action.escu.confidence = high
-action.escu.full_search_name = {{ detection.type | default('ESCU') }} - {{ detection.name }} - Rule
+action.escu.full_search_name = ESCU - {{ detection.name }} - Rule
 action.escu.search_type = detection
 {% if detection.product is defined %}
 action.escu.product = {{ detection.product | tojson }}
@@ -42,8 +42,8 @@ action.escu.providing_technologies = {{ detection.providing_technologies | tojso
 {% else %}
 action.escu.providing_technologies = []
 {% endif %}
-{% if detection.tags.analytics_story is defined %}
-action.escu.analytic_story = {{ detection.tags.analytics_story | tojson }}
+{% if detection.tags.analytic_story is defined %}
+action.escu.analytic_story = {{ detection.tags.analytic_story | tojson }}
 {% if detection.tags.risk_object is defined %}
 action.risk = 1
 action.risk.param._risk_object = {{ detection.tags.risk_object }}
@@ -58,7 +58,7 @@ cron_schedule = {{ detection.deployment.scheduling.cron_schedule }}
 dispatch.earliest_time = {{ detection.deployment.scheduling.earliest_time }}
 dispatch.latest_time = {{ detection.deployment.scheduling.latest_time }}
 action.correlationsearch.enabled = 1
-action.correlationsearch.label = {{ detection.type | default('ESCU') }} - {{ detection.name }} - Rule
+action.correlationsearch.label = ESCU - {{ detection.name }} - Rule
 action.correlationsearch.annotations = {{ detection.savedsearch_annotations | tojson }}
 {% if detection.deployment.scheduling.schedule_window is defined %}
 schedule_window = {{ detection.deployment.scheduling.schedule_window }}
@@ -106,8 +106,8 @@ action.escu.full_search_name = {{ baseline.type | default('ESCU') }} - {{ baseli
 description = {{ baseline.description }}
 action.escu.creation_date = {{ baseline.date }}
 action.escu.modification_date = {{ baseline.date }}
-{% if baseline.tags.analytics_story is defined %}
-action.escu.analytic_story = {{ baseline.tags.analytics_story | tojson }}
+{% if baseline.tags.analytic_story is defined %}
+action.escu.analytic_story = {{ baseline.tags.analytic_story | tojson }}
 {% else %}
 action.escu.analytic_story = []
 {% endif %}
@@ -154,7 +154,7 @@ description = {{ response_task.description }}
 action.escu.creation_date = {{ response_task.date }}
 action.escu.modification_date = {{ response_task.date }}
 {% if response_task.tags is defined %}
-action.escu.analytic_story = {{ response_task.tags.analytics_story | tojson }}
+action.escu.analytic_story = {{ response_task.tags.analytic_story | tojson }}
 {% else %}
 action.escu.analytic_story = []
 {% endif %}
diff --git a/bin/pretty_yaml.py b/bin/pretty_yaml.py
new file mode 100755
index 0000000000..f0424f3dec
--- /dev/null
+++ b/bin/pretty_yaml.py
@@ -0,0 +1,268 @@
+#!/bin/python
+from os import path, walk
+import sys
+import argparse
+import yaml
+import re
+
+def parse_data_models_from_search(search):
+    match = re.search(r'from\sdatamodel\s?=\s?([^\s.]*)', search)
+    if match is not None:
+        return match.group(1)
+    return False
+
+def pretty_yaml_detections(REPO_PATH, VERBOSE, content_part):
+    manifest_files = []
+    types = ["endpoint", "application", "cloud", "deprecated", "experimental", "network", "web"]
+    for t in types:
+        for root, dirs, files in walk(REPO_PATH + "/" + content_part + '/' + t):
+            for file in files:
+                if file.endswith(".yml"):
+                    manifest_files.append((path.join(root, file)))
+
+    for manifest_file in manifest_files:
+        pretty_yaml = dict()
+        if VERBOSE:
+            print("processing manifest {0}".format(manifest_file))
+
+        with open(manifest_file, 'r') as stream:
+            try:
+                object = list(yaml.safe_load_all(stream))[0]
+            except yaml.YAMLError as exc:
+                print(exc)
+                print("Error reading {0}".format(manifest_file))
+                error = True
+                continue
+
+        pretty_yaml['name'] = object['name']
+        pretty_yaml['id'] = object['id']
+        pretty_yaml['version'] = object['version']
+        pretty_yaml['date'] = object['date']
+        pretty_yaml['author'] = object['author']
+        pretty_yaml['type'] = object['type']
+        pretty_yaml['datamodel'] = object['datamodel']
+        pretty_yaml['description'] = object['description']
+        pretty_yaml['search'] = object['search']
+        if 'how_to_implement' in object:
+            pretty_yaml['how_to_implement'] = object['how_to_implement']
+        else:
+            pretty_yaml['how_to_implement'] = ''
+        pretty_yaml['known_false_positives'] = object['known_false_positives']
+        if 'references' in object:
+            pretty_yaml['references'] = object['references']
+        else:
+            pretty_yaml['references'] = []
+        pretty_yaml['tags'] = {key: value for key, value in sorted(object['tags'].items())}
+
+
+        with open(manifest_file, 'w') as file:
+            documents = yaml.dump(pretty_yaml, file, sort_keys=False)
+
+    return manifest_files
+
+def pretty_yaml_baselines(REPO_PATH, VERBOSE, content_part):
+    manifest_files = []
+    for root, dirs, files in walk(REPO_PATH + "/" + content_part + '/'):
+        for file in files:
+            if file.endswith(".yml"):
+                manifest_files.append((path.join(root, file)))
+
+    for manifest_file in manifest_files:
+        pretty_yaml = dict()
+        if VERBOSE:
+            print("processing manifest {0}".format(manifest_file))
+
+        with open(manifest_file, 'r') as stream:
+            try:
+                object = list(yaml.safe_load_all(stream))[0]
+            except yaml.YAMLError as exc:
+                print(exc)
+                print("Error reading {0}".format(manifest_file))
+                error = True
+                continue
+
+        pretty_yaml['name'] = object['name']
+        pretty_yaml['id'] = object['id']
+        pretty_yaml['version'] = object['version']
+        pretty_yaml['date'] = object['date']
+        pretty_yaml['author'] = object['author']
+        pretty_yaml['type'] = object['type']
+        pretty_yaml['datamodel'] = object['datamodel']
+        pretty_yaml['description'] = object['description']
+        pretty_yaml['search'] = object['search']
+        if 'how_to_implement' in object:
+            pretty_yaml['how_to_implement'] = object['how_to_implement']
+        else:
+            pretty_yaml['how_to_implement'] = ''
+        if 'references' in object:
+            pretty_yaml['references'] = object['references']
+        else:
+            pretty_yaml['references'] = []
+        pretty_yaml['tags'] = {key: value for key, value in sorted(object['tags'].items())}
+
+        with open(manifest_file, 'w') as file:
+            documents = yaml.dump(pretty_yaml, file, sort_keys=False)
+
+    return manifest_files
+
+def pretty_yaml_baselines(REPO_PATH, VERBOSE, content_part):
+    manifest_files = []
+    for root, dirs, files in walk(REPO_PATH + "/" + content_part + '/'):
+        for file in files:
+            if file.endswith(".yml"):
+                manifest_files.append((path.join(root, file)))
+
+    for manifest_file in manifest_files:
+        pretty_yaml = dict()
+        if VERBOSE:
+            print("processing manifest {0}".format(manifest_file))
+
+        with open(manifest_file, 'r') as stream:
+            try:
+                object = list(yaml.safe_load_all(stream))[0]
+            except yaml.YAMLError as exc:
+                print(exc)
+                print("Error reading {0}".format(manifest_file))
+                error = True
+                continue
+
+        pretty_yaml['name'] = object['name']
+        pretty_yaml['id'] = object['id']
+        pretty_yaml['version'] = object['version']
+        pretty_yaml['date'] = object['date']
+        pretty_yaml['author'] = object['author']
+        pretty_yaml['type'] = object['type']
+        pretty_yaml['datamodel'] = object['datamodel']
+        pretty_yaml['description'] = object['description']
+        pretty_yaml['search'] = object['search']
+        if 'how_to_implement' in object:
+            pretty_yaml['how_to_implement'] = object['how_to_implement']
+        else:
+            pretty_yaml['how_to_implement'] = ''
+        if 'references' in object:
+            pretty_yaml['references'] = object['references']
+        else:
+            pretty_yaml['references'] = []
+        pretty_yaml['tags'] = {key: value for key, value in sorted(object['tags'].items())}
+
+        with open(manifest_file, 'w') as file:
+            documents = yaml.dump(pretty_yaml, file, sort_keys=False)
+
+    return manifest_files
+
+def pretty_yaml_deployments(REPO_PATH, VERBOSE, content_part):
+    manifest_files = []
+    for root, dirs, files in walk(REPO_PATH + "/" + content_part + '/'):
+        for file in files:
+            if file.endswith(".yml"):
+                manifest_files.append((path.join(root, file)))
+
+    for manifest_file in manifest_files:
+        pretty_yaml = dict()
+        if VERBOSE:
+            print("processing manifest {0}".format(manifest_file))
+
+        with open(manifest_file, 'r') as stream:
+            try:
+                object = list(yaml.safe_load_all(stream))[0]
+            except yaml.YAMLError as exc:
+                print(exc)
+                print("Error reading {0}".format(manifest_file))
+                error = True
+                continue
+
+        pretty_yaml['name'] = object['name']
+        pretty_yaml['id'] = object['id']
+        pretty_yaml['date'] = object['date']
+        pretty_yaml['author'] = object['author']
+        pretty_yaml['description'] = object['description']
+        pretty_yaml['scheduling'] = object['scheduling']
+        if 'alert_action' in object:
+            pretty_yaml['alert_action'] = object['alert_action']
+        pretty_yaml['tags'] = {key: value for key, value in sorted(object['tags'].items())}
+
+        with open(manifest_file, 'w') as file:
+            documents = yaml.dump(pretty_yaml, file, sort_keys=False)
+
+    return manifest_files
+
+def pretty_yaml_stories(REPO_PATH, VERBOSE, content_part):
+    manifest_files = []
+    for root, dirs, files in walk(REPO_PATH + "/" + content_part + '/'):
+        for file in files:
+            if file.endswith(".yml"):
+                manifest_files.append((path.join(root, file)))
+
+    for manifest_file in manifest_files:
+        pretty_yaml = dict()
+        if VERBOSE:
+            print("processing manifest {0}".format(manifest_file))
+
+        with open(manifest_file, 'r') as stream:
+            try:
+                object = list(yaml.safe_load_all(stream))[0]
+            except yaml.YAMLError as exc:
+                print(exc)
+                print("Error reading {0}".format(manifest_file))
+                error = True
+                continue
+
+        pretty_yaml['name'] = object['name']
+        pretty_yaml['id'] = object['id']
+        pretty_yaml['version'] = object['version']
+        pretty_yaml['date'] = object['date']
+        pretty_yaml['author'] = object['author']
+        pretty_yaml['type'] = object['type']
+        pretty_yaml['description'] = object['description']
+        pretty_yaml['narrative'] = object['narrative']
+        if 'references' in object:
+            pretty_yaml['references'] = object['references']
+        else:
+            pretty_yaml['references'] = []
+        pretty_yaml['tags'] = {key: value for key, value in sorted(object['tags'].items())}
+
+        with open(manifest_file, 'w') as file:
+            documents = yaml.dump(pretty_yaml, file, sort_keys=False)
+
+    return manifest_files
+
+def pretty_yaml(REPO_PATH, VERBOSE, content_part):
+    #for root, dirs, files in walk(REPO_PATH + "/"):
+    manifest_files = []
+    if content_part == 'detections':
+        manifest_files = pretty_yaml_detections(REPO_PATH, VERBOSE, content_part)
+    elif content_part == 'baselines':
+        manifest_files = pretty_yaml_baselines(REPO_PATH, VERBOSE, content_part)
+    elif content_part == 'deployments':
+        manifest_files = pretty_yaml_deployments(REPO_PATH, VERBOSE, content_part)
+    elif content_part == 'stories':
+        manifest_files = pretty_yaml_stories(REPO_PATH, VERBOSE, content_part)
+    return len(manifest_files)
+
+def main(args):
+
+    parser = argparse.ArgumentParser(description="keeps yamls in security_content sorted and pretty printed with custom sort keys, \
+            meant to run quitely for CI, use -v flag to make it bark")
+
+    parser.add_argument("-p", "--path", required=True, help="path to security_content repo")
+    parser.add_argument("-v", "--verbose", required=False, default=False, action='store_true', help="prints verbose output")
+
+    # parse them
+    args = parser.parse_args()
+    REPO_PATH = args.path
+    VERBOSE = args.verbose
+    output = []
+    pretty_yaml_objects = ['macros','lookups','stories','detections','baselines','response_tasks','responses','deployments']
+    for pretty_yaml_object in pretty_yaml_objects:
+        touch_count = pretty_yaml(REPO_PATH, VERBOSE, pretty_yaml_object)
+        if VERBOSE:
+            output.append("made {0} {1} pretty".format(touch_count, pretty_yaml_object))
+
+    for o in output:
+        print(o)
+
+    print("finished successfully!")
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])
diff --git a/bin/reporting/detection_coverage.svg b/bin/reporting/detection_coverage.svg
index c94bb338b0..ae73966523 100644
--- a/bin/reporting/detection_coverage.svg
+++ b/bin/reporting/detection_coverage.svg
@@ -14,6 +14,6 @@
 <g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
     <text x="30" y="15" fill="#010101" fill-opacity=".3">coverage</text>
     <text x="30" y="14">coverage</text>
-    <text x="80" y="14">80%%</text>
+    <text x="80" y="14">82%</text>
 </g>
 </svg>
\ No newline at end of file
diff --git a/bin/testing_coverage.py b/bin/testing_coverage.py
index 5ec6508ca0..655a00d224 100644
--- a/bin/testing_coverage.py
+++ b/bin/testing_coverage.py
@@ -24,7 +24,7 @@ def main(args):
                         type=float,
                         default=0.0,
                         help="Minimum coverage, script return error if target is not met")
-    parser.add_argument('--types', type=str, nargs="*", help="SSA, ESCU", default=["ESCU", "SSA"])
+    parser.add_argument('--types', type=str, nargs="*", help="batch, streaming", default=["batch", "streaming"])
     parsed = parser.parse_args(args)
     populate_coverage(parsed.types)
     print_results(parsed.types)
diff --git a/bin/validate.py b/bin/validate.py
index 7c3696e8d2..2ecc721ef7 100644
--- a/bin/validate.py
+++ b/bin/validate.py
@@ -76,10 +76,8 @@ def validate_objects(REPO_PATH, objects, verbose):
         validation_errors, uuids = validate_standard_fields(object, uuids)
         errors = errors + validation_errors
 
-    if verbose:
-        print("validating object {0}".format(object['name']))
     for object in objects['detections']:
-        if object['type'] == 'ESCU':
+        if object['type'] == 'batch':
             errors = errors + validate_detection_search(object, objects['macros'])
 
     for object in objects['baselines']:
@@ -132,8 +130,12 @@ def validate_standard_fields(object, uuids):
     # the first two fields risk_object, and risk_object_type are an enum of fixed values
     # defined by ESCU risk scoring
 
-
     if 'tags' in object:
+        # check product tag is present in all objects
+        if 'product' not in object['tags']:
+            errors.append("ERROR: a `product` tag is required for object: %s" % object['name'])
+
+        # check risk score values
         for k,v in object['tags'].items():
 
             if k == 'risk_score':
diff --git a/deployments/10_enterprise_security_deployment_configuration.yml b/deployments/10_enterprise_security_deployment_configuration.yml
index 69abc643fa..e61026ba57 100644
--- a/deployments/10_enterprise_security_deployment_configuration.yml
+++ b/deployments/10_enterprise_security_deployment_configuration.yml
@@ -1,10 +1,11 @@
 name: Enterprise Security deployment configuration
 id: bc91a8cd-35e7-4bb2-6140-e756cc46f212
 date: '2020-04-27'
-description: This configuration file applies to all correlation searches that are used for detection
 author: Bhavin Patel
+description: This configuration file applies to all correlation searches that are
+  used for detection
 scheduling:
-  cron_schedule: '0 * * * *'
+  cron_schedule: 0 * * * *
   earliest_time: -70m@m
   latest_time: -10m@m
   schedule_window: auto
@@ -13,8 +14,8 @@ alert_action:
     rule_description: '%description%'
     rule_title: '%name%'
     nes_fields:
-      - user
-      - dest
-      - src
+    - user
+    - dest
+    - src
 tags:
   analytics_story: all
diff --git a/deployments/11_detect_arp_poisoning.yml b/deployments/11_detect_arp_poisoning.yml
index 669772a8e8..a0e0b55c3a 100644
--- a/deployments/11_detect_arp_poisoning.yml
+++ b/deployments/11_detect_arp_poisoning.yml
@@ -1,22 +1,22 @@
 name: Detect ARP Poisoning deployment configuration
 id: e1d5b4dc-4cf3-404f-905c-b478bbb20474
 date: '2020-08-14'
-description: This configuration file applies to the Detect ARP Poisoning detection
 author: Mikael Bjerkeland
+description: This configuration file applies to the Detect ARP Poisoning detection
 scheduling:
-  cron_schedule: '59 * * * *'
+  cron_schedule: 59 * * * *
   earliest_time: -70m@m
   latest_time: -10m@m
   schedule_window: auto
 alert_action:
   notable:
-    rule_description: 'ARP Poisoning has been detected on interface $src_interface$ on host $orig_host$.
-      This may be an indication of a MITM attack.'
-    rule_title: 'ARP Poisoning Detected on $orig_host$'
+    rule_description: ARP Poisoning has been detected on interface $src_interface$
+      on host $orig_host$. This may be an indication of a MITM attack.
+    rule_title: ARP Poisoning Detected on $orig_host$
     nes_fields:
-      - src_interface
-      - firstTime
-      - lastTime
-      - count
+    - src_interface
+    - firstTime
+    - lastTime
+    - count
 tags:
   detection_name: Detect ARP Poisoning
diff --git a/deployments/12_detect_dhcp_poisoning.yml b/deployments/12_detect_dhcp_poisoning.yml
index a01cdc431d..c1d0771fec 100644
--- a/deployments/12_detect_dhcp_poisoning.yml
+++ b/deployments/12_detect_dhcp_poisoning.yml
@@ -1,23 +1,23 @@
 name: Detect Rogue DHCP Server deployment configuration
 id: 6e4e20ac-e719-4ebe-a52d-d672cd451dbb
 date: '2020-08-14'
-description: This configuration file applies to the Detect Rogue DHCP Server detection
 author: Mikael Bjerkeland
+description: This configuration file applies to the Detect Rogue DHCP Server detection
 scheduling:
-  cron_schedule: '59 * * * *'
+  cron_schedule: 59 * * * *
   earliest_time: -70m@m
   latest_time: -10m@m
   schedule_window: auto
 alert_action:
   notable:
-    rule_description: 'DHCP Snooping has detected a Rogue DHCP Server on $orig_host$ from $src_mac$.
-      This may be an indication of a MITM attack.'
-    rule_title: 'Rogue DHCP Server Detected on $orig_host$'
+    rule_description: DHCP Snooping has detected a Rogue DHCP Server on $orig_host$
+      from $src_mac$. This may be an indication of a MITM attack.
+    rule_title: Rogue DHCP Server Detected on $orig_host$
     nes_fields:
-      - src_mac
-      - firstTime
-      - lastTime
-      - count
-      - message_type
+    - src_mac
+    - firstTime
+    - lastTime
+    - count
+    - message_type
 tags:
   detection_name: Detect Rogue DHCP Server
diff --git a/deployments/14_credential_dumping_story.yml b/deployments/14_credential_dumping_story.yml
index 1e11c21ec5..5fa4d76e28 100644
--- a/deployments/14_credential_dumping_story.yml
+++ b/deployments/14_credential_dumping_story.yml
@@ -1,10 +1,10 @@
 name: Credential Dumping Story
 id: 52f52a7c-078f-4413-84da-388b61ccac26
 date: '2021-01-13'
-description: Example for a deployment for a specific Analytics Story
 author: Patrick Bareiss
+description: Example for a deployment for a specific Analytics Story
 scheduling:
-  cron_schedule: '0 * * * *'
+  cron_schedule: 0 * * * *
   earliest_time: -40m@m
   latest_time: -10m@m
   schedule_window: auto
diff --git a/deployments/15_detection_access_LSASS_memory_for_dump_creation.yml b/deployments/15_detection_access_LSASS_memory_for_dump_creation.yml
index 33c04ec26d..166bd03f81 100644
--- a/deployments/15_detection_access_LSASS_memory_for_dump_creation.yml
+++ b/deployments/15_detection_access_LSASS_memory_for_dump_creation.yml
@@ -1,10 +1,10 @@
 name: Access LSASS Memory for Dump Creation Deployment
 id: 974c422f-db3f-4538-8f2a-ee5bf8eec0fa
 date: '2021-01-13'
-description: Example for a deployment for a specific Detection
 author: Patrick Bareiss
+description: Example for a deployment for a specific Detection
 scheduling:
-  cron_schedule: '0 * * * *'
+  cron_schedule: 0 * * * *
   earliest_time: -30m@m
   latest_time: now
   schedule_window: auto
@@ -13,6 +13,6 @@ alert_action:
     rule_description: '%description%'
     rule_title: '%name% Notable'
     nes_fields:
-      - dest
+    - dest
 tags:
   detection_name: Access LSASS Memory for Dump Creation
diff --git a/deployments/16_splunk_security_analytics_for_aws.yml b/deployments/16_splunk_security_analytics_for_aws.yml
index 12f5d6b71f..0956e70bd7 100644
--- a/deployments/16_splunk_security_analytics_for_aws.yml
+++ b/deployments/16_splunk_security_analytics_for_aws.yml
@@ -1,10 +1,12 @@
 name: Enterprise Security config for Splunk Security Analytics for AWS customers
 id: bc91a8cd-35e7-4bb2-6140-e756cc46f211
 date: '2021-01-20'
-description: This configuration file applies to all correlation searches that are used in the Splunk Security Analytics for AWS product. NOTE - Splunk Security Analytics for AWS searches do not need notable  configurations 
 author: Bhavin Patel
+description: This configuration file applies to all correlation searches that are
+  used in the Splunk Security Analytics for AWS product. NOTE - Splunk Security Analytics
+  for AWS searches do not need notable  configurations
 scheduling:
-  cron_schedule: '0 * * * *'
+  cron_schedule: 0 * * * *
   earliest_time: -70m@m
   latest_time: -10m@m
   schedule_window: auto
diff --git a/deployments/20_baseline_cache_hourly_updates.yml b/deployments/20_baseline_cache_hourly_updates.yml
index fdeaf254f2..54c51d4bc8 100644
--- a/deployments/20_baseline_cache_hourly_updates.yml
+++ b/deployments/20_baseline_cache_hourly_updates.yml
@@ -1,13 +1,14 @@
 name: Baseline Cache Hourly Updates
 id: 1030c701-2acf-4b1a-9970-46c7145caf2d
 date: '2020-06-24'
-description: This configuration file applies to all baselines with tag deployments Hourly Cache Updates
 author: Bhavin Patel
+description: This configuration file applies to all baselines with tag deployments
+  Hourly Cache Updates
 scheduling:
-  cron_schedule: '55 * * * *'
+  cron_schedule: 55 * * * *
   earliest_time: -70m@m
   latest_time: -10m@m
   schedule_window: auto
 tags:
   deployments:
-    - Hourly Cache Updates
+  - Hourly Cache Updates
diff --git a/deployments/21_baseline_cache_daily_updates.yml b/deployments/21_baseline_cache_daily_updates.yml
index 755c430ee2..e5ff4a68ee 100644
--- a/deployments/21_baseline_cache_daily_updates.yml
+++ b/deployments/21_baseline_cache_daily_updates.yml
@@ -1,13 +1,14 @@
 name: Baseline Cache Daily Updates
 id: 9541d6f8-fa58-4d48-bb44-6720e39b7b0d
 date: '2020-08-18'
-description: This configuration file applies to all baselines with tag deployments Daily Cache Updates
 author: David Dorsey
+description: This configuration file applies to all baselines with tag deployments
+  Daily Cache Updates
 scheduling:
-  cron_schedule: '10 0 * * *'
+  cron_schedule: 10 0 * * *
   earliest_time: -1450m@m
   latest_time: -10m@m
   schedule_window: auto
 tags:
   deployments:
-    - Daily Cache Updates
+  - Daily Cache Updates
diff --git a/deployments/30_long_running_baseline_searches.yml b/deployments/30_long_running_baseline_searches.yml
index ff2abce7bc..3991fd8909 100644
--- a/deployments/30_long_running_baseline_searches.yml
+++ b/deployments/30_long_running_baseline_searches.yml
@@ -1,8 +1,9 @@
 name: 90 Day Baseline Searches
 id: 6eac9f8b-a35d-4b64-b57f-e5ecde43be6b
 date: '2020-06-24'
-description: This configuration file applies to all baselines with tag deployments Long Running Baseline
 author: Bhavin Patel
+description: This configuration file applies to all baselines with tag deployments
+  Long Running Baseline
 scheduling:
   cron_schedule: 0 1 1 1,4,7,10 *
   earliest_time: -90d@d
@@ -10,4 +11,4 @@ scheduling:
   schedule_window: auto
 tags:
   deployments:
-    - 90 Day Baseline
+  - 90 Day Baseline
diff --git a/deployments/31_weeky_model_rebuild_90_days.yml b/deployments/31_weeky_model_rebuild_90_days.yml
index 9b1f444dfb..cc3d7b76bc 100644
--- a/deployments/31_weeky_model_rebuild_90_days.yml
+++ b/deployments/31_weeky_model_rebuild_90_days.yml
@@ -1,13 +1,14 @@
 name: Weekly Model Rebuild 90 Day Lookback
 id: 4b329568-bcff-49fa-8c85-92e95f0f270d
 date: '2020-09-07'
-description: This configuration file applies to all baselines with tag deployments Weekly Model Rebuild 90 Day Lookback
 author: David Dorsey
+description: This configuration file applies to all baselines with tag deployments
+  Weekly Model Rebuild 90 Day Lookback
 scheduling:
-  cron_schedule: '0 2 * * 0'
+  cron_schedule: 0 2 * * 0
   earliest_time: -90d@d
   latest_time: -1d@d
   schedule_window: auto
 tags:
   deployments:
-    - Weekly Model Rebuild 90 Day Lookback
+  - Weekly Model Rebuild 90 Day Lookback
diff --git a/detections/application/email_files_written_outside_of_the_outlook_directory.yml b/detections/application/email_files_written_outside_of_the_outlook_directory.yml
index 0be790e7c2..680c012412 100644
--- a/detections/application/email_files_written_outside_of_the_outlook_directory.yml
+++ b/detections/application/email_files_written_outside_of_the_outlook_directory.yml
@@ -1,19 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Email files written outside of the Outlook directory
+id: ee18ed37-0802-4268-9435-b3b91aaa18xx
+version: 3
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks at the change-analysis data model and detects email
   files created outside the normal Outlook directory.
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records the file-system activity from your hosts to populate the Endpoint.Filesystem
-  data model node. This is typically populated via endpoint detection-and-response
-  products, such as Carbon Black, or by other endpoint data sources, such as Sysmon.
-  The data used for this search is typically generated via logs that report file-system
-  reads and writes.
-id: ee18ed37-0802-4268-9435-b3b91aaa18xx
-known_false_positives: Administrators and users sometimes prefer backing up their
-  email data by moving the email files into a different folder. These attempts will
-  be detected by the search.
-name: Email files written outside of the Outlook directory
-references: []
 search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path)
   as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem
   where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path
@@ -21,8 +15,18 @@ search: '| tstats `security_content_summariesonly` count values(Filesystem.file_
   by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest
   | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
   `email_files_written_outside_of_the_outlook_directory_filter` '
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records the file-system activity from your hosts to populate the Endpoint.Filesystem
+  data model node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black, or by other endpoint data sources, such as Sysmon.
+  The data used for this search is typically generated via logs that report file-system
+  reads and writes.
+known_false_positives: Administrators and users sometimes prefer backing up their
+  email data by moving the email files into a different folder. These attempts will
+  be detected by the search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Collection and Staging
   asset_type: Endpoint
   cis20:
@@ -36,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/application/multiple_okta_users_with_invalid_credentails_from_the_same_ip.yml b/detections/application/multiple_okta_users_with_invalid_credentails_from_the_same_ip.yml
index e4738abe18..c5d3cf32e1 100644
--- a/detections/application/multiple_okta_users_with_invalid_credentails_from_the_same_ip.yml
+++ b/detections/application/multiple_okta_users_with_invalid_credentails_from_the_same_ip.yml
@@ -1,23 +1,27 @@
-author: Rico Valdez, Splunk
+name: Multiple Okta Users With Invalid Credentails From The Same IP
+id: 19cba45f-cad3-4032-8911-0c09e0444552
+version: 2
 date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search detects Okta login failures due to bad credentials for multiple
   users originating from the same ip address.
-how_to_implement: This search is specific to Okta and requires Okta logs are being
-  ingested in your Splunk deployment.
-id: 19cba45f-cad3-4032-8911-0c09e0444552
-known_false_positives: A single public IP address servicing multiple legitmate users
-  may trigger this search. In addition, the threshold of 5 distinct users may be too
-  low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise
-  the threshold or except specific IP adresses from triggering this search.
-name: Multiple Okta Users With Invalid Credentails From The Same IP
 search: '`okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country
   as country, client.geographicalContext.state as state, client.geographicalContext.city
   as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users
   values(user) as users by src_ip, displayMessage, outcome.reason, country, state,
   city  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   |  search distinct_users > 5| `multiple_okta_users_with_invalid_credentails_from_the_same_ip_filter` '
+how_to_implement: This search is specific to Okta and requires Okta logs are being
+  ingested in your Splunk deployment.
+known_false_positives: A single public IP address servicing multiple legitmate users
+  may trigger this search. In addition, the threshold of 5 distinct users may be too
+  low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise
+  the threshold or except specific IP adresses from triggering this search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Okta Activity
   asset_type: Infrastructure
   cis20:
@@ -31,5 +35,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 2
diff --git a/detections/application/okta_account_lockout_events.yml b/detections/application/okta_account_lockout_events.yml
index 6a4964aee7..0740c2cf38 100644
--- a/detections/application/okta_account_lockout_events.yml
+++ b/detections/application/okta_account_lockout_events.yml
@@ -1,18 +1,22 @@
-author: Rico Valdez, Splunk
-date: '2020-07-21'
-description: Detect Okta user lockout events
-how_to_implement: This search is specific to Okta and requires Okta logs are being
-  ingested in your Splunk deployment.
-id: 62b70968-a0a5-4724-8ac4-67871e6f544d
-known_false_positives: None. Account lockouts should be followed up on to determine
-  if the actual user was the one who caused the lockout, or if it was an unauthorized
-  actor.
 name: Okta Account Lockout Events
+id: 62b70968-a0a5-4724-8ac4-67871e6f544d
+version: 2
+date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
+description: Detect Okta user lockout events
 search: '`okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country
   as country, client.geographicalContext.state as state, client.geographicalContext.city
   as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` '
+how_to_implement: This search is specific to Okta and requires Okta logs are being
+  ingested in your Splunk deployment.
+known_false_positives: None. Account lockouts should be followed up on to determine
+  if the actual user was the one who caused the lockout, or if it was an unauthorized
+  actor.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Okta Activity
   asset_type: Infrastructure
   cis20:
@@ -26,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 2
diff --git a/detections/application/okta_failed_sso_attempts.yml b/detections/application/okta_failed_sso_attempts.yml
index 846376816a..8b5426dcc9 100644
--- a/detections/application/okta_failed_sso_attempts.yml
+++ b/detections/application/okta_failed_sso_attempts.yml
@@ -1,18 +1,22 @@
-author: Rico Valdez, Splunk
-date: '2020-07-21'
-description: Detect failed Okta SSO events
-how_to_implement: This search is specific to Okta and requires Okta logs are being
-  ingested in your Splunk deployment.
-id: 371a6545-2618-4032-ad84-93386b8698c5
-known_false_positives: There may be a faulty config preventing legitmate users from
-  accessing apps they should have access to.
 name: Okta Failed SSO Attempts
+id: 371a6545-2618-4032-ad84-93386b8698c5
+version: 2
+date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
+description: Detect failed Okta SSO events
 search: '`okta` displayMessage="User attempted unauthorized access to app" | stats  min(_time)
   as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage,
   src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `okta_failed_sso_attempts_filter` '
+how_to_implement: This search is specific to Okta and requires Okta logs are being
+  ingested in your Splunk deployment.
+known_false_positives: There may be a faulty config preventing legitmate users from
+  accessing apps they should have access to.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Okta Activity
   asset_type: Infrastructure
   cis20:
@@ -26,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 2
diff --git a/detections/application/okta_user_logins_from_multiple_cities.yml b/detections/application/okta_user_logins_from_multiple_cities.yml
index 566ec807cb..80a5e47e0b 100644
--- a/detections/application/okta_user_logins_from_multiple_cities.yml
+++ b/detections/application/okta_user_logins_from_multiple_cities.yml
@@ -1,24 +1,28 @@
-author: Rico Valdez, Splunk
+name: Okta User Logins From Multiple Cities
+id: 7594fa07-9f34-4d01-81cc-d6af6a5db9e8
+version: 2
 date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search detects logins from the same user from different states in
   a 24 hour period.
+search: '`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null
+  | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city)
+  as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state)
+  as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `okta_user_logins_from_multiple_cities_filter` | search locations > 1'
 how_to_implement: This search is specific to Okta and requires Okta logs are being
   ingested in your Splunk deployment.
-id: 7594fa07-9f34-4d01-81cc-d6af6a5db9e8
 known_false_positives: Users in your enviornment may legitmately be travelling and
   loggin in from different locations. This search is useful for those users that should
   *not* be travelling for some reason, such as the COVID-19 pandemic. The search also
   relies on the geographical information being populated in the Okta logs. It is also
   possible that a connection from another region may be attributed to a login from
   a remote VPN endpoint.
-name: Okta User Logins From Multiple Cities
-search: '`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null
-  | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city)
-  as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state)
-  as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `okta_user_logins_from_multiple_cities_filter` | search locations > 1'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Okta Activity
   asset_type: Infrastructure
   cis20:
@@ -32,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 2
diff --git a/detections/application/web_servers_executing_suspicious_processes.yml b/detections/application/web_servers_executing_suspicious_processes.yml
index 3551751ade..b9cceaa60a 100644
--- a/detections/application/web_servers_executing_suspicious_processes.yml
+++ b/detections/application/web_servers_executing_suspicious_processes.yml
@@ -1,26 +1,30 @@
-author: David Dorsey, Splunk
+name: Web Servers Executing Suspicious Processes
+id: ec3b7601-689a-4463-94e0-c9f45638efb9
+version: 1
 date: '2019-04-01'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for suspicious processes on all systems labeled as
   web servers.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model. In addition, web servers will need to be identified in the Assets and Identity
-  Framework of Enterprise Security.
-id: ec3b7601-689a-4463-94e0-c9f45638efb9
-known_false_positives: Some of these processes may be used legitimately on web servers
-  during maintenance or other administrative tasks.
-name: Web Servers Executing Suspicious Processes
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server"
   AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*"
   OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*")
   by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model. In addition, web servers will need to be identified in the Assets and Identity
+  Framework of Enterprise Security.
+known_false_positives: Some of these processes may be used legitimately on web servers
+  during maintenance or other administrative tasks.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Apache Struts Vulnerability
   asset_type: Web Server
   cis20:
@@ -36,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/abnormally_high_cloud_instances_destroyed.yml b/detections/cloud/abnormally_high_cloud_instances_destroyed.yml
index c98c1f17f3..c8df0ab4a6 100644
--- a/detections/cloud/abnormally_high_cloud_instances_destroyed.yml
+++ b/detections/cloud/abnormally_high_cloud_instances_destroyed.yml
@@ -1,18 +1,14 @@
-author: David Dorsey, Splunk
+name: Abnormally High Number Of Cloud Instances Destroyed
+id: ef629fc9-1583-4590-b62a-f2247fbf7bbf
+version: 1
 date: '2020-08-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search finds for the number successfully destroyed cloud instances
   for every 4 hour block. This is split up between weekdays and the weekend. It then
   applies the probability densitiy model previously created and alerts on any outliers.
-how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
-  run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability
-  density function.
-id: ef629fc9-1583-4590-b62a-f2247fbf7bbf
-known_false_positives: Many service accounts configured within a cloud infrastructure
-  are known to exhibit this behavior. Please adjust the threshold values and filter
-  out service accounts from the output. Always verify if this search alerted on a
-  human user.
-name: Abnormally High Number Of Cloud Instances Destroyed
-references: []
 search: '| tstats count as instances_destroyed values(All_Changes.object_id) as object_id
   from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success
   AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")`
@@ -25,8 +21,16 @@ search: '| tstats count as instances_destroyed values(All_Changes.object_id) as
   eval distance_from_threshold = instances_destroyed - expected_upper_threshold |
   table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold,
   object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
+  run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability
+  density function.
+known_false_positives: Many service accounts configured within a cloud infrastructure
+  are known to exhibit this behavior. Please adjust the threshold values and filter
+  out service accounts from the output. Always verify if this search alerted on a
+  human user.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Instance Activities
   asset_type: Cloud Instance
   cis20:
@@ -47,5 +51,3 @@ tags:
   risk_object_type: user
   risk_score: 10
   security_domain: Cloud
-type: ESCU
-version: 1
diff --git a/detections/cloud/abnormally_high_cloud_instances_launched.yml b/detections/cloud/abnormally_high_cloud_instances_launched.yml
index 31e13050b0..707441011f 100644
--- a/detections/cloud/abnormally_high_cloud_instances_launched.yml
+++ b/detections/cloud/abnormally_high_cloud_instances_launched.yml
@@ -1,18 +1,14 @@
-author: David Dorsey, Splunk
+name: Abnormally High Number Of Cloud Instances Launched
+id: f2361e9f-3928-496c-a556-120cd4223a65
+version: 2
 date: '2020-08-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search finds for the number successfully created cloud instances
   for every 4 hour block. This is split up between weekdays and the weekend. It then
   applies the probability densitiy model previously created and alerts on any outliers.
-how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
-  run the baseline search `Baseline Of Cloud Instances Launched` to create the probability
-  density function.
-id: f2361e9f-3928-496c-a556-120cd4223a65
-known_false_positives: Many service accounts configured within an AWS infrastructure
-  are known to exhibit this behavior. Please adjust the threshold values and filter
-  out service accounts from the output. Always verify if this search alerted on a
-  human user.
-name: Abnormally High Number Of Cloud Instances Launched
-references: []
 search: '| tstats count as instances_launched values(All_Changes.object_id) as object_id
   from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success
   AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")`
@@ -25,8 +21,16 @@ search: '| tstats count as instances_launched values(All_Changes.object_id) as o
   eval distance_from_threshold = instances_launched - expected_upper_threshold | table
   _time, user, instances_launched, expected_upper_threshold, distance_from_threshold,
   object_id | `abnormally_high_number_of_cloud_instances_launched_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
+  run the baseline search `Baseline Of Cloud Instances Launched` to create the probability
+  density function.
+known_false_positives: Many service accounts configured within an AWS infrastructure
+  are known to exhibit this behavior. Please adjust the threshold values and filter
+  out service accounts from the output. Always verify if this search alerted on a
+  human user.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   - Suspicious Cloud Instance Activities
   asset_type: Cloud Instance
@@ -48,5 +52,3 @@ tags:
   risk_object_type: user
   risk_score: 40
   security_domain: Cloud
-type: ESCU
-version: 2
diff --git a/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml b/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml
index dcefe62507..c1ff376df3 100644
--- a/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml
+++ b/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml
@@ -1,14 +1,13 @@
-author: David Dorsey, Splunk
+name: Abnormally High Number Of Cloud Infrastructure API Calls
+id: 0840ddf1-8c89-46ff-b730-c8d6722478c0
+version: 1
 date: '2020-09-07'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search will detect a spike in the number of API calls made to your
   cloud infrastructure environment by a user.
-how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
-  run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to
-  create the probability density function.
-id: 0840ddf1-8c89-46ff-b730-c8d6722478c0
-known_false_positives: ''
-name: Abnormally High Number Of Cloud Infrastructure API Calls
-references: []
 search: '| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change
   where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time
   span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time,
@@ -20,8 +19,13 @@ search: '| tstats count as api_calls values(All_Changes.command) as command from
   -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold
   = api_calls - expected_upper_threshold | table _time, user, command, api_calls,
   expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
+  run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to
+  create the probability density function.
+known_false_positives: ''
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud User Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -46,5 +50,3 @@ tags:
   risk_object_type: user
   risk_score: 25
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml b/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml
index 1a642c9b7e..d0d3e0896f 100644
--- a/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml
+++ b/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml
@@ -1,14 +1,13 @@
-author: David Dorsey, Splunk
+name: Abnormally High Number Of Cloud Security Group API Calls
+id: d4dfb7f3-7a37-498a-b5df-f19334e871af
+version: 1
 date: '2020-09-07'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search will detect a spike in the number of API calls made to your
   cloud infrastructure environment about security groups by a user.
-how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
-  run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to
-  create the probability density function model.
-id: d4dfb7f3-7a37-498a-b5df-f19334e871af
-known_false_positives: ''
-name: Abnormally High Number Of Cloud Security Group API Calls
-references: []
 search: '| tstats count as security_group_api_calls values(All_Changes.command) as
   command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success
   by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval
@@ -21,8 +20,13 @@ search: '| tstats count as security_group_api_calls values(All_Changes.command)
   > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls
   - expected_upper_threshold | table _time, user, command, security_group_api_calls,
   expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
+  run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to
+  create the probability density function model.
+known_false_positives: ''
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud User Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -47,5 +51,3 @@ tags:
   risk_object_type: user
   risk_score: 25
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/aws_cross_account_activity_from_previously_unseen_account.yml b/detections/cloud/aws_cross_account_activity_from_previously_unseen_account.yml
index 9e5dd93b19..031dc0314f 100644
--- a/detections/cloud/aws_cross_account_activity_from_previously_unseen_account.yml
+++ b/detections/cloud/aws_cross_account_activity_from_previously_unseen_account.yml
@@ -1,21 +1,14 @@
-author: Rico Valdez, Splunk
+name: AWS Cross Account Activity From Previously Unseen Account
+id: 21193641-cb96-4a2c-a707-d9b9a7f7792b
+version: 1
 date: '2020-05-28'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for AssumeRole events where an IAM role in a different
   account is requested for the first time.  This search is deprecated and have been
   translated to use the latest Authentication Datamodel.
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
-  provider. You should run the baseline search `Previously Seen AWS Cross Account
-  Activity - Initial` to build the initial table of source IP address, geographic
-  locations, and times. You must also enable the second baseline search `Previously
-  Seen AWS Cross Account Activity - Update` to keep this table up to date and to age
-  out old data. You can also provide additional filtering for this search by customizing
-  the `aws_cross_account_activity_from_previously_unseen_account_filter` macro.
-id: 21193641-cb96-4a2c-a707-d9b9a7f7792b
-known_false_positives: Using multiple AWS accounts and roles is perfectly valid behavior.
-  It's suspicious when an account requests privileges of an account it hasn't before.
-  You should validate with the account owner that this is a legitimate request.
-name: AWS Cross Account Activity From Previously Unseen Account
-references: []
 search: '| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user
   Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)`
@@ -26,8 +19,19 @@ search: '| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=
   Cross Account Activity","Previously Seen") |  where status = "New Cross Account
   Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
   `aws_cross_account_activity_from_previously_unseen_account_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You should run the baseline search `Previously Seen AWS Cross Account
+  Activity - Initial` to build the initial table of source IP address, geographic
+  locations, and times. You must also enable the second baseline search `Previously
+  Seen AWS Cross Account Activity - Update` to keep this table up to date and to age
+  out old data. You can also provide additional filtering for this search by customizing
+  the `aws_cross_account_activity_from_previously_unseen_account_filter` macro.
+known_false_positives: Using multiple AWS accounts and roles is perfectly valid behavior.
+  It's suspicious when an account requests privileges of an account it hasn't before.
+  You should validate with the account owner that this is a legitimate request.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Authentication Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -50,5 +54,3 @@ tags:
   risk_object_type: user
   risk_score: 15
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml b/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml
index ed01f5d9d5..a4908609fa 100644
--- a/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml
+++ b/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml
@@ -1,18 +1,14 @@
-author: Rod Soto, Patrick Bareiss Splunk
+name: AWS Detect Users creating keys with encrypt policy without MFA
+id: c79c164f-4b21-4847-98f9-cf6a9f49179e
+version: 1
 date: '2021-01-11'
+author: Rod Soto, Patrick Bareiss Splunk
+type: batch
+datamodel: []
 description: This search provides detection of KMS keys which action kms:Encrypt is
   accessible for everyone (also outside of your organization). This is an identicator
   that your account is compromised and the attacker uses the encryption key to compromise
   another company.
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudtrail logs
-id: c79c164f-4b21-4847-98f9-cf6a9f49179e
-known_false_positives: unknown
-name: AWS Detect Users creating keys with encrypt policy without MFA
-references:
-- https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
-- https://github.com/d1vious/git-wild-hunt
-- https://www.youtube.com/watch?v=PgzNib37g0M
 search: '`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy
   output=key_policy_statements path=Statement{} | mvexpand key_policy_statements |
   spath input=key_policy_statements output=key_policy_action_1 path=Action | spath
@@ -22,8 +18,15 @@ search: '`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath inpu
   | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource
   eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudtrail logs
+known_false_positives: unknown
+references:
+- https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
+- https://github.com/d1vious/git-wild-hunt
+- https://www.youtube.com/watch?v=PgzNib37g0M
 tags:
-  analytics_story:
+  analytic_story:
   - Ransomware Cloud
   asset_type: AWS Account
   automated_detection_testing: passed
@@ -36,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml b/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml
index 5445a5e027..4d6aa650a2 100644
--- a/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml
+++ b/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml
@@ -1,24 +1,27 @@
-author: Rod Soto, Patrick Bareiss Splunk
+name: AWS Detect Users with KMS keys performing encryption S3
+id: 884a5f59-eec7-4f4a-948b-dbde18225fdc
+version: 1
 date: '2021-01-11'
+author: Rod Soto, Patrick Bareiss Splunk
+type: batch
+datamodel: []
 description: This search provides detection of users with KMS keys performing encryption
   specifically against S3 buckets.
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudtrail logs
-id: 884a5f59-eec7-4f4a-948b-dbde18225fdc
-known_false_positives: bucket with S3 encryption
-name: AWS Detect Users with KMS keys performing encryption S3
-references:
-- https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
-- https://github.com/d1vious/git-wild-hunt
-- https://www.youtube.com/watch?v=PgzNib37g0M
 search: '`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms"
   | rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source
   AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime
   max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file
   values(userAgent) AS userAgent values(region) AS region values(src) AS src by user
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudtrail logs
+known_false_positives: bucket with S3 encryption
+references:
+- https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
+- https://github.com/d1vious/git-wild-hunt
+- https://www.youtube.com/watch?v=PgzNib37g0M
 tags:
-  analytics_story:
+  analytic_story:
   - Ransomware Cloud
   asset_type: S3 Bucket
   automated_detection_testing: passed
@@ -31,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml b/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml
index ac747d803b..d7139b27ac 100644
--- a/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml
+++ b/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml
@@ -1,16 +1,12 @@
-author: Bhavin Patel, Patrick Bareiss, Splunk
+name: AWS Network Access Control List Created with All Open Ports
+id: ada0f478-84a8-4641-a3f1-d82362d6bd75
+version: 2
 date: '2021-01-11'
+author: Bhavin Patel, Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: The search looks for CloudTrail events to detect if any network ACLs
   were created with all the ports open to a specified CIDR.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail
-  inputs.
-id: ada0f478-84a8-4641-a3f1-d82362d6bd75
-known_false_positives: It's possible that an admin has created this ACL with all ports
-  open for some legitimate purpose however, this should be scoped and not allowed
-  in production environment.
-name: AWS Network Access Control List Created with All Open Ports
-references: []
 search: '`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry
   requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1
   | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry
@@ -21,8 +17,15 @@ search: '`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetwor
   requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to
   requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail
+  inputs.
+known_false_positives: It's possible that an admin has created this ACL with all ports
+  open for some legitimate purpose however, this should be scoped and not allowed
+  in production environment.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -45,5 +48,3 @@ tags:
   risk_object_type: user
   risk_score: 10
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/cloud/aws_network_access_control_list_deleted.yml b/detections/cloud/aws_network_access_control_list_deleted.yml
index 9ebcf4c74d..8e46d7d836 100644
--- a/detections/cloud/aws_network_access_control_list_deleted.yml
+++ b/detections/cloud/aws_network_access_control_list_deleted.yml
@@ -1,24 +1,27 @@
-author: Bhavin Patel, Patrick Bareiss, Splunk
+name: AWS Network Access Control List Deleted
+id: ada0f478-84a8-4641-a3f1-d82362d6fd75
+version: 2
 date: '2021-01-12'
+author: Bhavin Patel, Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: Enforcing network-access controls is one of the defensive mechanisms
   used by cloud administrators to restrict access to a cloud instance. After the attacker
   has gained control of the AWS console by compromising an admin account, they can
   delete a network ACL and gain access to the instance from anywhere. This search
   will query the CloudTrail logs to detect users deleting network ACLs.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs.
-id: ada0f478-84a8-4641-a3f1-d82362d6fd75
-known_false_positives: It's possible that a user has legitimately deleted a network
-  ACL.
-name: AWS Network Access Control List Deleted
-references: []
 search: '`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false
   | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName
   userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs.
+known_false_positives: It's possible that a user has legitimately deleted a network
+  ACL.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -41,5 +44,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/cloud/aws_saml_access_by_provider_user_and_principal.yml b/detections/cloud/aws_saml_access_by_provider_user_and_principal.yml
index 9ac60556e2..1a5234bb90 100644
--- a/detections/cloud/aws_saml_access_by_provider_user_and_principal.yml
+++ b/detections/cloud/aws_saml_access_by_provider_user_and_principal.yml
@@ -1,38 +1,43 @@
-author: Rod Soto, Splunk
+name: AWS SAML Access by Provider User and Principal
+id: bbe23980-6019-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides specific SAML access from specific Service Provider,
   user and targeted principal at AWS. This search provides specific information to
   detect abnormal access or potential credential hijack or forgery, specially in federated
   environments using SAML protocol inside the perimeter or cloud provider.
+search: '`cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime
+  max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn
+  requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress
+  userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  |`aws_saml_access_by_provider_user_and_principal_filter`'
 how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
   search works with cloudtrail logs
-id: bbe23980-6019-11eb-ae93-0242ac130002
 known_false_positives: Attacks using a Golden SAML or SAML assertion hijacks or forgeries
   are very difficult to detect as accessing cloud providers with these assertions
   looks exactly like normal access, however things such as source IP sourceIPAddress
   user, and principal targeted at receiving cloud provider along with endpoint credential
   access and abuse detection searches can provide the necessary context to detect
   these attacks.
-name: AWS SAML Access by Provider User and Principal
 references:
 - https://us-cert.cisa.gov/ncas/alerts/aa21-008a
 - https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
 - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
 - https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps
-search: '`cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime
-  max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn
-  requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress
-  userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  |`aws_saml_access_by_provider_user_and_principal_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Federated Credential Abuse
   asset_type: AWS Federated Account
-  mitre_attack_id:
-  - T1078
-  security_domain: threat
   automated_detection_testing: passed
   dataset:
   - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json
-type: ESCU
-version: 1
+  mitre_attack_id:
+  - T1078
+  security_domain: threat
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/cloud/aws_saml_update_identity_provider.yml b/detections/cloud/aws_saml_update_identity_provider.yml
index 8a7321ae2c..fdefa32002 100644
--- a/detections/cloud/aws_saml_update_identity_provider.yml
+++ b/detections/cloud/aws_saml_update_identity_provider.yml
@@ -1,34 +1,39 @@
-author: Rod Soto, Splunk
+name: AWS SAML Update identity provider
+id: 2f0604c6-6030-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of updates to SAML provider in AWS. Updates
   to SAML provider need to be monitored closely as they may indicate possible perimeter
   compromise of federated credentials, or backdoor access from another cloud provider
   set by attacker.
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudtrail logs.
-id: 2f0604c6-6030-11eb-ae93-0242ac130002
-known_false_positives: Updating a SAML provider or creating a new one may not necessarily
-  be malicious however it needs to be closely monitored.
-name: AWS SAML Update identity provider
-references:
-- https://us-cert.cisa.gov/ncas/alerts/aa21-008a
-- https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
-- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
-- https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps
 search: '`cloudtrail` eventName=UpdateSAMLProvider | stats count min(_time) as firstTime
   max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn
   userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId
   userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   |`aws_saml_update_identity_provider_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudtrail logs.
+known_false_positives: Updating a SAML provider or creating a new one may not necessarily
+  be malicious however it needs to be closely monitored.
+references:
+- https://us-cert.cisa.gov/ncas/alerts/aa21-008a
+- https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
+- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
+- https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Federated Credential Abuse
   asset_type: AWS Federated Account
-  mitre_attack_id:
-  - T1078
-  security_domain: threat
   automated_detection_testing: passed
   dataset:
   - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json
-type: ESCU
-version: 1
+  mitre_attack_id:
+  - T1078
+  security_domain: threat
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml b/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml
index a402699cce..6df67d5086 100644
--- a/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml
+++ b/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml
@@ -1,18 +1,12 @@
-author: David Dorsey, Splunk
-date: '2020-09-04'
-description: This search looks for new commands from each user role.
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
-  provider.  You should run the baseline search `Previously Seen Cloud API Calls Per
-  User Role - Initial` to build the initial table of user roles, commands, and times.
-  You must also enable the second baseline search `Previously Seen Cloud API Calls
-  Per User Role - Update` to keep this table up to date and to age out old data. You
-  can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window`
-  macro. You can also provide additional filtering for this search by customizing
-  the `cloud_api_calls_from_previously_unseen_user_roles_filter`
-id: 2181ad1f-1e73-4d0c-9780-e8880482a08f
-known_false_positives: .
 name: Cloud API Calls From Previously Unseen User Roles
-references: []
+id: 2181ad1f-1e73-4d0c-9780-e8880482a08f
+version: 1
+date: '2020-09-04'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
+description: This search looks for new commands from each user role.
 search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
   where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user,
   All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` |
@@ -22,8 +16,18 @@ search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from d
   OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user,
   object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
   `cloud_api_calls_from_previously_unseen_user_roles_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider.  You should run the baseline search `Previously Seen Cloud API Calls Per
+  User Role - Initial` to build the initial table of user roles, commands, and times.
+  You must also enable the second baseline search `Previously Seen Cloud API Calls
+  Per User Role - Update` to keep this table up to date and to age out old data. You
+  can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window`
+  macro. You can also provide additional filtering for this search by customizing
+  the `cloud_api_calls_from_previously_unseen_user_roles_filter`
+known_false_positives: .
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud User Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -44,5 +48,3 @@ tags:
   risk_object_type: user
   risk_score: 25
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml b/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml
index b1d0159e39..3d7badf4fb 100644
--- a/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml
+++ b/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml
@@ -1,16 +1,13 @@
-author: Rico Valdez, Splunk
+name: Cloud Compute Instance Created By Previously Unseen User
+id: 37a0ec8d-827e-4d6d-8025-cedf31f3a149
+version: 1
 date: '2020-08-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud compute instances created by users who have
   not created them before.
-how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs
-  Run the "Previously Seen Cloud Compute Creations By User" support search to create
-  of baseline of previously seen users.
-id: 37a0ec8d-827e-4d6d-8025-cedf31f3a149
-known_false_positives: It's possible that a user will start to create compute instances
-  for the first time, for any number of reasons. Verify with the user launching instances
-  that this is the intended behavior.
-name: Cloud Compute Instance Created By Previously Unseen User
-references: []
 search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime,
   latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change
   where All_Changes.action=created by All_Changes.user All_Changes.vendor_region |
@@ -20,8 +17,15 @@ search: '| tstats `security_content_summariesonly` count earliest(_time) as firs
   | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h")
   | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)`
   | `cloud_compute_instance_created_by_previously_unseen_user_filter`'
+how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs
+  Run the "Previously Seen Cloud Compute Creations By User" support search to create
+  of baseline of previously seen users.
+known_false_positives: It's possible that a user will start to create compute instances
+  for the first time, for any number of reasons. Verify with the user launching instances
+  that this is the intended behavior.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   asset_type: Cloud Compute Instance
   automated_detection_testing: passed
@@ -42,5 +46,3 @@ tags:
   risk_object_type: user
   risk_score: 20
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml b/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml
index a5cf68bfaa..978fde7bf0 100644
--- a/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml
+++ b/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml
@@ -1,20 +1,14 @@
-author: David Dorsey, Splunk
+name: Cloud Compute Instance Created In Previously Unused Region
+id: fa4089e2-50e3-40f7-8469-d2cc1564ca59
+version: 1
 date: '2020-09-02'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks at cloud-infrastructure events where an instance is
   created in any region within the last hour and then compares it to a lookup file
   of previously seen regions where instances have been created.
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
-  provider. You should run the baseline search `Previously Seen Cloud Regions - Initial`
-  to build the initial table of images observed and times. You must also enable the
-  second baseline search `Previously Seen Cloud Regions - Update` to keep this table
-  up to date and to age out old data. You can also provide additional filtering for
-  this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter`
-  macro.
-id: fa4089e2-50e3-40f7-8469-d2cc1564ca59
-known_false_positives: It's possible that a user has unknowingly started an instance
-  in a new region. Please verify that this activity is legitimate.
-name: Cloud Compute Instance Created In Previously Unused Region
-references: []
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id)
   as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region,
   All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions
@@ -23,8 +17,18 @@ search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime values(
   | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(),
   "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)`
   | `cloud_compute_instance_created_in_previously_unused_region_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You should run the baseline search `Previously Seen Cloud Regions - Initial`
+  to build the initial table of images observed and times. You must also enable the
+  second baseline search `Previously Seen Cloud Regions - Update` to keep this table
+  up to date and to age out old data. You can also provide additional filtering for
+  this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter`
+  macro.
+known_false_positives: It's possible that a user has unknowingly started an instance
+  in a new region. Please verify that this activity is legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   asset_type: Cloud Compute Instance
   automated_detection_testing: passed
@@ -48,5 +52,3 @@ tags:
   risk_object_type: user
   risk_score: 20
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml
index 84face6d12..d0929d8ea7 100644
--- a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml
+++ b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml
@@ -1,20 +1,13 @@
-author: David Dorsey, Splunk
+name: Cloud Compute Instance Created With Previously Unseen Image
+id: bc24922d-987c-4645-b288-f8c73ec194c4
+version: 1
 date: '2018-10-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud compute instances being created with previously
   unseen image IDs.
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
-  provider. You should run the baseline search `Previously Seen Cloud Compute Images
-  - Initial` to build the initial table of images observed and times. You must also
-  enable the second baseline search `Previously Seen Cloud Compute Images - Update`
-  to keep this table up to date and to age out old data. You can also provide additional
-  filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter`
-  macro.
-id: bc24922d-987c-4645-b288-f8c73ec194c4
-known_false_positives: After a new image is created, the first systems created with
-  that image will cause this alert to fire.  Verify that the image being used was
-  created by a legitimate user.
-name: Cloud Compute Instance Created With Previously Unseen Image
-references: []
 search: '| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id)
   as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id,
   All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")`
@@ -23,8 +16,19 @@ search: '| tstats count earliest(_time) as firstTime, latest(_time) as lastTime
   | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage)
   OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user,
   image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You should run the baseline search `Previously Seen Cloud Compute Images
+  - Initial` to build the initial table of images observed and times. You must also
+  enable the second baseline search `Previously Seen Cloud Compute Images - Update`
+  to keep this table up to date and to age out old data. You can also provide additional
+  filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter`
+  macro.
+known_false_positives: After a new image is created, the first systems created with
+  that image will cause this alert to fire.  Verify that the image being used was
+  created by a legitimate user.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   asset_type: Cloud Compute Instance
   automated_detection_testing: passed
@@ -43,5 +47,3 @@ tags:
   risk_object_type: user
   risk_score: 20
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml
index fe71342506..4aeac30448 100644
--- a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml
+++ b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml
@@ -1,19 +1,12 @@
-author: David Dorsey, Splunk
-date: '2020-09-12'
-description: Find EC2 instances being created with previously unseen instance types.
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
-  provider. You should run the baseline search `Previously Seen Cloud Compute Instance
-  Types - Initial` to build the initial table of instance types observed and times.
-  You must also enable the second baseline search `Previously Seen Cloud Compute Instance
-  Types - Update` to keep this table up to date and to age out old data. You can also
-  provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`
-  macro.
-id: c6ddbf53-9715-49f3-bb4c-fb2e8a309cda
-known_false_positives: It is possible that an admin will create a new system using
-  a new instance type that has never been used before. Verify with the creator that
-  they intended to create the system with the new instance type.
 name: Cloud Compute Instance Created With Previously Unseen Instance Type
-references: []
+id: c6ddbf53-9715-49f3-bb4c-fb2e8a309cda
+version: 1
+date: '2020-09-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
+description: Find EC2 instances being created with previously unseen instance types.
 search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id)
   as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type,
   All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")`
@@ -23,8 +16,19 @@ search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values
   | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(),
   "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)`
   | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You should run the baseline search `Previously Seen Cloud Compute Instance
+  Types - Initial` to build the initial table of instance types observed and times.
+  You must also enable the second baseline search `Previously Seen Cloud Compute Instance
+  Types - Update` to keep this table up to date and to age out old data. You can also
+  provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`
+  macro.
+known_false_positives: It is possible that an admin will create a new system using
+  a new instance type that has never been used before. Verify with the creator that
+  they intended to create the system with the new instance type.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
   asset_type: Cloud Compute Instance
   automated_detection_testing: passed
@@ -43,5 +47,3 @@ tags:
   risk_object_type: user
   risk_score: 20
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_instance_modified_with_previously_unseen_user.yml b/detections/cloud/cloud_instance_modified_with_previously_unseen_user.yml
index 371d252ff3..aa780adff6 100644
--- a/detections/cloud/cloud_instance_modified_with_previously_unseen_user.yml
+++ b/detections/cloud/cloud_instance_modified_with_previously_unseen_user.yml
@@ -1,17 +1,13 @@
-author: Rico Valdez, Splunk
+name: Cloud Instance Modified By Previously Unseen User
+id: 7fb15084-b14e-405a-bd61-a6de15a40722
+version: 1
 date: '2020-07-29'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud instances being modified by users who have
   not previously modified them.
-how_to_implement: This search has a dependency on other searches to create and update
-  a baseline of users observed to be associated with this activity. The search "Previously
-  Seen Cloud Instance Modifications By User - Update" should be enabled for this detection
-  to properly work.
-id: 7fb15084-b14e-405a-bd61-a6de15a40722
-known_false_positives: It's possible that a new user will start to modify EC2 instances
-  when they haven't before for any number of reasons. Verify with the user that is
-  modifying instances that this is the intended behavior.
-name: Cloud Instance Modified By Previously Unseen User
-references: []
 search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime,
   latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command)
   as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2
@@ -21,8 +17,16 @@ search: '| tstats `security_content_summariesonly` count earliest(_time) as firs
   enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser)
   OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command
   object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter`'
+how_to_implement: This search has a dependency on other searches to create and update
+  a baseline of users observed to be associated with this activity. The search "Previously
+  Seen Cloud Instance Modifications By User - Update" should be enabled for this detection
+  to properly work.
+known_false_positives: It's possible that a new user will start to modify EC2 instances
+  when they haven't before for any number of reasons. Verify with the user that is
+  modifying instances that this is the intended behavior.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Instance Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -43,5 +47,3 @@ tags:
   risk_object_type: user
   risk_score: 10
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_provisioning_from_previously_unseen_city.yml b/detections/cloud/cloud_provisioning_from_previously_unseen_city.yml
index 9b4d1687ec..8c643b706c 100644
--- a/detections/cloud/cloud_provisioning_from_previously_unseen_city.yml
+++ b/detections/cloud/cloud_provisioning_from_previously_unseen_city.yml
@@ -1,8 +1,24 @@
-author: Rico Valdez, Bhavin Patel, Splunk
+name: Cloud Provisioning Activity From Previously Unseen City
+id: e7ecc5e0-88df-48b9-91af-51104c68f02f
+version: 1
 date: '2020-10-09'
+author: Rico Valdez, Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud provisioning activities from previously unseen
   cities. Provisioning activities are defined broadly as any event that runs or creates
   something.
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
+  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
+  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
+  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) |
+  lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen,
+  enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 |
+  eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity
+  > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) |
+  table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter`
+  | `security_content_ctime(firstTime)`'
 how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
   provider.  You should run the baseline search `Previously Seen Cloud Provisioning
   Activity Sources - Initial` to build the initial table of source IP address, geographic
@@ -12,7 +28,6 @@ how_to_implement: You must be ingesting your cloud infrastructure logs from your
   the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide
   additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter`
   macro.
-id: e7ecc5e0-88df-48b9-91af-51104c68f02f
 known_false_positives: "This is a strictly behavioral search, so we define \"false\
   \ positive\" slightly differently. Every time this fires, it will accurately reflect\
   \ the first occurrence in the time period you're searching within, plus what is\
@@ -24,20 +39,9 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal
   \ where the free version of **MaxMind GeoIP** that ships by default with Splunk\
   \ has weak resolution (particularly small countries in less economically powerful\
   \ regions), this may be much less valuable to you."
-name: Cloud Provisioning Activity From Previously Unseen City
 references: []
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
-  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
-  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
-  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) |
-  lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen,
-  enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 |
-  eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity
-  > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) |
-  table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter`
-  | `security_content_ctime(firstTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Provisioning Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -58,5 +62,3 @@ tags:
   risk_object_type: user
   risk_score: 10
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_provisioning_from_previously_unseen_country.yml b/detections/cloud/cloud_provisioning_from_previously_unseen_country.yml
index 6844f847ed..fbb48af47f 100644
--- a/detections/cloud/cloud_provisioning_from_previously_unseen_country.yml
+++ b/detections/cloud/cloud_provisioning_from_previously_unseen_country.yml
@@ -1,8 +1,24 @@
-author: Rico Valdez, Bhavin Patel, Splunk
+name: Cloud Provisioning Activity From Previously Unseen Country
+id: 94994255-3acf-4213-9b3f-0494df03bb31
+version: 1
 date: '2020-10-09'
+author: Rico Valdez, Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud provisioning activities from previously unseen
   countries. Provisioning activities are defined broadly as any event that runs or
   creates something.
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
+  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
+  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
+  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country)
+  | lookup previously_seen_cloud_provisioning_activity_sources Country as Country
+  OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data |
+  where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry)
+  OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src,
+  Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter`
+  | `security_content_ctime(firstTime)`'
 how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
   provider.  You should run the baseline search `Previously Seen Cloud Provisioning
   Activity Sources - Initial` to build the initial table of source IP address, geographic
@@ -12,7 +28,6 @@ how_to_implement: You must be ingesting your cloud infrastructure logs from your
   the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide
   additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter`
   macro.
-id: 94994255-3acf-4213-9b3f-0494df03bb31
 known_false_positives: "This is a strictly behavioral search, so we define \"false\
   \ positive\" slightly differently. Every time this fires, it will accurately reflect\
   \ the first occurrence in the time period you're searching within, plus what is\
@@ -24,20 +39,9 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal
   \ where the free version of **MaxMind GeoIP** that ships by default with Splunk\
   \ has weak resolution (particularly small countries in less economically powerful\
   \ regions), this may be much less valuable to you."
-name: Cloud Provisioning Activity From Previously Unseen Country
 references: []
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
-  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
-  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
-  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country)
-  | lookup previously_seen_cloud_provisioning_activity_sources Country as Country
-  OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data |
-  where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry)
-  OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src,
-  Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter`
-  | `security_content_ctime(firstTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Provisioning Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -58,5 +62,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_provisioning_from_previously_unseen_ip_address.yml b/detections/cloud/cloud_provisioning_from_previously_unseen_ip_address.yml
index e3b3d077c4..e509bcbc4a 100644
--- a/detections/cloud/cloud_provisioning_from_previously_unseen_ip_address.yml
+++ b/detections/cloud/cloud_provisioning_from_previously_unseen_ip_address.yml
@@ -1,8 +1,23 @@
-author: Rico Valdez, Splunk
+name: Cloud Provisioning Activity From Previously Unseen IP Address
+id: f86a8ec9-b042-45eb-92f4-e9ed1d781078
+version: 1
 date: '2020-08-16'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud provisioning activities from previously unseen
   IP addresses. Provisioning activities are defined broadly as any event that runs
   or creates something.
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id)
+  as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created)
+  All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command
+  | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources
+  src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data
+  | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc)
+  OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`)
+  | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter`
+  | `security_content_ctime(firstTime)`'
 how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
   provider.  You should run the baseline search `Previously Seen Cloud Provisioning
   Activity Sources - Initial` to build the initial table of source IP address, geographic
@@ -12,7 +27,6 @@ how_to_implement: You must be ingesting your cloud infrastructure logs from your
   the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide
   additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter`
   macro.
-id: f86a8ec9-b042-45eb-92f4-e9ed1d781078
 known_false_positives: "This is a strictly behavioral search, so we define \"false\
   \ positive\" slightly differently. Every time this fires, it will accurately reflect\
   \ the first occurrence in the time period you're searching within, plus what is\
@@ -24,19 +38,9 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal
   \ where the free version of **MaxMind GeoIP** that ships by default with Splunk\
   \ has weak resolution (particularly small countries in less economically powerful\
   \ regions), this may be much less valuable to you."
-name: Cloud Provisioning Activity From Previously Unseen IP Address
 references: []
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id)
-  as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created)
-  All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command
-  | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources
-  src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data
-  | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc)
-  OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`)
-  | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter`
-  | `security_content_ctime(firstTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Provisioning Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -57,5 +61,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/cloud_provisioning_from_previously_unseen_region.yml b/detections/cloud/cloud_provisioning_from_previously_unseen_region.yml
index dc681ada6d..20f142f9ab 100644
--- a/detections/cloud/cloud_provisioning_from_previously_unseen_region.yml
+++ b/detections/cloud/cloud_provisioning_from_previously_unseen_region.yml
@@ -1,8 +1,24 @@
-author: Rico Valdez, Bhavin Patel, Splunk
+name: Cloud Provisioning Activity From Previously Unseen Region
+id: 5aba1860-9617-4af9-b19d-aecac16fe4f2
+version: 1
 date: '2020-08-16'
+author: Rico Valdez, Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Change
 description: This search looks for cloud provisioning activities from previously unseen
   regions. Provisioning activities are defined broadly as any event that runs or creates
   something.
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
+  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
+  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
+  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region)
+  | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT
+  firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where
+  enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion)
+  OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`)
+  | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter`
+  | `security_content_ctime(firstTime)`'
 how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
   provider.  You should run the baseline search `Previously Seen Cloud Provisioning
   Activity Sources - Initial` to build the initial table of source IP address, geographic
@@ -12,7 +28,6 @@ how_to_implement: You must be ingesting your cloud infrastructure logs from your
   the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide
   additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter`
   macro.
-id: 5aba1860-9617-4af9-b19d-aecac16fe4f2
 known_false_positives: "This is a strictly behavioral search, so we define \"false\
   \ positive\" slightly differently. Every time this fires, it will accurately reflect\
   \ the first occurrence in the time period you're searching within, plus what is\
@@ -24,20 +39,9 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal
   \ where the free version of **MaxMind GeoIP** that ships by default with Splunk\
   \ has weak resolution (particularly small countries in less economically powerful\
   \ regions), this may be much less valuable to you."
-name: Cloud Provisioning Activity From Previously Unseen Region
 references: []
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
-  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
-  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
-  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region)
-  | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT
-  firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where
-  enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion)
-  OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`)
-  | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter`
-  | `security_content_ctime(firstTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Provisioning Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -58,5 +62,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_aws_console_login_by_new_user.yml b/detections/cloud/detect_aws_console_login_by_new_user.yml
index e487f1ed3a..0aafdb8a6d 100644
--- a/detections/cloud/detect_aws_console_login_by_new_user.yml
+++ b/detections/cloud/detect_aws_console_login_by_new_user.yml
@@ -1,22 +1,16 @@
-author: Rico Valdez, Splunk
+name: Detect AWS Console Login by New User
+id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71
+version: 1
 date: '2020-05-28'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for CloudTrail events wherein a console login event
   by a user was recorded within the last hour, then compares the event to a lookup
   file of previously seen users (by ARN values) who have logged into the console.
   The alert is fired if the user has logged into the console for the first time within
   the last hour
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version
-  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
-  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
-  in CloudTrail - Initial` support search only once to create a baseline of previously
-  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
-  - Update` hourly (or more frequently depending on how often you run the detection
-  searches) to refresh the baselines.
-id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71
-known_false_positives: When a legitimate new user logins for the first time, this
-  activity will be detected. Check how old the account is and verify that the user
-  activity is legitimate.
-name: Detect AWS Console Login by New User
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)`
   | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime)
@@ -24,8 +18,19 @@ search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from da
   "First Time Logging into AWS Console", "Previously Seen User") |where userStatus="First
   Time Logging into AWS Console" |  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
   `detect_aws_console_login_by_new_user_filter`'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
+  in CloudTrail - Initial` support search only once to create a baseline of previously
+  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
+  - Update` hourly (or more frequently depending on how often you run the detection
+  searches) to refresh the baselines.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Cloud Authentication Activities
   asset_type: AWS Instance
   automated_detection_testing: passed
@@ -47,5 +52,3 @@ tags:
   risk_object_type: user
   risk_score: 30
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml
index e5b7643f50..f1da78bdcd 100644
--- a/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml
+++ b/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml
@@ -1,24 +1,16 @@
-author: Bhavin Patel, Splunk
+name: Detect AWS Console Login by User from New City
+id: 121b0b11-f8ac-4ed6-a132-3800ca4fc07a
+version: 1
 date: '2020-10-07'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for CloudTrail events wherein a console login event
   by a user was recorded within the last hour, then compares the event to a lookup
   file of previously seen users (by ARN values) who have logged into the console.
   The alert is fired if the user has logged into the console for the first time within
   the last hour
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version
-  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
-  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
-  in CloudTrail - Initial` support search only once to create a baseline of previously
-  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
-  - Update` hourly (or more frequently depending on how often you run the detection
-  searches) to refresh the baselines. You can also provide additional filtering for
-  this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter`
-  macro.
-id: 121b0b11-f8ac-4ed6-a132-3800ca4fc07a
-known_false_positives: When a legitimate new user logins for the first time, this
-  activity will be detected. Check how old the account is and verify that the user
-  activity is legitimate.
-name: Detect AWS Console Login by User from New City
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src
   | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table
@@ -29,8 +21,21 @@ search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from da
   OR isnull(earliestseen), "New User","Old User") | where userCity = "New City" AND
   userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | table firstTime lastTime user City  userStatus userCity  | `detect_aws_console_login_by_user_from_new_city_filter`'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
+  in CloudTrail - Initial` support search only once to create a baseline of previously
+  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
+  - Update` hourly (or more frequently depending on how often you run the detection
+  searches) to refresh the baselines. You can also provide additional filtering for
+  this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter`
+  macro.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS Login Activities
   - Suspicious Cloud Authentication Activities
   asset_type: AWS Instance
@@ -55,5 +60,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml
index ff4de79335..49329e47c4 100644
--- a/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml
+++ b/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml
@@ -1,24 +1,16 @@
-author: Bhavin Patel, Splunk
+name: Detect AWS Console Login by User from New Country
+id: 67bd3def-c41c-4bf6-837b-ae196b4257c6
+version: 1
 date: '2020-10-07'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for CloudTrail events wherein a console login event
   by a user was recorded within the last hour, then compares the event to a lookup
   file of previously seen users (by ARN values) who have logged into the console.
   The alert is fired if the user has logged into the console for the first time within
   the last hour
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version
-  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
-  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
-  in CloudTrail - Initial` support search only once to create a baseline of previously
-  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
-  - Update` hourly (or more frequently depending on how often you run the detection
-  searches) to refresh the baselines. You can also provide additional filtering for
-  this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter`
-  macro.
-id: 67bd3def-c41c-4bf6-837b-ae196b4257c6
-known_false_positives: When a legitimate new user logins for the first time, this
-  activity will be detected. Check how old the account is and verify that the user
-  activity is legitimate.
-name: Detect AWS Console Login by User from New Country
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src
   | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table
@@ -29,8 +21,21 @@ search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from da
   OR isnull(earliestseen), "New User","Old User") | where userCountry = "New Country"
   AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | table firstTime lastTime user Country  userStatus userCountry  | `detect_aws_console_login_by_user_from_new_country_filter`'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
+  in CloudTrail - Initial` support search only once to create a baseline of previously
+  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
+  - Update` hourly (or more frequently depending on how often you run the detection
+  searches) to refresh the baselines. You can also provide additional filtering for
+  this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter`
+  macro.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS Login Activities
   - Suspicious Cloud Authentication Activities
   asset_type: AWS Instance
@@ -55,5 +60,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml
index f4bb82af75..965ab4d574 100644
--- a/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml
+++ b/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml
@@ -1,24 +1,16 @@
-author: Bhavin Patel, Splunk
+name: Detect AWS Console Login by User from New Region
+id: 9f31aa8e-e37c-46bc-bce1-8b3be646d026
+version: 1
 date: '2020-10-07'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Authentication
 description: This search looks for CloudTrail events wherein a console login event
   by a user was recorded within the last hour, then compares the event to a lookup
   file of previously seen users (by ARN values) who have logged into the console.
   The alert is fired if the user has logged into the console for the first time within
   the last hour
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version
-  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
-  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
-  in CloudTrail - Initial` support search only once to create a baseline of previously
-  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
-  - Update` hourly (or more frequently depending on how often you run the detection
-  searches) to refresh the baselines. You can also provide additional filtering for
-  this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter`
-  macro.
-id: 9f31aa8e-e37c-46bc-bce1-8b3be646d026
-known_false_positives: When a legitimate new user logins for the first time, this
-  activity will be detected. Check how old the account is and verify that the user
-  activity is legitimate.
-name: Detect AWS Console Login by User from New Region
 search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
   where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src
   | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table
@@ -29,8 +21,21 @@ search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from da
   "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userRegion = "New
   Region" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | table firstTime lastTime user Region  userStatus userRegion  | `detect_aws_console_login_by_user_from_new_region_filter`'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
+  in CloudTrail - Initial` support search only once to create a baseline of previously
+  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
+  - Update` hourly (or more frequently depending on how often you run the detection
+  searches) to refresh the baselines. You can also provide additional filtering for
+  this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter`
+  macro.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS Login Activities
   - Suspicious Cloud Authentication Activities
   asset_type: AWS Instance
@@ -55,5 +60,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml b/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml
index 9aada4bca1..d5a702a1cb 100644
--- a/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml
+++ b/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml
@@ -1,26 +1,13 @@
-author: Shannon Davis, Splunk
+name: Detect GCP Storage access from a new IP
+id: ccc3246a-daa1-11ea-87d0-0242ac130022
+version: 1
 date: '2020-08-10'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
 description: This search looks at GCP Storage bucket-access logs and detects new or
   previously unseen remote IP addresses that have successfully accessed a GCP Storage
   bucket.
-how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform,
-  setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and
-  logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
-  In order to capture public GCP Storage Bucket access logs, you must also enable
-  storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs.  These
-  logs are deposited into the nominated Storage Bucket on an hourly basis and typically
-  show up by 15 minutes past the hour.  It is recommended to configure any saved searches
-  or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes
-  past the hour (cron definition of 30 * * * *).  A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv)
-  stores the previously seen access requests, and is used by this search to determine
-  any newly seen IP addresses accessing the Storage Buckets.
-id: ccc3246a-daa1-11ea-87d0-0242ac130022
-known_false_positives: GCP Storage buckets can be accessed from any IP (if the ACLs
-  are open to allow it), as long as it can make a successful connection. This will
-  be a false postive, since the search is looking for a new IP within the past two
-  hours.
-name: Detect GCP Storage access from a new IP
-references: []
 search: '`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename
   cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri
   | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time)
@@ -32,8 +19,24 @@ search: '`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | r
   >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y
   %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table  first_time
   last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter`'
+how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform,
+  setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and
+  logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
+  In order to capture public GCP Storage Bucket access logs, you must also enable
+  storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs.  These
+  logs are deposited into the nominated Storage Bucket on an hourly basis and typically
+  show up by 15 minutes past the hour.  It is recommended to configure any saved searches
+  or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes
+  past the hour (cron definition of 30 * * * *).  A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv)
+  stores the previously seen access requests, and is used by this search to determine
+  any newly seen IP addresses accessing the Storage Buckets.
+known_false_positives: GCP Storage buckets can be accessed from any IP (if the ACLs
+  are open to allow it), as long as it can make a successful connection. This will
+  be a false postive, since the search is looking for a new IP within the past two
+  hours.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious GCP Storage Activities
   asset_type: GCP Storage Bucket
   cis20:
@@ -52,5 +55,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_new_open_gcp_storage_buckets.yml b/detections/cloud/detect_new_open_gcp_storage_buckets.yml
index 01816a5b41..cb8dfcf3fe 100644
--- a/detections/cloud/detect_new_open_gcp_storage_buckets.yml
+++ b/detections/cloud/detect_new_open_gcp_storage_buckets.yml
@@ -1,17 +1,12 @@
-author: Shannon Davis, Splunk
+name: Detect New Open GCP Storage Buckets
+id: f6ea3466-d6bb-11ea-87d0-0242ac130003
+version: 1
 date: '2020-08-05'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
 description: This search looks for GCP PubSub events where a user has created an open/public
   GCP Storage bucket.
-how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform,
-  setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and
-  logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
-id: f6ea3466-d6bb-11ea-87d0-0242ac130003
-known_false_positives: While this search has no known false positives, it is possible
-  that a GCP admin has legitimately created a public bucket for a specific purpose.
-  That said, GCP strongly advises against granting full control to the "allUsers"
-  group.
-name: Detect New Open GCP Storage Buckets
-references: []
 search: '`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions
   | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action
   | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath
@@ -21,8 +16,16 @@ search: '`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPay
   | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member
   | search (member=allUsers AND action=ADD) | table  _time, bucketName, src, user,
   location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter`'
+how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform,
+  setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and
+  logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
+known_false_positives: While this search has no known false positives, it is possible
+  that a GCP admin has legitimately created a public bucket for a specific purpose.
+  That said, GCP strongly advises against granting full control to the "allUsers"
+  group.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious GCP Storage Activities
   asset_type: GCP Storage Bucket
   cis20:
@@ -40,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_new_open_s3_buckets.yml b/detections/cloud/detect_new_open_s3_buckets.yml
index f6282510e6..7de7edd00e 100644
--- a/detections/cloud/detect_new_open_s3_buckets.yml
+++ b/detections/cloud/detect_new_open_s3_buckets.yml
@@ -1,15 +1,12 @@
-author: Bhavin Patel, Patrick Bareiss, Splunk
+name: Detect New Open S3 buckets
+id: 2a9b80d3-6340-4345-b5ad-290bf3d0dac4
+version: 2
 date: '2021-01-12'
+author: Bhavin Patel, Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where a user has created an open/public
   S3 bucket.
-how_to_implement: You must install the AWS App for Splunk.
-id: 2a9b80d3-6340-4345-b5ad-290bf3d0dac4
-known_false_positives: While this search has no known false positives, it is possible
-  that an AWS admin has legitimately created a public bucket for a specific purpose.
-  That said, AWS strongly advises against granting full control to the "All Users"
-  group.
-name: Detect New Open S3 buckets
-references: []
 search: '`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw
   "(?<json_field>{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{}
   | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI
@@ -19,8 +16,14 @@ search: '`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex
   max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission
   bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `detect_new_open_s3_buckets_filter` '
+how_to_implement: You must install the AWS App for Splunk.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately created a public bucket for a specific purpose.
+  That said, AWS strongly advises against granting full control to the "All Users"
+  group.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS S3 Activities
   asset_type: S3 Bucket
   automated_detection_testing: passed
@@ -44,5 +47,3 @@ tags:
   risk_object_type: system
   risk_score: 20
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml b/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml
index 786448cbc3..83af071e38 100644
--- a/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml
+++ b/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml
@@ -1,14 +1,12 @@
-author: Patrick Bareiss, Splunk
+name: Detect New Open S3 Buckets over AWS CLI
+id: 39c61d09-8b30-4154-922b-2d0a694ecc22
+version: 1
 date: '2021-01-12'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where a user has created an open/public
   S3 bucket over the aws cli.
-id: 39c61d09-8b30-4154-922b-2d0a694ecc22
-known_false_positives: While this search has no known false positives, it is possible
-  that an AWS admin has legitimately created a public bucket for a specific purpose.
-  That said, AWS strongly advises against granting full control to the "All Users"
-  group.
-name: Detect New Open S3 Buckets over AWS CLI
-references: []
 search: '`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp
   IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write
   IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp
@@ -19,8 +17,14 @@ search: '`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR r
   requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write
   requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` '
+how_to_implement: ''
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately created a public bucket for a specific purpose.
+  That said, AWS strongly advises against granting full control to the "All Users"
+  group.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS S3 Activities
   asset_type: S3 Bucket
   automated_detection_testing: passed
@@ -44,5 +48,3 @@ tags:
   risk_object_type: system
   risk_score: 20
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_s3_access_from_a_new_ip.yml b/detections/cloud/detect_s3_access_from_a_new_ip.yml
index 310f041a70..3cf6e5e656 100644
--- a/detections/cloud/detect_s3_access_from_a_new_ip.yml
+++ b/detections/cloud/detect_s3_access_from_a_new_ip.yml
@@ -1,18 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Detect S3 access from a new IP
+id: 2a9b80d3-6340-4345-b5ad-291bq3d0daq4
+version: 1
 date: '2018-06-28'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks at S3 bucket-access logs and detects new or previously
   unseen remote IP addresses that have successfully accessed an S3 bucket.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access
-  logs' inputs. This search works best when you run the "Previously Seen S3 Bucket
-  Access by Remote IP" support search once to create a history of previously seen
-  remote IPs and bucket names.
-id: 2a9b80d3-6340-4345-b5ad-291bq3d0daq4
-known_false_positives: S3 buckets can be accessed from any IP, as long as it can make
-  a successful connection. This will be a false postive, since the search is looking
-  for a new IP within the past hour
-name: Detect S3 access from a new IP
-references: []
 search: '`aws_s3_accesslogs` http_status=200  [search `aws_s3_accesslogs` http_status=200
   | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip
   | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime)
@@ -21,8 +15,17 @@ search: '`aws_s3_accesslogs` http_status=200  [search `aws_s3_accesslogs` http_s
   "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip
   | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access
+  logs' inputs. This search works best when you run the "Previously Seen S3 Bucket
+  Access by Remote IP" support search once to create a history of previously seen
+  remote IPs and bucket names.
+known_false_positives: S3 buckets can be accessed from any IP, as long as it can make
+  a successful connection. This will be a false postive, since the search is looking
+  for a new IP within the past hour
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS S3 Activities
   asset_type: S3 Bucket
   cis20:
@@ -44,5 +47,3 @@ tags:
   risk_object_type: system
   risk_score: 10
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml
index a09199ef31..f5081516f4 100644
--- a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml
+++ b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml
@@ -1,19 +1,12 @@
-author: Bhavin Patel, Splunk
-date: '2021-01-26'
 name: Detect Spike in AWS Security Hub Alerts for EC2 Instance
 id: 2a9b80d3-6340-4345-b5ad-290bf5d0d222
 version: 3
+date: '2021-01-26'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for a spike in number of of AWS security Hub alerts
   for an EC2 instance in 4 hours intervals
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security
-  Hub inputs. The threshold_value should be tuned to your environment and schedule
-  these searches according to the bucket span interval.
-type: ESCU
-references: []
-known_false_positives: None
-name: Detect Spike in AWS Security Hub Alerts for EC2 Instance
-references: []
 search: '`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h
   _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account)
   as vendor_account values(vendor_region) as vendor_region values(severity) as severity
@@ -21,12 +14,21 @@ search: '`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket sp
   | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev
   * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types
   vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security
+  Hub inputs. The threshold_value should be tuned to your environment and schedule
+  these searches according to the bucket span interval.
+known_false_positives: None
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Security Hub Alerts
   asset_type: AWS Instance
+  automated_detection_testing: passed
   cis20:
   - CIS 13
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json
   nist:
   - DE.DP
   - DE.AE
@@ -35,6 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-  automated_detection_testing: passed
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json
diff --git a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml
index 7ef4dcb70c..a2cbabf3fe 100644
--- a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml
+++ b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml
@@ -1,27 +1,25 @@
-author: Bhavin Patel, Splunk
+name: Detect Spike in AWS Security Hub Alerts for User
+id: 2a9b80d3-6220-4345-b5ad-290bf5d0d222
+version: 3
 date: '2021-01-26'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for a spike in number of of AWS security Hub alerts
   for an AWS IAM User in 4 hours intervals.
-id: 2a9b80d3-6220-4345-b5ad-290bf5d0d222
+search: '`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename
+  findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts
+  by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev
+  | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev
+  * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter`'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub
-  inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
-author: Bhavin Patel, Splunk
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security
+  Hub inputs. The threshold_value should be tuned to your environment and schedule
+  these searches according to the bucket span interval.
 known_false_positives: None
-name: Detect Spike in AWS Security Hub Alerts for User
 references: []
-search: '`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time
-| stats count AS alerts by _time user
-| eventstats avg(alerts)
-    as total_launched_avg, stdev(alerts) as total_launched_stdev
-| eval
-    threshold_value = 2
-| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev
-    * threshold_value), 1, 0)
-| search isOutlier=1
-| table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Security Hub Alerts
   asset_type: AWS Instance
   cis20:
@@ -34,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml b/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml
index cc0d56a0bb..fbc6eef691 100644
--- a/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml
+++ b/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml
@@ -1,24 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Detect Spike in blocked Outbound Traffic from your AWS
+id: ada0f278-84a8-46w1-a3f1-w32372d4bd53
+version: 1
 date: '2018-05-07'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search will detect spike in blocked outbound network connections
   originating from within your AWS environment.  It will also update the cache file
   that factors in the latest data.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow
-  logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
-  your environment. The `dataPointThreshold` variable is the number of data points
-  required to meet the definition of "spike." The `deviationThreshold` variable is
-  the number of standard deviations away from the mean that the value must be to be
-  considered a spike. This search works best when you run the "Baseline of Blocked
-  Outbound Connection" support search once to create a history of previously seen
-  blocked outbound connections.
-id: ada0f278-84a8-46w1-a3f1-w32372d4bd53
-known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold`
-  and `deviationThreshold`. Additionally, false positives may result when AWS administrators
-  roll out policies enforcing network blocks, causing sudden increases in the number
-  of blocked outbound connections.
-name: Detect Spike in blocked Outbound Traffic from your AWS
-references: []
 search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12
   OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  [search  `cloudwatchlogs_vpcflow`
   action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16)
@@ -37,8 +26,22 @@ search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=17
   | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId"
   count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip
   | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow
+  logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
+  your environment. The `dataPointThreshold` variable is the number of data points
+  required to meet the definition of "spike." The `deviationThreshold` variable is
+  the number of standard deviations away from the mean that the value must be to be
+  considered a spike. This search works best when you run the "Baseline of Blocked
+  Outbound Connection" support search once to create a history of previously seen
+  blocked outbound connections.
+known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold`
+  and `deviationThreshold`. Additionally, false positives may result when AWS administrators
+  roll out policies enforcing network blocks, causing sudden increases in the number
+  of blocked outbound connections.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Suspicious AWS Traffic
   - Command and Control
@@ -60,5 +63,3 @@ tags:
   risk_object_type: system
   risk_score: 20
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/detect_spike_in_s3_bucket_deletion.yml b/detections/cloud/detect_spike_in_s3_bucket_deletion.yml
index 6c1402b84d..e43dd157f8 100644
--- a/detections/cloud/detect_spike_in_s3_bucket_deletion.yml
+++ b/detections/cloud/detect_spike_in_s3_bucket_deletion.yml
@@ -1,22 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Detect Spike in S3 Bucket deletion
+id: ad12w478-84a8-4641-a3w1-e32372q4bd53
+version: 1
 date: '2018-11-27'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search detects users creating spikes in API activity related to
   deletion of S3 buckets in your AWS environment. It will also update the cache file
   that factors in the latest data.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
-  your environment. The `dataPointThreshold` variable is the minimum number of data
-  points required to have a statistically significant amount of data to determine.
-  The `deviationThreshold` variable is the number of standard deviations away from
-  the mean that the value must be to be considered a spike. This search works best
-  when you run the "Baseline of S3 Bucket deletion activity by ARN" support search
-  once to create a baseline of previously seen S3 bucket-deletion activity.
-id: ad12w478-84a8-4641-a3w1-e32372q4bd53
-known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`,
-  the false positive rate may vary. Please modify this according the your environment.
-name: Detect Spike in S3 Bucket deletion
-references: []
 search: '`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket
   | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup
   s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn
@@ -31,8 +22,20 @@ search: '`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=Dele
   table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName
   path=requestParameters.bucketName | stats values(bucketName) as bucketName, count
   as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
+  your environment. The `dataPointThreshold` variable is the minimum number of data
+  points required to have a statistically significant amount of data to determine.
+  The `deviationThreshold` variable is the number of standard deviations away from
+  the mean that the value must be to be considered a spike. This search works best
+  when you run the "Baseline of S3 Bucket deletion activity by ARN" support search
+  once to create a baseline of previously seen S3 bucket-deletion activity.
+known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`,
+  the false positive rate may vary. Please modify this according the your environment.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS S3 Activities
   asset_type: S3 Bucket
   cis20:
@@ -53,5 +56,3 @@ tags:
   risk_object_type: user
   risk_score: 10
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/cloud/high_number_of_login_failures_from_a_single_source.yml b/detections/cloud/high_number_of_login_failures_from_a_single_source.yml
index 30146d37b1..549076642f 100644
--- a/detections/cloud/high_number_of_login_failures_from_a_single_source.yml
+++ b/detections/cloud/high_number_of_login_failures_from_a_single_source.yml
@@ -1,18 +1,23 @@
-author: Bhavin Patel, Splunk
+name: High Number of Login Failures from a single source
+id: 7f398cfb-918d-41f4-8db8-2e2474e02222
+version: 1
 date: '2020-12-16'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search will detect more than 5 login failures in Office365 Azure
   Active Directory from a single source IP address. Please adjust the threshold value
   of 5 as suited for your environment.
-id: 7f398cfb-918d-41f4-8db8-2e2474e02222
-known_false_positives: unknown
-name: High Number of Login Failures from a single source
 search: '`o365_management_activity` Operation=UserLoginFailed  record_type=AzureActiveDirectoryStsLogon
   app=AzureActiveDirectory | stats count dc(user) as accounts_locked values(user)
   as user values(LogonError) as LogonError values(authentication_method) as authentication_method
   values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type
   Operation app | search accounts_locked >= 5| `high_number_of_login_failures_from_a_single_source_filter`'
+how_to_implement: ''
+known_false_positives: unknown
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   asset_type: Office 365
   cis20:
@@ -29,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/new_container_uploaded_to_aws_ecr.yml b/detections/cloud/new_container_uploaded_to_aws_ecr.yml
index 6e56015841..0ce8471f56 100644
--- a/detections/cloud/new_container_uploaded_to_aws_ecr.yml
+++ b/detections/cloud/new_container_uploaded_to_aws_ecr.yml
@@ -1,24 +1,27 @@
-author: Rod Soto, Rico Valdez, Splunk
+name: New container uploaded to AWS ECR
+id: f0f70b40-f7ad-489d-9905-23d149da8099
+version: 1
 date: '2020-02-20'
+author: Rod Soto, Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This searches show information on uploaded containers including source
   user, image id, source IP user type, http user agent, region, first time, last time
   of operation (PutImage). These searches are based on Cloud Infrastructure Data Model.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. You must also install Cloud Infrastructure data model. Please also customize
-  the `container_implant_aws_detection_filter` macro to filter out the false positives.
-id: f0f70b40-f7ad-489d-9905-23d149da8099
-known_false_positives: Uploading container is a normal behavior from developers or
-  users with access to container registry.
-name: New container uploaded to AWS ECR
-references: []
 search: '| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute
   where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal"
   AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src
   Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")`
   | `new_container_uploaded_to_aws_ecr_filter` '
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. You must also install Cloud Infrastructure data model. Please also customize
+  the `container_implant_aws_detection_filter` macro to filter out the false positives.
+known_false_positives: Uploading container is a normal behavior from developers or
+  users with access to container registry.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Container Implantation Monitoring and Investigation
   asset_type: AWS ECR container
   mitre_attack_id:
@@ -28,5 +31,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/o365_add_app_role_assignment_grant_user.yml b/detections/cloud/o365_add_app_role_assignment_grant_user.yml
index 4f393bbcb6..d3fb9a40cb 100644
--- a/detections/cloud/o365_add_app_role_assignment_grant_user.yml
+++ b/detections/cloud/o365_add_app_role_assignment_grant_user.yml
@@ -1,34 +1,39 @@
-author: Rod Soto, Splunk
+name: O365 Add App Role Assignment Grant User
+id: b2c81cc6-6040-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search detects the creation of a new Federation setting by alerting
   about an specific event related to its creation.
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
-  works with o365:management:activity
-id: b2c81cc6-6040-11eb-ae93-0242ac130002
-known_false_positives: The creation of a new Federation is not necessarily malicious,
-  however this events need to be followed closely, as it may indicate federated credential
-  abuse or backdoor via federated identities at a different cloud provider.
-name: O365 Add App Role Assignment Grant User
-references:
-- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
-- https://us-cert.cisa.gov/ncas/alerts/aa21-008a
 search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app
   role assignment grant to user." | stats count min(_time) as firstTime max(_time)
   as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by
   ActorIpAddress dest ResultStatus | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `o365_add_app_role_assignment_grant_user_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: The creation of a new Federation is not necessarily malicious,
+  however this events need to be followed closely, as it may indicate federated credential
+  abuse or backdoor via federated identities at a different cloud provider.
+references:
+- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
+- https://us-cert.cisa.gov/ncas/alerts/aa21-008a
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   - Cloud Federated Credential Abuse
   asset_type: Office 365
+  automated_detection_testing: passed
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json
   kill_chain_phases:
   - Actions on Objective
   mitre_attack_id:
   - T1136.003
   security_domain: threat
-  automated_detection_testing: passed
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json
-type: ESCU
-version: 1
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/cloud/o365_added_service_principal.yml b/detections/cloud/o365_added_service_principal.yml
index 88e3123ed8..feb1dfc3ea 100644
--- a/detections/cloud/o365_added_service_principal.yml
+++ b/detections/cloud/o365_added_service_principal.yml
@@ -1,37 +1,42 @@
-author: Rod Soto, Splunk
+name: O365 Added Service Principal
+id: 1668812a-6047-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search detects the creation of a new Federation setting by alerting
   about an specific event related to its creation.
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
-  works with o365:management:activity
-id: 1668812a-6047-11eb-ae93-0242ac130002
-known_false_positives: The creation of a new Federation is not necessarily malicious,
-  however these events need to be followed closely, as it may indicate federated credential
-  abuse or backdoor via federated identities at a different cloud provider.
-name: O365 Added Service Principal
-references:
-- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
-- https://us-cert.cisa.gov/ncas/alerts/aa21-008a
-- https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
-- https://www.sygnia.co/golden-saml-advisory
 search: '`o365_management_activity` Workload=AzureActiveDirectory signature="Add service
   principal credentials." | stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID)
   as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue)
   as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress
   signature | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `o365_added_service_principal_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: The creation of a new Federation is not necessarily malicious,
+  however these events need to be followed closely, as it may indicate federated credential
+  abuse or backdoor via federated identities at a different cloud provider.
+references:
+- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
+- https://us-cert.cisa.gov/ncas/alerts/aa21-008a
+- https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
+- https://www.sygnia.co/golden-saml-advisory
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   - Cloud Federated Credential Abuse
   asset_type: Office 365
+  automated_detection_testing: passed
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json
   kill_chain_phases:
   - Actions on Objective
   mitre_attack_id:
   - T1136.003
   security_domain: threat
-  automated_detection_testing: passed
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json
-type: ESCU
-version: 1
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml b/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml
index a1e0006fb7..068ee74a67 100644
--- a/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml
+++ b/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml
@@ -1,17 +1,13 @@
-author: Bhavin Patel, Splunk
+name: O365 Bypass MFA via Trusted IP
+id: c783dd98-c703-4252-9e8a-f19d9f66949e
+version: 1
 date: '2021-01-12'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search detects newly added IP addresses/CIDR blocks to the list
   of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known
   to use this technique so that they can bypass the MFA system.
-how_to_implement: You must install Splunk Microsoft Office 365 add-on. This search
-  works with o365:management:activity
-id: c783dd98-c703-4252-9e8a-f19d9f66949e
-known_false_positives: Unless it is a special case, it is uncommon to continually
-  update Trusted IPs to MFA configuration.
-name: O365 Bypass MFA via Trusted IP
-references:
-- https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf
-- https://attack.mitre.org/techniques/T1562/007/
 search: '`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy
   | rex max_match=100 field=ModifiedProperties{}.NewValue "(?<ip_addresses_new_added>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})"
   | rex max_match=100 field=ModifiedProperties{}.OldValue "(?<ip_addresses_old>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})"
@@ -21,8 +17,15 @@ search: '`o365_management_activity` signature="Set Company Information." Modifie
   as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account
   status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
   `o365_bypass_mfa_via_trusted_ip_filter`'
+how_to_implement: You must install Splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: Unless it is a special case, it is uncommon to continually
+  update Trusted IPs to MFA configuration.
+references:
+- https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf
+- https://attack.mitre.org/techniques/T1562/007/
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   asset_type: Office 365
   automated_detection_testing: passed
@@ -37,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/o365_disable_mfa.yml b/detections/cloud/o365_disable_mfa.yml
index 78565e14c6..6f2bafe120 100644
--- a/detections/cloud/o365_disable_mfa.yml
+++ b/detections/cloud/o365_disable_mfa.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: O365 Disable MFA
+id: c783dd98-c703-4252-9e8a-f19d9f5c949e
+version: 1
 date: '2020-12-16'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search detects when multi factor authentication has been disabled,
   what entitiy performed the action and against what user
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
-  works with o365:management:activity
-id: c783dd98-c703-4252-9e8a-f19d9f5c949e
-known_false_positives: Unless it is a special case, it is uncommon to disable MFA
-  or Strong Authentication
-name: O365 Disable MFA
-references:
-- https://attack.mitre.org/techniques/T1556/
 search: '`o365_management_activity` Operation="Disable Strong Authentication." | stats
   count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation
   user status signature dest ResultStatus |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`
   | `o365_disable_mfa_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: Unless it is a special case, it is uncommon to disable MFA
+  or Strong Authentication
+references:
+- https://attack.mitre.org/techniques/T1556/
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   asset_type: Office 365
   automated_detection_testing: passed
@@ -30,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/o365_excessive_authentication_failures_alert.yml b/detections/cloud/o365_excessive_authentication_failures_alert.yml
index 1713026e6c..7bed02ff02 100644
--- a/detections/cloud/o365_excessive_authentication_failures_alert.yml
+++ b/detections/cloud/o365_excessive_authentication_failures_alert.yml
@@ -1,22 +1,25 @@
-author: Rod Soto, Splunk
+name: O365 Excessive Authentication Failures Alert
+id: d441364c-349c-453b-b55f-12eccab67cf9
+version: 1
 date: '2020-12-16'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search detects when an excessive number of authentication failures
   occur this search also includes attempts against MFA prompt codes
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
-  works with o365:management:activity
-id: d441364c-349c-453b-b55f-12eccab67cf9
-known_false_positives: The threshold for alert is above 10 attempts and this should
-  reduce the number of false positives.
-name: O365 Excessive Authentication Failures Alert
-references:
-- https://attack.mitre.org/techniques/T1110/
 search: '`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=*
   status=Failed | stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod)
   AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status
   values(src_ip) AS src_ip by user | where count > 10 |`security_content_ctime(firstTime)`
   |`security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: The threshold for alert is above 10 attempts and this should
+  reduce the number of false positives.
+references:
+- https://attack.mitre.org/techniques/T1110/
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   asset_type: Office 365
   automated_detection_testing: passed
@@ -31,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/o365_excessive_sso_logon_errors.yml b/detections/cloud/o365_excessive_sso_logon_errors.yml
index 2195e7caf8..ce4f398b72 100644
--- a/detections/cloud/o365_excessive_sso_logon_errors.yml
+++ b/detections/cloud/o365_excessive_sso_logon_errors.yml
@@ -1,32 +1,37 @@
-author: Rod Soto, Splunk
+name: O365 Excessive SSO logon errors
+id: 8158ccc4-6038-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search detects accounts with high number of Single Sign ON (SSO)
   logon errors. Excessive logon errors may indicate attempts to bruteforce of password
   or single sign on token hijack or reuse.
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
-  works with o365:management:activity
-id: 8158ccc4-6038-11eb-ae93-0242ac130002
-known_false_positives: Logon errors may not be malicious in nature however it may
-  indicate attempts to reuse a token or password obtained via credential access attack.
-name: O365 Excessive SSO logon errors
-references:
-- https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/
 search: '`o365_management_activity`  Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired
   | stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress
   UserAgent UserId | where count > 5 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `o365_excessive_sso_logon_errors_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: Logon errors may not be malicious in nature however it may
+  indicate attempts to reuse a token or password obtained via credential access attack.
+references:
+- https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   - Cloud Federated Credential Abuse
   asset_type: Office 365
+  automated_detection_testing: passed
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json
   kill_chain_phases:
   - Actions on Objective
   mitre_attack_id:
   - T1556
   security_domain: threat
-  automated_detection_testing: passed
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json
-type: ESCU
-version: 1
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/cloud/o365_new_federated_domain_added.yml b/detections/cloud/o365_new_federated_domain_added.yml
index 949f8a081e..21eb289874 100644
--- a/detections/cloud/o365_new_federated_domain_added.yml
+++ b/detections/cloud/o365_new_federated_domain_added.yml
@@ -1,36 +1,41 @@
-author: Rod Soto, Splunk
+name: O365 New Federated Domain Added
+id: e155876a-6048-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search detects the addition of a new Federated domain.
+search: '`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain"
+  | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value)
+  as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId
+  UserKey | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_new_federated_domain_added_filter`'
 how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
   works with o365:management:activity.
-id: e155876a-6048-11eb-ae93-0242ac130002
 known_false_positives: The creation of a new Federated domain is not necessarily malicious,
   however these events need to be followed closely, as it may indicate federated credential
   abuse or backdoor via federated identities at a similar or different cloud provider.
-name: O365 New Federated Domain Added
 references:
 - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
 - https://us-cert.cisa.gov/ncas/alerts/aa21-008a
 - https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
 - https://www.sygnia.co/golden-saml-advisory
 - https://o365blog.com/post/aadbackdoor/
-search: '`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain"
-  | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value)
-  as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId
-  UserKey | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `o365_new_federated_domain_added_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   - Cloud Federated Credential Abuse
   asset_type: Office 365
+  automated_detection_testing: passed
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json
   kill_chain_phases:
   - Actions on Objective
   mitre_attack_id:
   - T1136.003
   security_domain: threat
-  automated_detection_testing: passed
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json
-type: ESCU
-version: 1
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/cloud/o365_pst_export_alert.yml b/detections/cloud/o365_pst_export_alert.yml
index 369dc6cc1e..ff9b4fdc02 100644
--- a/detections/cloud/o365_pst_export_alert.yml
+++ b/detections/cloud/o365_pst_export_alert.yml
@@ -1,22 +1,25 @@
-author: Rod Soto, Splunk
+name: O365 PST export alert
+id: 5f694cc4-a678-4a60-9410-bffca1b647dc
+version: 1
 date: '2020-12-16'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search detects when a user has performed an Ediscovery search or
   exported a PST file from the search. This PST file usually has sensitive information
   including email body content
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
-  works with o365:management:activity
-id: 5f694cc4-a678-4a60-9410-bffca1b647dc
-known_false_positives: PST export can be done for legitimate purposes but due to the
-  sensitive nature of its content it must be monitored.
-name: O365 PST export alert
-references:
-- https://attack.mitre.org/techniques/T1114/
 search: '`o365_management_activity` Category=ThreatManagement Name="eDiscovery search
   started or exported" | stats count earliest(_time) as firstTime latest(_time) as
   lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)`
   |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: PST export can be done for legitimate purposes but due to the
+  sensitive nature of its content it must be monitored.
+references:
+- https://attack.mitre.org/techniques/T1114/
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   asset_type: Office 365
   automated_detection_testing: passed
@@ -31,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/o365_suspicious_admin_email_forwarding.yml b/detections/cloud/o365_suspicious_admin_email_forwarding.yml
index 8bd3a6fc0c..0b68dd9f0f 100644
--- a/detections/cloud/o365_suspicious_admin_email_forwarding.yml
+++ b/detections/cloud/o365_suspicious_admin_email_forwarding.yml
@@ -1,17 +1,22 @@
-author: Patrick Bareiss, Splunk
+name: O365 Suspicious Admin Email Forwarding
+id: 7f398cfb-918d-41f4-8db8-2e2474e02c28
+version: 1
 date: '2020-12-16'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search detects when an admin configured a forwarding rule for multiple
   mailboxes to the same destination.
-id: 7f398cfb-918d-41f4-8db8-2e2474e02c28
-known_false_positives: unknown
-name: O365 Suspicious Admin Email Forwarding
 search: '`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters
   | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user)
   AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user)
   AS src_user values(user) AS user by ForwardingAddress | where count_src_user > 1
   |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter`'
+how_to_implement: ''
+known_false_positives: unknown
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   asset_type: Office 365
   automated_detection_testing: passed
@@ -31,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/o365_suspicious_rights_delegation.yml b/detections/cloud/o365_suspicious_rights_delegation.yml
index 717be1f122..7c149be71d 100644
--- a/detections/cloud/o365_suspicious_rights_delegation.yml
+++ b/detections/cloud/o365_suspicious_rights_delegation.yml
@@ -1,17 +1,22 @@
-author: Patrick Bareiss, Splunk
+name: O365 Suspicious Rights Delegation
+id: b25d2973-303e-47c8-bacd-52b61604c6a7
+version: 1
 date: '2020-12-15'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search detects the assignment of rights to accesss content from
   another mailbox. This is usually only assigned to a service account.
-id: b25d2973-303e-47c8-bacd-52b61604c6a7
-known_false_positives: Service Accounts
-name: O365 Suspicious Rights Delegation
 search: '`o365_management_activity` Operation=Add-MailboxPermission | spath input=Parameters
   | rename User AS src_user, Identity AS dest_user | search AccessRights=FullAccess
   OR AccessRights=SendAs OR AccessRights=SendOnBehalf | stats count earliest(_time)
   as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights
   |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_rights_delegation_filter`'
+how_to_implement: ''
+known_false_positives: Service Accounts
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   asset_type: Office 365
   automated_detection_testing: passed
@@ -31,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/cloud/o365_suspicious_user_email_forwarding.yml b/detections/cloud/o365_suspicious_user_email_forwarding.yml
index 920f095128..11df462b65 100644
--- a/detections/cloud/o365_suspicious_user_email_forwarding.yml
+++ b/detections/cloud/o365_suspicious_user_email_forwarding.yml
@@ -1,17 +1,22 @@
-author: Patrick Bareiss, Splunk
+name: O365 Suspicious User Email Forwarding
+id: f8dfe015-dbb3-4569-ba75-b13787e06aa4
+version: 1
 date: '2020-12-16'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search detects when multiple user configured a forwarding rule to
   the same destination.
-id: f8dfe015-dbb3-4569-ba75-b13787e06aa4
-known_false_positives: unknown
-name: O365 Suspicious User Email Forwarding
 search: '`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters
   | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user)
   AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user)
   AS src_user values(user) AS user by ForwardingSmtpAddress | where count_src_user
   > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter`'
+how_to_implement: ''
+known_false_positives: unknown
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Office 365 Detections
   asset_type: Office 365
   automated_detection_testing: passed
@@ -31,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/deprecated/abnormally_high_aws_instances_launched_by_user.yml b/detections/deprecated/abnormally_high_aws_instances_launched_by_user.yml
index 0c76eb70c8..04a951ed8e 100644
--- a/detections/deprecated/abnormally_high_aws_instances_launched_by_user.yml
+++ b/detections/deprecated/abnormally_high_aws_instances_launched_by_user.yml
@@ -1,18 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Abnormally High AWS Instances Launched by User
+id: 2a9b80d3-6340-4345-b5ad-290bf5d0dac4
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where a user successfully launches
   an abnormally high number of instances. This search is deprecated and have been
   translated to use the latest Change Datamodel
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. The threshold value should be tuned to your environment.
-id: 2a9b80d3-6340-4345-b5ad-290bf5d0dac4
-known_false_positives: Many service accounts configured within an AWS infrastructure
-  are known to exhibit this behavior. Please adjust the threshold values and filter
-  out service accounts from the output. Always verify if this search alerted on a
-  human user.
-name: Abnormally High AWS Instances Launched by User
-references: []
 search: '`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time
   | stats count AS instances_launched by _time userName | eventstats avg(instances_launched)
   as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval
@@ -21,8 +16,16 @@ search: '`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m
   "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg)
   / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away,
   total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. The threshold value should be tuned to your environment.
+known_false_positives: Many service accounts configured within an AWS infrastructure
+  are known to exhibit this behavior. Please adjust the threshold values and filter
+  out service accounts from the output. Always verify if this search alerted on a
+  human user.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Suspicious AWS EC2 Activities
   asset_type: AWS Instance
@@ -43,5 +46,3 @@ tags:
   risk_object_type: user
   risk_score: 40
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/abnormally_high_aws_instances_launched_by_user___mltk.yml b/detections/deprecated/abnormally_high_aws_instances_launched_by_user___mltk.yml
index a683b8a332..1f65472313 100644
--- a/detections/deprecated/abnormally_high_aws_instances_launched_by_user___mltk.yml
+++ b/detections/deprecated/abnormally_high_aws_instances_launched_by_user___mltk.yml
@@ -1,24 +1,27 @@
-author: Jason Brewer, Splunk
+name: Abnormally High AWS Instances Launched by User - MLTK
+id: dec41ad5-d579-42cb-b4c6-f5dbb778bbe5
+version: 2
 date: '2020-07-21'
+author: Jason Brewer, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where a user successfully launches
   an abnormally high number of instances. This search is deprecated and have been
   translated to use the latest Change Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. The threshold value should be tuned to your environment.
-id: dec41ad5-d579-42cb-b4c6-f5dbb778bbe5
-known_false_positives: Many service accounts configured within an AWS infrastructure
-  are known to exhibit this behavior. Please adjust the threshold values and filter
-  out service accounts from the output. Always verify if this search alerted on a
-  human user.
-name: Abnormally High AWS Instances Launched by User - MLTK
-references: []
 search: '`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter`
   | bucket span=10m _time  | stats count as instances_launched by _time src_user  |
   apply ec2_excessive_runinstances_v1  | rename "IsOutlier(instances_launched)" as
   isOutlier  | where isOutlier=1'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. The threshold value should be tuned to your environment.
+known_false_positives: Many service accounts configured within an AWS infrastructure
+  are known to exhibit this behavior. Please adjust the threshold values and filter
+  out service accounts from the output. Always verify if this search alerted on a
+  human user.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Suspicious AWS EC2 Activities
   asset_type: AWS Instance
@@ -39,5 +42,3 @@ tags:
   risk_object_type: user
   risk_score: 10
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/abnormally_high_aws_instances_terminated_by_user.yml b/detections/deprecated/abnormally_high_aws_instances_terminated_by_user.yml
index 7aa2cac30f..3a96f41ae8 100644
--- a/detections/deprecated/abnormally_high_aws_instances_terminated_by_user.yml
+++ b/detections/deprecated/abnormally_high_aws_instances_terminated_by_user.yml
@@ -1,18 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Abnormally High AWS Instances Terminated by User
+id: ada0f478-84a8-4641-s3f3-d82362dffd75
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where an abnormally high number
   of instances were successfully terminated by a user in a 10-minute window. This
   search is deprecated and have been translated to use the latest Change Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs.
-id: ada0f478-84a8-4641-s3f3-d82362dffd75
-known_false_positives: Many service accounts configured with your AWS infrastructure
-  are known to exhibit this behavior. Please adjust the threshold values and filter
-  out service accounts from the output. Always verify whether this search alerted
-  on a human user.
-name: Abnormally High AWS Instances Terminated by User
-references: []
 search: '`cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m
   _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated)
   as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev
@@ -22,8 +17,16 @@ search: '`cloudtrail` eventName=TerminateInstances errorCode=success | bucket sp
   total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated,
   num_standard_deviations_away, total_terminations_avg, total_terminations_stdev |
   `abnormally_high_aws_instances_terminated_by_user_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs.
+known_false_positives: Many service accounts configured with your AWS infrastructure
+  are known to exhibit this behavior. Please adjust the threshold values and filter
+  out service accounts from the output. Always verify whether this search alerted
+  on a human user.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS EC2 Activities
   asset_type: AWS Instance
   cis20:
@@ -40,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/abnormally_high_aws_instances_terminated_by_user___mltk.yml b/detections/deprecated/abnormally_high_aws_instances_terminated_by_user___mltk.yml
index 4f869ee643..da395bc609 100644
--- a/detections/deprecated/abnormally_high_aws_instances_terminated_by_user___mltk.yml
+++ b/detections/deprecated/abnormally_high_aws_instances_terminated_by_user___mltk.yml
@@ -1,24 +1,27 @@
-author: Jason Brewer, Splunk
+name: Abnormally High AWS Instances Terminated by User - MLTK
+id: 1c02b86a-cd85-473e-a50b-014a9ac8fe3e
+version: 2
 date: '2020-07-21'
+author: Jason Brewer, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where a user successfully terminates
   an abnormally high number of instances. This search is deprecated and have been
   translated to use the latest Change Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. The threshold value should be tuned to your environment.
-id: 1c02b86a-cd85-473e-a50b-014a9ac8fe3e
-known_false_positives: Many service accounts configured within an AWS infrastructure
-  are known to exhibit this behavior. Please adjust the threshold values and filter
-  out service accounts from the output. Always verify if this search alerted on a
-  human user.
-name: Abnormally High AWS Instances Terminated by User - MLTK
-references: []
 search: '`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter`
   | bucket span=10m _time  | stats count as instances_terminated by _time src_user  |
   apply ec2_excessive_terminateinstances_v1  | rename "IsOutlier(instances_terminated)"
   as isOutlier  | where isOutlier=1'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. The threshold value should be tuned to your environment.
+known_false_positives: Many service accounts configured within an AWS infrastructure
+  are known to exhibit this behavior. Please adjust the threshold values and filter
+  out service accounts from the output. Always verify if this search alerted on a
+  human user.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS EC2 Activities
   asset_type: AWS Instance
   cis20:
@@ -35,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_city.yml b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_city.yml
index 0b5fa730e0..cd02a37e25 100644
--- a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_city.yml
+++ b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_city.yml
@@ -1,15 +1,29 @@
-author: David Dorsey, Splunk
+name: AWS Cloud Provisioning From Previously Unseen City
+id: 344a1778-0b25-490c-adb1-de8beddf59cd
+version: 1
 date: '2018-03-16'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: 'This search looks for AWS provisioning activities from previously unseen
   cities.  Provisioning activities are defined broadly as any event that begins with
   "Run" or "Create." This search is deprecated and have been translated to use the
   latest Change Datamodel. '
+search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
+  | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation
+  sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time)
+  as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv
+  | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress,
+  City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv
+  | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime
+  >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath
+  output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user,
+  src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter`'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs. This search works best when you run the "Previously Seen AWS Provisioning
   Activity Sources" support search once to create a history of previously seen locations
   that have provisioned AWS resources.
-id: 344a1778-0b25-490c-adb1-de8beddf59cd
 known_false_positives: "This is a strictly behavioral search, so we define \"false\
   \ positive\" slightly differently. Every time this fires, it will accurately reflect\
   \ the first occurrence in the time period you're searching within, plus what is\
@@ -21,20 +35,9 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal
   \ free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution\
   \ (particularly small countries in less economically powerful regions), this may\
   \ be much less valuable to you."
-name: AWS Cloud Provisioning From Previously Unseen City
 references: []
-search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
-  | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation
-  sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time)
-  as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv
-  | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress,
-  City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv
-  | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime
-  >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath
-  output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user,
-  src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Suspicious Provisioning Activities
   asset_type: AWS Instance
   cis20:
@@ -51,5 +54,3 @@ tags:
   risk_object_type: user
   risk_score: 25
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_country.yml b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_country.yml
index b79bf256eb..97a40cd24b 100644
--- a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_country.yml
+++ b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_country.yml
@@ -1,28 +1,14 @@
-author: David Dorsey, Splunk
+name: AWS Cloud Provisioning From Previously Unseen Country
+id: ceb8d3d8-06cb-49eb-beaf-829526e33ff0
+version: 1
 date: '2018-03-16'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: 'This search looks for AWS provisioning activities from previously unseen
   countries. Provisioning activities are defined broadly as any event that begins
   with "Run" or "Create." This search is deprecated and have been translated to use
   the latest Change Datamodel. '
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. This search works best when you run the "Previously Seen AWS Provisioning
-  Activity Sources" support search once to create a history of previously seen locations
-  that have provisioned AWS resources.
-id: ceb8d3d8-06cb-49eb-beaf-829526e33ff0
-known_false_positives: "This is a strictly behavioral search, so we define \"false\
-  \ positive\" slightly differently. Every time this fires, it will accurately reflect\
-  \ the first occurrence in the time period you're searching over plus what is stored\
-  \ in the cache feature. But while there are really no \"false positives\" in a traditional\
-  \ sense, there is definitely lots of noise.\\\n This search will fire any time a\
-  \ new country is seen in the **GeoIP** database for any kind of provisioning activity.\
-  \ If you typically do all provisioning from tools inside of your country, there\
-  \ should be few false positives. If you are located in countries where the free\
-  \ version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution\
-  \ (particularly small countries in less economically powerful regions), this may\
-  \ be much less valuable to you."
-name: AWS Cloud Provisioning From Previously Unseen Country
-references: []
 search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
   | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) |
   iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime,
@@ -34,8 +20,25 @@ search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceI
   >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] |
   spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time,
   user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. This search works best when you run the "Previously Seen AWS Provisioning
+  Activity Sources" support search once to create a history of previously seen locations
+  that have provisioned AWS resources.
+known_false_positives: "This is a strictly behavioral search, so we define \"false\
+  \ positive\" slightly differently. Every time this fires, it will accurately reflect\
+  \ the first occurrence in the time period you're searching over plus what is stored\
+  \ in the cache feature. But while there are really no \"false positives\" in a traditional\
+  \ sense, there is definitely lots of noise.\\\n This search will fire any time a\
+  \ new country is seen in the **GeoIP** database for any kind of provisioning activity.\
+  \ If you typically do all provisioning from tools inside of your country, there\
+  \ should be few false positives. If you are located in countries where the free\
+  \ version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution\
+  \ (particularly small countries in less economically powerful regions), this may\
+  \ be much less valuable to you."
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Suspicious Provisioning Activities
   asset_type: AWS Instance
   cis20:
@@ -49,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_ip_address.yml b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_ip_address.yml
index b41eae382f..d143b96e1c 100644
--- a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_ip_address.yml
+++ b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_ip_address.yml
@@ -1,15 +1,29 @@
-author: David Dorsey, Splunk
+name: AWS Cloud Provisioning From Previously Unseen IP Address
+id: 42e15012-ac14-4801-94f4-f1acbe64880b
+version: 1
 date: '2018-03-16'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: 'This search looks for AWS provisioning activities from previously unseen
   IP addresses. Provisioning activities are defined broadly as any event that begins
   with "Run" or "Create." This search is deprecated and have been translated to use
   the latest Change Datamodel. '
+search: '`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run*
+  OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time)
+  as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country
+  | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime)
+  as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country
+  | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime)
+  as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime
+  >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress]
+  | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table
+  _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter`'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs. This search works best when you run the "Previously Seen AWS Provisioning
   Activity Sources" support search once to create a history of previously seen locations
   that have provisioned AWS resources.
-id: 42e15012-ac14-4801-94f4-f1acbe64880b
 known_false_positives: "This is a strictly behavioral search, so we define \"false\
   \ positive\" slightly differently. Every time this fires, it will accurately reflect\
   \ the first occurrence in the time period you're searching within, plus what is\
@@ -21,20 +35,9 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal
   \ where the free version of **MaxMind GeoIP** that ships by default with Splunk\
   \ has weak resolution (particularly small countries in less economically powerful\
   \ regions), this may be much less valuable to you."
-name: AWS Cloud Provisioning From Previously Unseen IP Address
 references: []
-search: '`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run*
-  OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time)
-  as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country
-  | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime)
-  as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country
-  | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime)
-  as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime
-  >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress]
-  | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table
-  _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Suspicious Provisioning Activities
   asset_type: AWS Instance
   cis20:
@@ -46,5 +49,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_region.yml b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_region.yml
index c758004df9..ed27f7f87a 100644
--- a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_region.yml
+++ b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_region.yml
@@ -1,15 +1,29 @@
-author: David Dorsey, Splunk
+name: AWS Cloud Provisioning From Previously Unseen Region
+id: 7971d3df-da82-4648-a6e5-b5637bea5253
+version: 1
 date: '2018-03-16'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for AWS provisioning activities from previously unseen
   regions. Region in this context is similar to a state in the United States. Provisioning
   activities are defined broadly as any event that begins with "Run" or "Create."
   This search is deprecated and have been translated to use the latest Change Datamodel.
+search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
+  | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation
+  sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time)
+  as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv
+  | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress,
+  City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv
+  | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime
+  >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath
+  output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user,
+  src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter`'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs. This search works best when you run the "Previously Seen AWS Provisioning
   Activity Sources" support search once to create a history of previously seen locations
   that have provisioned AWS resources.
-id: 7971d3df-da82-4648-a6e5-b5637bea5253
 known_false_positives: "This is a strictly behavioral search, so we define \"false\
   \ positive\" slightly differently. Every time this fires, it will accurately reflect\
   \ the first occurrence in the time period you're searching within, plus what is\
@@ -21,20 +35,9 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal
   \ version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution\
   \ (particularly small countries in less economically powerful regions), this may\
   \ be much less valuable to you."
-name: AWS Cloud Provisioning From Previously Unseen Region
 references: []
-search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
-  | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation
-  sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time)
-  as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv
-  | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress,
-  City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv
-  | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime
-  >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath
-  output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user,
-  src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Suspicious Provisioning Activities
   asset_type: AWS Instance
   cis20:
@@ -48,5 +51,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/clients_connecting_to_multiple_dns_servers.yml b/detections/deprecated/clients_connecting_to_multiple_dns_servers.yml
index 6ca4149c10..20ba2b6cd7 100644
--- a/detections/deprecated/clients_connecting_to_multiple_dns_servers.yml
+++ b/detections/deprecated/clients_connecting_to_multiple_dns_servers.yml
@@ -1,7 +1,17 @@
-author: David Dorsey, Splunk
+name: Clients Connecting to Multiple DNS Servers
+id: 74ec6f18-604b-4202-a567-86b2066be3ce
+version: 3
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search allows you to identify the endpoints that have connected
   to more than five DNS servers and made DNS Queries over the time frame of the search.
+search: '| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest
+  dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY
+  by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 |
+  `clients_connecting_to_multiple_dns_servers_filter` '
 how_to_implement: 'This search requires that DNS data is being ingested and populating
   the `Network_Resolution` data model. This data can come from DNS logs or from solutions
   that parse network traffic for this data, such as Splunk Stream or Bro.\
@@ -15,16 +25,11 @@ how_to_implement: 'This search requires that DNS data is being ingested and popu
 
   Detailed documentation on how to create a new field within Incident Review may be
   found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-id: 74ec6f18-604b-4202-a567-86b2066be3ce
 known_false_positives: It's possible that an enterprise has more than five DNS servers
   that are configured in a round-robin rotation. Please customize the search, as appropriate.
-name: Clients Connecting to Multiple DNS Servers
-search: '| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest
-  dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY
-  by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 |
-  `clients_connecting_to_multiple_dns_servers_filter` '
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DNS Hijacking
   - Command and Control
   - Suspicious DNS Traffic
@@ -47,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/deprecated/cloud_network_access_control_list_deleted.yml b/detections/deprecated/cloud_network_access_control_list_deleted.yml
index 1b5ef45525..5dad492a18 100644
--- a/detections/deprecated/cloud_network_access_control_list_deleted.yml
+++ b/detections/deprecated/cloud_network_access_control_list_deleted.yml
@@ -1,25 +1,28 @@
-author: Peter Gael, Splunk
+name: Cloud Network Access Control List Deleted
+id: 021abc51-1862-41dd-ad43-43c739c0a983
+version: 1
 date: '2020-09-08'
+author: Peter Gael, Splunk
+type: batch
+datamodel: []
 description: Enforcing network-access controls is one of the defensive mechanisms
   used by cloud administrators to restrict access to a cloud instance. After the attacker
   has gained control of the console by compromising an admin account, they can delete
   a network ACL and gain access to the instance from anywhere. This search will query
   the Change datamodel to detect users deleting network ACLs. Deprecated because it's
   a duplicate
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
-  provider. You can also provide additional filtering for this search by customizing
-  the `cloud_network_access_control_list_deleted_filter` macro.
-id: 021abc51-1862-41dd-ad43-43c739c0a983
-known_false_positives: It's possible that a user has legitimately deleted a network
-  ACL.
-name: Cloud Network Access Control List Deleted
-references: []
 search: '`cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn  |
   stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage)
   values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName
   | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `cloud_network_access_control_list_deleted_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You can also provide additional filtering for this search by customizing
+  the `cloud_network_access_control_list_deleted_filter` macro.
+known_false_positives: It's possible that a user has legitimately deleted a network
+  ACL.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Network ACL Activity
   asset_type: Instance
   cis20:
@@ -34,5 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/deprecated/detect_api_activity_from_users_without_mfa.yml b/detections/deprecated/detect_api_activity_from_users_without_mfa.yml
index 8e548691b2..4946cd844b 100644
--- a/detections/deprecated/detect_api_activity_from_users_without_mfa.yml
+++ b/detections/deprecated/detect_api_activity_from_users_without_mfa.yml
@@ -1,11 +1,21 @@
-author: Bhavin Patel, Splunk
+name: Detect API activity from users without MFA
+id: 2a9b80d3-6340-4345-w5ad-212bf5d1dac4
+version: 1
 date: '2018-05-17'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where a user logged into the
   AWS account, is making API calls and has not enabled Multi Factor authentication.
   Multi factor authentication adds a layer of security by forcing the users to type
   a unique authentication code from an approved authentication device when they access
   AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged
   IAM users.
+search: '`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false
+  | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity
+  as user]| stats  count min(_time) as firstTime max(_time) as lastTime values(eventName)
+  as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)`  |
+  `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter`'
 how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs. Leverage the support search `Create a list of approved AWS service accounts`:
@@ -29,21 +39,14 @@ how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or lat
 
   Detailed documentation on how to create a new field within Incident Review may be
   found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-id: 2a9b80d3-6340-4345-w5ad-212bf5d1dac4
 known_false_positives: Many service accounts configured within an AWS infrastructure
   do not have multi factor authentication enabled. Please ignore the service accounts,
   if triggered and instead add them to the aws_service_accounts.csv file to fine tune
   the detection. It is also possible that the search detects users in your environment
   using Single Sign-On systems, since the MFA is not handled by AWS.
-name: Detect API activity from users without MFA
 references: []
-search: '`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false
-  | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity
-  as user]| stats  count min(_time) as firstTime max(_time) as lastTime values(eventName)
-  as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)`  |
-  `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   asset_type: AWS Instance
   cis20:
@@ -59,5 +62,3 @@ tags:
   risk_object_type: user
   risk_score: 10
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/deprecated/detect_aws_api_activities_from_unapproved_accounts.yml b/detections/deprecated/detect_aws_api_activities_from_unapproved_accounts.yml
index 8f1f358024..8fe8f9abfb 100644
--- a/detections/deprecated/detect_aws_api_activities_from_unapproved_accounts.yml
+++ b/detections/deprecated/detect_aws_api_activities_from_unapproved_accounts.yml
@@ -1,10 +1,21 @@
-author: Bhavin Patel, Splunk
+name: Detect AWS API Activities From Unapproved Accounts
+id: ada0f478-84a8-4641-a3f1-d82362d4bd55
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for successful CloudTrail activity by user accounts
   that are not listed in the identity table or `aws_service_accounts.csv`. It returns
   event names and count, as well as the first and last time a specific user or service
   is detected, grouped by users. Deprecated because managing this list can be quite
   hard.
+search: '`cloudtrail` errorCode=success | rename userName as identity | search NOT
+  [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup
+  aws_service_accounts | fields identity] | rename identity as user | stats count
+  min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by
+  user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_aws_api_activities_from_unapproved_accounts_filter`'
 how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs. You must also populate the `identity_lookup_expanded` lookup shipped with
@@ -30,21 +41,13 @@ how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or lat
 
   Detailed documentation on how to create a new field within Incident Review may be
   found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-id: ada0f478-84a8-4641-a3f1-d82362d4bd55
 known_false_positives: It's likely that you'll find activity detected by users/service
   accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv`
   file. If the user is a legitimate service account, update the `aws_service_accounts.csv`
   table with that entry.
-name: Detect AWS API Activities From Unapproved Accounts
 references: []
-search: '`cloudtrail` errorCode=success | rename userName as identity | search NOT
-  [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup
-  aws_service_accounts | fields identity] | rename identity as user | stats count
-  min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by
-  user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `detect_aws_api_activities_from_unapproved_accounts_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   asset_type: AWS Instance
   cis20:
@@ -66,5 +69,3 @@ tags:
   risk_object_type: user
   risk_score: 15
   security_domain: access
-type: ESCU
-version: 2
diff --git a/detections/deprecated/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml b/detections/deprecated/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml
index 39ff87a6e7..5119da220a 100644
--- a/detections/deprecated/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml
+++ b/detections/deprecated/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml
@@ -1,7 +1,23 @@
-author: Bhavin Patel, Splunk
+name: Detect DNS requests to Phishing Sites leveraging EvilGinx2
+id: 24dd17b1-e2fb-4c31-878c-d4f226595bfa
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search looks for DNS requests for phishing domains that are leveraging
   EvilGinx tools to mimic websites.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by
+  DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$"
+  | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon`
+  OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365`
+  OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google`
+  | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer
+  [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web
+  by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$"
+  | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter`'
 how_to_implement: "You need to ingest data from your DNS logs in the Network_Resolution\
   \ datamodel. Specifically you must ingest the domain that is being queried and the\
   \ IP of the host originating the request. Ideally, you should also be ingesting\
@@ -16,23 +32,12 @@ how_to_implement: "You need to ingest data from your DNS logs in the Network_Res
   \ Actions when configuring this detection search, and set the corresponding Playbook\
   \ to active. \\\n(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\\\
   \n"
-id: 24dd17b1-e2fb-4c31-878c-d4f226595bfa
 known_false_positives: If a known good domain is not listed in the legit_domains.csv
   file, then the search could give you false postives. Please update that lookup file
   to filter out DNS requests to legitimate domains.
-name: Detect DNS requests to Phishing Sites leveraging EvilGinx2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by
-  DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$"
-  | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon`
-  OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365`
-  OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google`
-  | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer
-  [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web
-  by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$"
-  | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter`'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Common Phishing Frameworks
   asset_type: Endpoint
   cis20:
@@ -54,5 +59,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/detect_long_dns_txt_record_response.yml b/detections/deprecated/detect_long_dns_txt_record_response.yml
index d2fe54711b..a8027e9689 100644
--- a/detections/deprecated/detect_long_dns_txt_record_response.yml
+++ b/detections/deprecated/detect_long_dns_txt_record_response.yml
@@ -1,21 +1,16 @@
-author: Rico Valdez, Splunk
+name: Detect Long DNS TXT Record Response
+id: 05437c07-62f5-452e-afdc-04dd44815bb9
+version: 2
 date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search is used to detect attempts to use DNS tunneling, by calculating
   the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission
   for data exfiltration, command and control, or evasion of security controls can
   often be detected by noting unusually large volumes of DNS traffic. Deprecated because
   this detection should focus on DNS queries instead of DNS responses.
-how_to_implement: To successfully implement this search you need to ingest data from
-  your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically,
-  this query requires that the DNS data model is populated with information regarding
-  the DNS record type that is being returned as well as the data in the answer section
-  of the protocol.
-id: 05437c07-62f5-452e-afdc-04dd44815bb9
-known_false_positives: It's possible that legitimate TXT record responses can be long
-  enough to trigger this search. You can modify the packet threshold for this search
-  to help mitigate false positives.
-name: Detect Long DNS TXT Record Response
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND
   DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type |  `drop_dm_object_name("DNS")`
@@ -25,8 +20,17 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source
   IP" "Destination IP" "DNS Answer" "DNS Record Type"  "Answer Length" Count "First
   Time" "Last Time" | `detect_long_dns_txt_record_response_filter`'
+how_to_implement: To successfully implement this search you need to ingest data from
+  your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically,
+  this query requires that the DNS data model is populated with information regarding
+  the DNS record type that is being returned as well as the data in the answer section
+  of the protocol.
+known_false_positives: It's possible that legitimate TXT record responses can be long
+  enough to trigger this search. You can modify the packet threshold for this search
+  to help mitigate false positives.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious DNS Traffic
   - Command and Control
   asset_type: Endpoint
@@ -48,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/detect_mimikatz_using_loaded_images.yml b/detections/deprecated/detect_mimikatz_using_loaded_images.yml
index 64df524f99..65b460710f 100644
--- a/detections/deprecated/detect_mimikatz_using_loaded_images.yml
+++ b/detections/deprecated/detect_mimikatz_using_loaded_images.yml
@@ -1,41 +1,41 @@
-author: Patrick Bareiss, Splunk
+name: Detect Mimikatz Using Loaded Images
+id: 29e307ba-40af-4ab2-91b2-3c6b392bbba0
+version: 1
 date: '2019-12-03'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search looks for reading loaded Images unique to credential dumping
   with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon
   Event Code.
+search: '`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId)
+  as ProcessId by Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll
+  ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename
+  Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `detect_mimikatz_using_loaded_images_filter`'
 how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which
   includes EventCode 7 with powershell.exe. This search uses an input macro named
   `sysmon`. We strongly recommend that you specify your environment-specific configurations
   (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
   with configurations for your Splunk environment. The search also uses a post-filter
   macro designed to filter out known false positives.
-id: 29e307ba-40af-4ab2-91b2-3c6b392bbba0
-known_false_positives: Other tools can import the same DLLs. These tools should be
-  part of a whtelist.
-name: Detect Mimikatz Using Loaded Images
-references:
-- https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html
-search: '`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId)
-  as ProcessId by Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll
-  ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename
-  Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `detect_mimikatz_using_loaded_images_filter`'
-
 known_false_positives: Other tools can import the same DLLs. These tools should be
   part of a whitelist.
+references:
+- https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   - Detect Zerologon Attack
   - Cloud Federated Credential Abuse
-  mitre_attack_id:
-  - T1003.001
-  kill_chain_phases:
-  - Actions on Objectives
   asset_type: Windows
   cis20:
   - CIS 6
   - CIS 8
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1003.001
   nist:
   - DE.AE
   - DE.CM
@@ -44,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/detect_mimikatz_via_powershell_and_eventcode_4703.yml b/detections/deprecated/detect_mimikatz_via_powershell_and_eventcode_4703.yml
index 7a7e9c5c62..2311ab170e 100644
--- a/detections/deprecated/detect_mimikatz_via_powershell_and_eventcode_4703.yml
+++ b/detections/deprecated/detect_mimikatz_via_powershell_and_eventcode_4703.yml
@@ -1,7 +1,18 @@
-author: Rico Valdez, Splunk
+name: Detect Mimikatz Via PowerShell And EventCode 4703
+id: 98917be2-bfc8-475a-8618-a9bb06575188
+version: 2
 date: '2019-02-27'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search looks for PowerShell requesting privileges consistent with
   credential dumping. Deprecated, looks like things changed from a logging perspective.
+search: '`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex
+  field=Message "Enabled Privileges:\s+(?<privs>\w+)\s+Disabled Privileges:" | where
+  privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime
+  by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege"
+  | rename Process_Name as process |  `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `detect_mimikatz_via_powershell_and_eventcode_4703_filter`'
 how_to_implement: 'You must be ingesting Windows Security logs. You must also enable
   the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata.
   Additionally, this search requires you to enable your Group Management Audit Logs
@@ -9,21 +20,13 @@ how_to_implement: 'You must be ingesting Windows Security logs. You must also en
   on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/.
   Finally, please make sure that the local administrator group name is "Administrators"
   to be able to look for the right group membership changes.'
-id: 98917be2-bfc8-475a-8618-a9bb06575188
 known_false_positives: The activity may be legitimate. PowerShell is often used by
   administrators to perform various tasks, and it's possible this event could be generated
   in those cases. In these cases, false positives should be fairly obvious and you
   may need to tweak the search to eliminate noise.
-name: Detect Mimikatz Via PowerShell And EventCode 4703
 references: []
-search: '`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex
-  field=Message "Enabled Privileges:\s+(?<privs>\w+)\s+Disabled Privileges:" | where
-  privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime
-  by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege"
-  | rename Process_Name as process |  `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `detect_mimikatz_via_powershell_and_eventcode_4703_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Federated Credential Abuse
   asset_type: Windows
   cis20:
@@ -43,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 2
diff --git a/detections/deprecated/detect_new_api_calls_from_user_roles.yml b/detections/deprecated/detect_new_api_calls_from_user_roles.yml
index 6d1f28ffa0..6fb8d2bae1 100644
--- a/detections/deprecated/detect_new_api_calls_from_user_roles.yml
+++ b/detections/deprecated/detect_new_api_calls_from_user_roles.yml
@@ -1,18 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Detect new API calls from user roles
+id: 22773e84-bac0-4595-b086-20d3f335b4f1
+version: 1
 date: '2018-04-16'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search detects new API calls that have either never been seen before
   or that have not been seen in the previous hour, where the identity type is `AssumedRole`.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. This search works best when you run the "Previously seen API call per user
-  roles in CloudTrail" support search once to create a history of previously seen
-  user roles.
-id: 22773e84-bac0-4595-b086-20d3f335b4f1
-known_false_positives: It is possible that there are legitimate user roles making
-  new or infrequently used API calls in your infrastructure, causing the search to
-  trigger.
-name: Detect new API calls from user roles
-references: []
 search: '`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole
   [search `cloudtrail` eventType=AwsApiCall errorCode=success  userIdentity.type=AssumedRole
   | stats earliest(_time) as earliest latest(_time) as latest by userName eventName
@@ -23,8 +17,17 @@ search: '`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=A
   | table eventName userName]  |rename userName as user| stats values(eventName) earliest(_time)
   as earliest latest(_time) as latest by user | `security_content_ctime(earliest)`
   | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. This search works best when you run the "Previously seen API call per user
+  roles in CloudTrail" support search once to create a history of previously seen
+  user roles.
+known_false_positives: It is possible that there are legitimate user roles making
+  new or infrequently used API calls in your infrastructure, causing the search to
+  trigger.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   asset_type: AWS Instance
   cis20:
@@ -41,5 +44,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/detect_new_user_aws_console_login.yml b/detections/deprecated/detect_new_user_aws_console_login.yml
index 72fa3054a5..d3b28f5d3b 100644
--- a/detections/deprecated/detect_new_user_aws_console_login.yml
+++ b/detections/deprecated/detect_new_user_aws_console_login.yml
@@ -1,29 +1,33 @@
-author: Bhavin Patel, Splunk
+name: Detect new user AWS Console Login
+id: ada0f478-84a8-4641-a3f3-d82362dffd75
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events wherein a console login event
   by a user was recorded within the last hour, then compares the event to a lookup
   file of previously seen users (by ARN values) who have logged into the console.
   The alert is fired if the user has logged into the console for the first time within
   the last hour. Deprecated now this search is updated to use the Authentication datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. Run the "Previously seen users in CloudTrail" support search only once to
-  create a baseline of previously seen IAM users within the last 30 days. Run "Update
-  previously seen users in CloudTrail" hourly (or more frequently depending on how
-  often you run the detection searches) to refresh the baselines.
-id: ada0f478-84a8-4641-a3f3-d82362dffd75
-known_false_positives: When a legitimate new user logins for the first time, this
-  activity will be detected. Check how old the account is and verify that the user
-  activity is legitimate.
-name: Detect new user AWS Console Login
 search: '`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats
   earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t
   previously_seen_users_console_logins_cloudtrail  | stats min(firstTime) as firstTime
   max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(),
   "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`|
   where userStatus ="First Time Logging into AWS Console"  | `detect_new_user_aws_console_login_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. Run the "Previously seen users in CloudTrail" support search only once to
+  create a baseline of previously seen IAM users within the last 30 days. Run "Update
+  previously seen users in CloudTrail" hourly (or more frequently depending on how
+  often you run the detection searches) to refresh the baselines.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS Login Activities
   asset_type: AWS Instance
   cis20:
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/detect_spike_in_aws_api_activity.yml b/detections/deprecated/detect_spike_in_aws_api_activity.yml
index a0be04286b..963b8cf9df 100644
--- a/detections/deprecated/detect_spike_in_aws_api_activity.yml
+++ b/detections/deprecated/detect_spike_in_aws_api_activity.yml
@@ -1,9 +1,28 @@
-author: David Dorsey, Splunk
+name: Detect Spike in AWS API Activity
+id: ada0f478-84a8-4641-a3f1-d32362d4bd55
+version: 2
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search will detect users creating spikes of API activity in your
   AWS environment.  It will also update the cache file that factors in the latest
   data. This search is deprecated and have been translated to use the latest Change
   Datamodel.
+search: '`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall
+  | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup
+  api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as *
+  by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720
+  | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720))
+  | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls,
+  stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1)
+  | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup
+  api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3
+  | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND
+  numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn
+  | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName)
+  as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user
+  | `detect_spike_in_aws_api_activity_filter`'
 how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
@@ -30,26 +49,10 @@ how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or lat
 
   Detailed documentation on how to create a new field within Incident Review may be
   found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-id: ada0f478-84a8-4641-a3f1-d32362d4bd55
 known_false_positives: ''
-name: Detect Spike in AWS API Activity
 references: []
-search: '`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall
-  | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup
-  api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as *
-  by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720
-  | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720))
-  | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls,
-  stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1)
-  | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup
-  api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3
-  | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND
-  numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn
-  | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName)
-  as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user
-  | `detect_spike_in_aws_api_activity_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   asset_type: AWS Instance
   cis20:
@@ -70,5 +73,3 @@ tags:
   risk_object_type: user
   risk_score: 10
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/detect_spike_in_network_acl_activity.yml b/detections/deprecated/detect_spike_in_network_acl_activity.yml
index 5c0109d25e..5af6a6f8a9 100644
--- a/detections/deprecated/detect_spike_in_network_acl_activity.yml
+++ b/detections/deprecated/detect_spike_in_network_acl_activity.yml
@@ -1,23 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Detect Spike in Network ACL Activity
+id: ada0f478-84a8-4641-a1f1-e32372d4bd53
+version: 1
 date: '2018-05-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search will detect users creating spikes in API activity related
   to network access-control lists (ACLs)in your AWS environment. This search is deprecated
   and have been translated to use the latest Change Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
-  your environment. The `dataPointThreshold` variable is the minimum number of data
-  points required to have a statistically significant amount of data to determine.
-  The `deviationThreshold` variable is the number of standard deviations away from
-  the mean that the value must be to be considered a spike. This search works best
-  when you run the "Baseline of Network ACL Activity by ARN" support search once to
-  create a lookup file of previously seen Network ACL Activity. To add or remove API
-  event names related to network ACLs, edit the macro `network_acl_events`.
-id: ada0f478-84a8-4641-a1f1-e32372d4bd53
-known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold`
-  and `deviationThreshold`. Please modify this according the your environment.
-name: Detect Spike in Network ACL Activity
-references: []
 search: '`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events`
   | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup
   network_acl_activity_baseline append=t | fields - latestCount | stats values(*)
@@ -32,8 +22,21 @@ search: '`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_eve
   userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn
   | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName)
   as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
+  your environment. The `dataPointThreshold` variable is the minimum number of data
+  points required to have a statistically significant amount of data to determine.
+  The `deviationThreshold` variable is the number of standard deviations away from
+  the mean that the value must be to be considered a spike. This search works best
+  when you run the "Baseline of Network ACL Activity by ARN" support search once to
+  create a lookup file of previously seen Network ACL Activity. To add or remove API
+  event names related to network ACLs, edit the macro `network_acl_events`.
+known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold`
+  and `deviationThreshold`. Please modify this according the your environment.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   asset_type: AWS Instance
   cis20:
@@ -55,5 +58,3 @@ tags:
   risk_object_type: user
   risk_score: 20
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/deprecated/detect_spike_in_security_group_activity.yml b/detections/deprecated/detect_spike_in_security_group_activity.yml
index e34a5d9ac0..5ef191c32c 100644
--- a/detections/deprecated/detect_spike_in_security_group_activity.yml
+++ b/detections/deprecated/detect_spike_in_security_group_activity.yml
@@ -1,24 +1,14 @@
-author: Bhavin Patel, Splunk
+name: Detect Spike in Security Group Activity
+id: ada0f478-84a8-4641-a3f1-e32372d4bd53
+version: 1
 date: '2018-04-18'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search will detect users creating spikes in API activity related
   to security groups in your AWS environment.  It will also update the cache file
   that factors in the latest data.  This search is deprecated and have been translated
   to use the latest Change Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
-  your environment. The `dataPointThreshold` variable is the minimum number of data
-  points required to have a statistically significant amount of data to determine.
-  The `deviationThreshold` variable is the number of standard deviations away from
-  the mean that the value must be to be considered a spike.This search works best
-  when you run the "Baseline of Security Group Activity by ARN" support search once
-  to create a history of previously seen Security Group Activity. To add or remove
-  API event names for security groups, edit the macro `security_group_api_calls`.
-id: ada0f478-84a8-4641-a3f1-e32372d4bd53
-known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`,
-  the false positive rate may vary. Please modify this according the your environment.
-name: Detect Spike in Security Group Activity
-references: []
 search: '`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls`
   | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup
   security_group_activity_baseline append=t | fields - latestCount | stats values(*)
@@ -33,8 +23,21 @@ search: '`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_
   userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn
   | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName)
   as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
+  your environment. The `dataPointThreshold` variable is the minimum number of data
+  points required to have a statistically significant amount of data to determine.
+  The `deviationThreshold` variable is the number of standard deviations away from
+  the mean that the value must be to be considered a spike.This search works best
+  when you run the "Baseline of Security Group Activity by ARN" support search once
+  to create a history of previously seen Security Group Activity. To add or remove
+  API event names for security groups, edit the macro `security_group_api_calls`.
+known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`,
+  the false positive rate may vary. Please modify this according the your environment.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   asset_type: AWS Instance
   cis20:
@@ -55,5 +58,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/deprecated/detect_usb_device_insertion.yml b/detections/deprecated/detect_usb_device_insertion.yml
index 0145ee0fc4..de91cb7658 100644
--- a/detections/deprecated/detect_usb_device_insertion.yml
+++ b/detections/deprecated/detect_usb_device_insertion.yml
@@ -1,29 +1,33 @@
-author: Bhavin Patel, Splunk
+name: Detect USB device insertion
+id: 104658f4-afdc-499f-9719-17a43f9826f5
+version: 1
 date: '2017-11-27'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Change_Analysis
 description: The search is used to detect hosts that generate Windows Event ID 4663
   for successful attempts to write to or read from a removable storage and Event ID
   4656 for failures, which occurs when a USB drive is plugged in. In this scenario
   we are querying the Change_Analysis data model to look for Windows Event ID 4656
   or 4663 where the priority of the affected host is marked as high in the ES Assets
   and Identity Framework.
+search: '| tstats `security_content_summariesonly` count earliest(_time) AS earliest
+  latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes)
+  All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656)
+  (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`|
+  `security_content_ctime(earliest)`| `security_content_ctime(latest)`  | `detect_usb_device_insertion_filter`'
 how_to_implement: To successfully implement this search, you must ingest Windows Security
   Event logs and track event code 4663 and 4656. Ensure that the field from the event
   logs is being mapped to the result_id field in the Change_Analysis data model. To
   minimize the alert volume, this search leverages the Assets and Identity framework
   to filter out events from those assets not marked high priority in the Enterprise
   Security Assets and Identity Framework.
-id: 104658f4-afdc-499f-9719-17a43f9826f5
 known_false_positives: Legitimate USB activity will also be detected. Please verify
   and investigate as appropriate.
-name: Detect USB device insertion
 references: []
-search: '| tstats `security_content_summariesonly` count earliest(_time) AS earliest
-  latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes)
-  All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656)
-  (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`|
-  `security_content_ctime(earliest)`| `security_content_ctime(latest)`  | `detect_usb_device_insertion_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Data Protection
   asset_type: Endpoint
   cis20:
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/detect_web_traffic_to_dynamic_domain_providers.yml b/detections/deprecated/detect_web_traffic_to_dynamic_domain_providers.yml
index 8907b28613..6159f55813 100644
--- a/detections/deprecated/detect_web_traffic_to_dynamic_domain_providers.yml
+++ b/detections/deprecated/detect_web_traffic_to_dynamic_domain_providers.yml
@@ -1,6 +1,16 @@
-author: Bhavin Patel, Splunk
+name: Detect web traffic to dynamic domain providers
+id: 134da869-e264-4a8f-8d7e-fcd01c18f301
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Web
 description: This search looks for web connections to dynamic DNS providers.
+search: '| tstats `security_content_summariesonly` count values(Web.url) as url min(_time)
+  as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic`
+  | `detect_web_traffic_to_dynamic_domain_providers_filter`'
 how_to_implement: 'This search requires you to be ingesting web-traffic logs. You
   can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis
   tool, such as Bro or Splunk Stream. The web data model must contain the URL being
@@ -19,17 +29,11 @@ how_to_implement: 'This search requires you to be ingesting web-traffic logs. Yo
   Detailed documentation on how to create a new field within Incident Review may be
   found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
   Deprecated because duplicate.'
-id: 134da869-e264-4a8f-8d7e-fcd01c18f301
 known_false_positives: It is possible that list of dynamic DNS providers is outdated
   and/or that the URL being requested is legitimate.
-name: Detect web traffic to dynamic domain providers
 references: []
-search: '| tstats `security_content_summariesonly` count values(Web.url) as url min(_time)
-  as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status
-  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic`
-  | `detect_web_traffic_to_dynamic_domain_providers_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Dynamic DNS
   asset_type: Endpoint
   cis20:
@@ -48,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/detection_of_dns_tunnels.yml b/detections/deprecated/detection_of_dns_tunnels.yml
index e6a6f48f65..026ff56776 100644
--- a/detections/deprecated/detection_of_dns_tunnels.yml
+++ b/detections/deprecated/detection_of_dns_tunnels.yml
@@ -1,5 +1,11 @@
-author: Bhavin Patel, Splunk
+name: Detection of DNS Tunnels
+id: 104658f4-afdc-499f-9719-17a43f9826f4
+version: 2
 date: '2017-09-18'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search is used to detect DNS tunneling, by calculating the sum of
   the length of DNS queries and DNS answers. The search also filters out potential
   false positives by filtering out queries made to internal systems and the queries
@@ -7,20 +13,6 @@ description: This search is used to detect DNS tunneling, by calculating the sum
   method of transmission for data exfiltration, command and control, or evasion of
   security controls can often be detected by noting an unusually large volume of DNS
   traffic. Deprecated because existing detection is doing the same.
-how_to_implement: To successfully implement this search, we must ensure that DNS data
-  is being ingested and mapped to the appropriate fields in the Network_Resolution
-  data model. Fields like src_category are automatically provided by the Assets and
-  Identity Framework shipped with Splunk Enterprise Security. You will need to ensure
-  you are using the Assets and Identity Framework and populating the src_category
-  field. You will also need to enable the `cim_corporate_web_domain_search()` macro
-  which will essentially filter out the DNS queries made to the corporate web domains
-  to reduce alert fatigue.
-id: 104658f4-afdc-499f-9719-17a43f9826f4
-known_false_positives: It's possible that normal DNS traffic will exhibit this behavior.
-  If an alert is generated, please investigate and validate as appropriate. The threshold
-  can also be modified to better suit your environment.
-name: Detection of DNS Tunnels
-references: []
 search: '| tstats `security_content_summariesonly` dc("DNS.query") as count  from
   datamodel=Network_Resolution  where nodename=DNS "DNS.message_type"="QUERY" NOT
   (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa"
@@ -35,8 +27,20 @@ search: '| tstats `security_content_summariesonly` dc("DNS.query") as count  fro
   "DNS.src" as src  "DNS.answer" as message | eval message=if(message=="unknown","",
   message) | eval length=len(message) | stats sum(length) as length by src ] | stats
   sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter`'
+how_to_implement: To successfully implement this search, we must ensure that DNS data
+  is being ingested and mapped to the appropriate fields in the Network_Resolution
+  data model. Fields like src_category are automatically provided by the Assets and
+  Identity Framework shipped with Splunk Enterprise Security. You will need to ensure
+  you are using the Assets and Identity Framework and populating the src_category
+  field. You will also need to enable the `cim_corporate_web_domain_search()` macro
+  which will essentially filter out the DNS queries made to the corporate web domains
+  to reduce alert fatigue.
+known_false_positives: It's possible that normal DNS traffic will exhibit this behavior.
+  If an alert is generated, please investigate and validate as appropriate. The threshold
+  can also be modified to better suit your environment.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Data Protection
   - Suspicious DNS Traffic
   - Command and Control
@@ -56,5 +60,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/deprecated/dns_query_requests_resolved_by_unauthorized_dns_servers.yml b/detections/deprecated/dns_query_requests_resolved_by_unauthorized_dns_servers.yml
index 6da03bb551..f469b44e0f 100644
--- a/detections/deprecated/dns_query_requests_resolved_by_unauthorized_dns_servers.yml
+++ b/detections/deprecated/dns_query_requests_resolved_by_unauthorized_dns_servers.yml
@@ -1,21 +1,26 @@
-author: Bhavin Patel, Splunk
+name: DNS Query Requests Resolved by Unauthorized DNS Servers
+id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f6
+version: 3
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search will detect DNS requests resolved by unauthorized DNS servers.
   Legitimate DNS servers should be identified in the Enterprise Security Assets and
   Identity Framework.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
+  where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src
+  DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` '
 how_to_implement: To successfully implement this search you will need to ensure that
   DNS data is populating the Network_Resolution data model. It also requires that
   your DNS servers are identified correctly in the Assets and Identity table of Enterprise
   Security.
-id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f6
 known_false_positives: Legitimate DNS activity can be detected in this search. Investigate,
   verify and update the list of authorized DNS servers as appropriate.
-name: DNS Query Requests Resolved by Unauthorized DNS Servers
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
-  where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src
-  DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` '
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DNS Hijacking
   - Command and Control
   - Suspicious DNS Traffic
@@ -41,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/deprecated/ec2_instance_modified_with_previously_unseen_user.yml b/detections/deprecated/ec2_instance_modified_with_previously_unseen_user.yml
index 031e273b88..007b8365b1 100644
--- a/detections/deprecated/ec2_instance_modified_with_previously_unseen_user.yml
+++ b/detections/deprecated/ec2_instance_modified_with_previously_unseen_user.yml
@@ -1,19 +1,13 @@
-author: David Dorsey, Splunk
+name: EC2 Instance Modified With Previously Unseen User
+id: 56f91724-cf3f-4666-84e1-e3712fb41e76
+version: 3
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for EC2 instances being modified by users who have
   not previously modified them. This search is deprecated and have been translated
   to use the latest Change Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. This search works best when you run the "Previously Seen EC2 Launches By
-  User" support search once to create a history of previously seen ARNs. To add or
-  remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
-id: 56f91724-cf3f-4666-84e1-e3712fb41e76
-known_false_positives: It's possible that a new user will start to modify EC2 instances
-  when they haven't before for any number of reasons. Verify with the user that is
-  modifying instances that this is the intended behavior.
-name: EC2 Instance Modified With Previously Unseen User
-references: []
 search: '`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls`
   errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime
   by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user
@@ -22,8 +16,17 @@ search: '`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_mod
   "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId
   | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. This search works best when you run the "Previously Seen EC2 Launches By
+  User" support search once to create a history of previously seen ARNs. To add or
+  remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
+known_false_positives: It's possible that a new user will start to modify EC2 instances
+  when they haven't before for any number of reasons. Verify with the user that is
+  modifying instances that this is the intended behavior.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Unusual AWS EC2 Modifications
   asset_type: AWS Instance
   cis20:
@@ -40,5 +43,3 @@ tags:
   risk_object_type: user
   risk_score: 5
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/deprecated/ec2_instance_started_in_previously_unseen_region.yml b/detections/deprecated/ec2_instance_started_in_previously_unseen_region.yml
index 3716eb9fa8..650c4b7ded 100644
--- a/detections/deprecated/ec2_instance_started_in_previously_unseen_region.yml
+++ b/detections/deprecated/ec2_instance_started_in_previously_unseen_region.yml
@@ -1,18 +1,13 @@
-author: Bhavin Patel, Splunk
+name: EC2 Instance Started In Previously Unseen Region
+id: ada0f478-84a8-4641-a3f3-d82362d6fd75
+version: 1
 date: '2018-02-23'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for CloudTrail events where an instance is started
   in a particular region in the last one hour and then compares it to a lookup file
   of previously seen regions where an instance was started
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. Run the "Previously seen AWS Regions" support search only once to create
-  of baseline of previously seen regions.  This search is deprecated and have been
-  translated to use the latest Change Datamodel.
-id: ada0f478-84a8-4641-a3f3-d82362d6fd75
-known_false_positives: It's possible that a user has unknowingly started an instance
-  in a new region. Please verify that this activity is legitimate.
-name: EC2 Instance Started In Previously Unseen Region
-references: []
 search: '`cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest
   latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv
   | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup
@@ -20,8 +15,16 @@ search: '`cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as ear
   "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)`
   | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New
   Region" | `ec2_instance_started_in_previously_unseen_region_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. Run the "Previously seen AWS Regions" support search only once to create
+  of baseline of previously seen regions.  This search is deprecated and have been
+  translated to use the latest Change Datamodel.
+known_false_positives: It's possible that a user has unknowingly started an instance
+  in a new region. Please verify that this activity is legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Suspicious AWS EC2 Activities
   asset_type: AWS Instance
@@ -42,5 +45,3 @@ tags:
   risk_object_type: system
   risk_score: 10
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/deprecated/ec2_instance_started_with_previously_unseen_ami.yml b/detections/deprecated/ec2_instance_started_with_previously_unseen_ami.yml
index 0400f1408c..f3f035632a 100644
--- a/detections/deprecated/ec2_instance_started_with_previously_unseen_ami.yml
+++ b/detections/deprecated/ec2_instance_started_with_previously_unseen_ami.yml
@@ -1,18 +1,13 @@
-author: David Dorsey, Splunk
+name: EC2 Instance Started With Previously Unseen AMI
+id: 347ec301-601b-48b9-81aa-9ddf9c829dd3
+version: 1
 date: '2018-03-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for EC2 instances being created with previously unseen
   AMIs.  This search is deprecated and have been translated to use the latest Change
   Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. This search works best when you run the "Previously Seen EC2 AMIs" support
-  search once to create a history of previously seen AMIs.
-id: 347ec301-601b-48b9-81aa-9ddf9c829dd3
-known_false_positives: After a new AMI is created, the first systems created with
-  that AMI will cause this alert to fire.  Verify that the AMI being used was created
-  by a legitimate user.
-name: EC2 Instance Started With Previously Unseen AMI
-references: []
 search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances
   errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime
   by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId
@@ -24,8 +19,16 @@ search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunI
   as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn
   as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime,
   lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. This search works best when you run the "Previously Seen EC2 AMIs" support
+  search once to create a history of previously seen AMIs.
+known_false_positives: After a new AMI is created, the first systems created with
+  that AMI will cause this alert to fire.  Verify that the AMI being used was created
+  by a legitimate user.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   asset_type: AWS Instance
   cis20:
@@ -37,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/ec2_instance_started_with_previously_unseen_instance_type.yml b/detections/deprecated/ec2_instance_started_with_previously_unseen_instance_type.yml
index 405e5c552a..96f2384d72 100644
--- a/detections/deprecated/ec2_instance_started_with_previously_unseen_instance_type.yml
+++ b/detections/deprecated/ec2_instance_started_with_previously_unseen_instance_type.yml
@@ -1,18 +1,13 @@
-author: David Dorsey, Splunk
+name: EC2 Instance Started With Previously Unseen Instance Type
+id: 65541c80-03c7-4e05-83c8-1dcd57a2e1ad
+version: 2
 date: '2020-02-07'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for EC2 instances being created with previously unseen
   instance types.  This search is deprecated and have been translated to use the latest
   Change Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. This search works best when you run the "Previously Seen EC2 Instance Types"
-  support search once to create a history of previously seen instance types.
-id: 65541c80-03c7-4e05-83c8-1dcd57a2e1ad
-known_false_positives: It is possible that an admin will create a new system using
-  a new instance type never used before. Verify with the creator that they intended
-  to create the system with the new instance type.
-name: EC2 Instance Started With Previously Unseen Instance Type
-references: []
 search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances
   errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats
   earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType
@@ -24,8 +19,16 @@ search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunI
   requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType
   as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table
   _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. This search works best when you run the "Previously Seen EC2 Instance Types"
+  support search once to create a history of previously seen instance types.
+known_false_positives: It is possible that an admin will create a new system using
+  a new instance type never used before. Verify with the creator that they intended
+  to create the system with the new instance type.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   asset_type: AWS Instance
   cis20:
@@ -37,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/deprecated/ec2_instance_started_with_previously_unseen_user.yml b/detections/deprecated/ec2_instance_started_with_previously_unseen_user.yml
index ea6efe265a..9174162343 100644
--- a/detections/deprecated/ec2_instance_started_with_previously_unseen_user.yml
+++ b/detections/deprecated/ec2_instance_started_with_previously_unseen_user.yml
@@ -1,18 +1,13 @@
-author: David Dorsey, Splunk
+name: EC2 Instance Started With Previously Unseen User
+id: 22773e84-bac0-4595-b086-20d3f735b4f1
+version: 2
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for EC2 instances being created by users who have not
   created them before. This search is deprecated and have been translated to use the
   latest Change Datamodel.
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs. This search works best when you run the "Previously Seen EC2 Launches By
-  User" support search once to create a history of previously seen ARNs.
-id: 22773e84-bac0-4595-b086-20d3f735b4f1
-known_false_positives: It's possible that a user will start to create EC2 instances
-  when they haven't before for any number of reasons. Verify with the user that is
-  launching instances that this is the intended behavior.
-name: EC2 Instance Started With Previously Unseen User
-references: []
 search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances
   errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime
   by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv
@@ -22,8 +17,16 @@ search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunI
   | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType
   as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn
   as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
+  inputs. This search works best when you run the "Previously Seen EC2 Launches By
+  User" support search once to create a history of previously seen ARNs.
+known_false_positives: It's possible that a user will start to create EC2 instances
+  when they haven't before for any number of reasons. Verify with the user that is
+  launching instances that this is the intended behavior.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Suspicious AWS EC2 Activities
   asset_type: AWS Instance
@@ -38,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/deprecated/execution_of_file_with_spaces_before_extension.yml b/detections/deprecated/execution_of_file_with_spaces_before_extension.yml
index d07ad69e3d..e55b5629a0 100644
--- a/detections/deprecated/execution_of_file_with_spaces_before_extension.yml
+++ b/detections/deprecated/execution_of_file_with_spaces_before_extension.yml
@@ -1,23 +1,27 @@
-author: Rico Valdez, Splunk
+name: Execution of File With Spaces Before Extension
+id: ab0353e6-a956-420b-b724-a8b4846d5d5a
+version: 3
 date: '2020-11-19'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for processes launched from files with at least five
   spaces in the name before the extension. This is typically done to obfuscate the
   file extension by pushing it outside of the default view.
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records process activity from your hosts to populate the endpoint data model
-  in the processes node. If you are using Sysmon, you must have at least version 6.0.4
-  of the Sysmon TA.
-id: ab0353e6-a956-420b-b724-a8b4846d5d5a
-known_false_positives: None identified.
-name: Execution of File With Spaces Before Extension
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process_path)
   as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process = "*     .*" by Processes.dest Processes.user Processes.process
   Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records process activity from your hosts to populate the endpoint data model
+  in the processes node. If you are using Sysmon, you must have at least version 6.0.4
+  of the Sysmon TA.
+known_false_positives: None identified.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows File Extension and Association Abuse
   asset_type: Endpoint
   cis20:
@@ -36,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/deprecated/extended_period_without_successful_netbackup_backups.yml b/detections/deprecated/extended_period_without_successful_netbackup_backups.yml
index 911df36f5c..f6f4bd3b06 100644
--- a/detections/deprecated/extended_period_without_successful_netbackup_backups.yml
+++ b/detections/deprecated/extended_period_without_successful_netbackup_backups.yml
@@ -1,23 +1,26 @@
-author: David Dorsey, Splunk
+name: Extended Period Without Successful Netbackup Backups
+id: a34aae96-ccf8-4aef-952c-3ea214444440
+version: 1
 date: '2017-09-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search returns a list of hosts that have not successfully completed
   a backup in over a week. Deprecated because it's a infrastructure monitoring.
+search: '`netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats
+  latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)`
+  | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(),
+  "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter`'
 how_to_implement: To successfully implement this search you need to first obtain data
   from your backup solution, either from the backup logs on your hosts, or from a
   central server responsible for performing the backups. If you do not use Netbackup,
   you can modify this search for your backup solution. Depending on how often you
   backup your systems, you may want to modify how far in the past to look for a successful
   backup, other than the default of seven days.
-id: a34aae96-ccf8-4aef-952c-3ea214444440
 known_false_positives: None identified
-name: Extended Period Without Successful Netbackup Backups
 references: []
-search: '`netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats
-  latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)`
-  | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(),
-  "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Monitor Backup Solution
   asset_type: Endpoint
   cis20:
@@ -29,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/first_time_seen_command_line_argument.yml b/detections/deprecated/first_time_seen_command_line_argument.yml
index cc3fff3411..4346379bcd 100644
--- a/detections/deprecated/first_time_seen_command_line_argument.yml
+++ b/detections/deprecated/first_time_seen_command_line_argument.yml
@@ -1,23 +1,13 @@
-author: Bhavin Patel, Splunk
+name: First time seen command line argument
+id: 9be56c82-b1cc-4318-87eb-q138afaaqa39
+version: 5
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for command-line arguments that use a `/c` parameter
   to execute a command that has not previously been seen.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must be ingesting
-  logs with both the process name and command line from your endpoints. The complete
-  process name with command-line arguments are mapped to the "process" field in the
-  Endpoint data model. Please make sure you run the support search "Previously seen
-  command line arguments,"&#151;which creates a lookup file called `previously_seen_cmd_line_arguments.csv`&#151;a
-  historical baseline of all command-line arguments. You must also validate this list.
-  For the search to do accurate calculation, ensure the search scheduling is the same
-  value as the `relative_time` evaluation function.
-id: 9be56c82-b1cc-4318-87eb-q138afaaqa39
-known_false_positives: Legitimate programs can also use command-line arguments to
-  execute. Please verify the command-line arguments to check what command/program
-  is being executed. We recommend customizing the `first_time_seen_cmd_line_filter`
-  macro to exclude legitimate parent_process_name
-name: First time seen command line argument
-references: []
 search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe
   Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name
@@ -30,8 +20,22 @@ search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_
   previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(),
   "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` '
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must be ingesting
+  logs with both the process name and command line from your endpoints. The complete
+  process name with command-line arguments are mapped to the "process" field in the
+  Endpoint data model. Please make sure you run the support search "Previously seen
+  command line arguments,"&#151;which creates a lookup file called `previously_seen_cmd_line_arguments.csv`&#151;a
+  historical baseline of all command-line arguments. You must also validate this list.
+  For the search to do accurate calculation, ensure the search scheduling is the same
+  value as the `relative_time` evaluation function.
+known_false_positives: Legitimate programs can also use command-line arguments to
+  execute. Please verify the command-line arguments to check what command/program
+  is being executed. We recommend customizing the `first_time_seen_cmd_line_filter`
+  macro to exclude legitimate parent_process_name
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   - Suspicious Command-Line Executions
   - Orangeworm Attack Group
@@ -56,5 +60,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/deprecated/gcp_gcr_container_uploaded.yml b/detections/deprecated/gcp_gcr_container_uploaded.yml
index 86c8e7d368..7285447e7e 100644
--- a/detections/deprecated/gcp_gcr_container_uploaded.yml
+++ b/detections/deprecated/gcp_gcr_container_uploaded.yml
@@ -1,25 +1,28 @@
-author: Rod Soto, Rico Valdez, Splunk
+name: GCP GCR container uploaded
+id: 4f00ca88-e766-4605-ac65-ae51c9fd185b
+version: 1
 date: '2020-02-20'
+author: Rod Soto, Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search show information on uploaded containers including source
   user, account, action, bucket name event name, http user agent, message and destination
   path.
-how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later),
-  then configure stackdriver and set a subpub subscription to be imported to Splunk.
-  You must also install Cloud Infrastructure data model. Please also customize the
-  `container_implant_gcp_detection_filter` macro to filter out the false positives.
-id: 4f00ca88-e766-4605-ac65-ae51c9fd185b
-known_false_positives: Uploading container is a normal behavior from developers or
-  users with access to container registry. GCP GCR registers container upload as a
-  Storage event, this search must be considered under the context of CONTAINER upload
-  creation which automatically generates a bucket entry for destination path.
-name: GCP GCR container uploaded
-references: []
 search: '|tstats count min(_time) as firstTime max(_time) as lastTime  FROM datamodel=Cloud_Infrastructure.Storage
   where Storage.event_name=storage.objects.create by Storage.src_user Storage.account
   Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg
   Storage.object_path | `drop_dm_object_name("Storage")`  | `gcp_gcr_container_uploaded_filter` '
+how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later),
+  then configure stackdriver and set a subpub subscription to be imported to Splunk.
+  You must also install Cloud Infrastructure data model. Please also customize the
+  `container_implant_gcp_detection_filter` macro to filter out the false positives.
+known_false_positives: Uploading container is a normal behavior from developers or
+  users with access to container registry. GCP GCR registers container upload as a
+  Storage event, this search must be considered under the context of CONTAINER upload
+  creation which automatically generates a bucket entry for destination path.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Container Implantation Monitoring and Investigation
   asset_type: GCP GCR Container
   mitre_attack_id:
@@ -29,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/deprecated/identify_new_user_accounts.yml b/detections/deprecated/identify_new_user_accounts.yml
index 410bdd9f84..d061e8349b 100644
--- a/detections/deprecated/identify_new_user_accounts.yml
+++ b/detections/deprecated/identify_new_user_accounts.yml
@@ -1,23 +1,26 @@
-author: Bhavin Patel, Splunk
+name: Identify New User Accounts
+id: 475b9e27-17e4-46e2-b7e2-648221be3b89
+version: 1
 date: '2017-09-12'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This detection search will help profile user accounts in your environment
   by identifying newly created accounts that have been added to your network in the
   past week.
-how_to_implement: To successfully implement this search, you need to be populating
-  the Enterprise Security Identity_Management data model in the assets and identity
-  framework.
-id: 475b9e27-17e4-46e2-b7e2-648221be3b89
-known_false_positives: If the Identity_Management data model is not updated regularly,
-  this search could give you false positive alerts. Please consider this and investigate
-  appropriately.
-name: Identify New User Accounts
-references: []
 search: '| from datamodel Identity_Management.All_Identities  | eval empStatus=case((now()-startDate)<604800,
   "Accounts created in last week") | search empStatus="Accounts created in last week"|
   `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity
   empStatus endDate startDate | `identify_new_user_accounts_filter`'
+how_to_implement: To successfully implement this search, you need to be populating
+  the Enterprise Security Identity_Management data model in the assets and identity
+  framework.
+known_false_positives: If the Identity_Management data model is not updated regularly,
+  this search could give you false positive alerts. Please consider this and investigate
+  appropriately.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Account Monitoring and Controls
   asset_type: Domain Server
   cis20:
@@ -31,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 1
diff --git a/detections/deprecated/malicious_powershell_process___multiple_suspicious_command_line_arguments.yml b/detections/deprecated/malicious_powershell_process___multiple_suspicious_command_line_arguments.yml
index a86e84d2c9..f25d7f22f3 100644
--- a/detections/deprecated/malicious_powershell_process___multiple_suspicious_command_line_arguments.yml
+++ b/detections/deprecated/malicious_powershell_process___multiple_suspicious_command_line_arguments.yml
@@ -1,5 +1,11 @@
-author: David Dorsey, Splunk
+name: Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments
+id: 2cdb91d2-542c-497f-b252-be495e71f38c
+version: 6
 date: '2021-01-19'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for PowerShell processes started with a base64 encoded
   command-line passed to it, with parameters to modify the execution policy for the
   process, and those that prevent the display of an interactive prompt to the user.
@@ -7,24 +13,22 @@ description: This search looks for PowerShell processes started with a base64 en
   default PowerShell execution policy, attempts to hide itself from the user, and
   passes an encoded script to be run on the command-line. Deprecated because almost
   the same as Malicious PowerShell Process - Encoded Command
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: 2cdb91d2-542c-497f-b252-be495e71f38c
-known_false_positives: Legitimate process can have this combination of command-line
-  options, but it's not common.
-name: Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe
   by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  |
   `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
   search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* | `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: Legitimate process can have this combination of command-line
+  options, but it's not common.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Malicious PowerShell
   asset_type: Endpoint
   cis20:
@@ -45,5 +49,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 6
diff --git a/detections/deprecated/monitor_dns_for_brand_abuse.yml b/detections/deprecated/monitor_dns_for_brand_abuse.yml
index 1bca72ed2e..192b397281 100644
--- a/detections/deprecated/monitor_dns_for_brand_abuse.yml
+++ b/detections/deprecated/monitor_dns_for_brand_abuse.yml
@@ -1,7 +1,16 @@
-author: David Dorsey, Splunk
+name: Monitor DNS For Brand Abuse
+id: 24dd17b1-e2fb-4c31-878c-d4f746595bfa
+version: 1
 date: '2017-09-23'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search looks for DNS requests for faux domains similar to the domains
   that you want to have monitored for abuse.
+search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time)
+  as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")`
+  | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter`'
 how_to_implement: You need to ingest data from your DNS logs. Specifically you must
   ingest the domain that is being queried and the IP of the host originating the request.
   Ideally, you should also be ingesting the answer to the query and the query type.
@@ -9,15 +18,10 @@ how_to_implement: You need to ingest data from your DNS logs. Specifically you m
   which can aid you in future investigations. You also need to have run the search
   "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that
   will be checked for.
-id: 24dd17b1-e2fb-4c31-878c-d4f746595bfa
 known_false_positives: None at this time
-name: Monitor DNS For Brand Abuse
 references: []
-search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time)
-  as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")`
-  | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Brand Monitoring
   asset_type: Endpoint
   kill_chain_phases:
@@ -28,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/deprecated/open_redirect_in_splunk_web.yml b/detections/deprecated/open_redirect_in_splunk_web.yml
index 7ef82cad94..4de972c878 100644
--- a/detections/deprecated/open_redirect_in_splunk_web.yml
+++ b/detections/deprecated/open_redirect_in_splunk_web.yml
@@ -1,15 +1,18 @@
-author: Bhavin Patel, Splunk
+name: Open Redirect in Splunk Web
+id: d199fb99-2312-451a-9daa-e5efa6ed76a7
+version: 1
 date: '2017-09-19'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search allows you to look for evidence of exploitation for CVE-2016-4859,
   the Splunk Open Redirect Vulnerability.
-how_to_implement: No extra steps needed to implement this search.
-id: d199fb99-2312-451a-9daa-e5efa6ed76a7
-known_false_positives: None identified
-name: Open Redirect in Splunk Web
-references: []
 search: index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter`
+how_to_implement: No extra steps needed to implement this search.
+known_false_positives: None identified
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Splunk Enterprise Vulnerability
   asset_type: Splunk Server
   cis20:
@@ -30,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/deprecated/osquery_pack___coldroot_detection.yml b/detections/deprecated/osquery_pack___coldroot_detection.yml
index a0e62ef758..67af959905 100644
--- a/detections/deprecated/osquery_pack___coldroot_detection.yml
+++ b/detections/deprecated/osquery_pack___coldroot_detection.yml
@@ -1,20 +1,23 @@
-author: Rico Valdez, Splunk
+name: Osquery pack - ColdRoot detection
+id: a6fffe5e-05c3-4c04-badc-887607fbb8dc
+version: 1
 date: '2019-01-29'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search looks for ColdRoot events from the osx-attacks osquery pack.
+search: '| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd
+  OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path |
+  bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter`'
 how_to_implement: In order to properly run this search, Splunk needs to ingest data
   from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599)
   pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must
   be deployed across your indexers and universal forwarders in order to have the osquery
   data populate the Alerts data model
-id: a6fffe5e-05c3-4c04-badc-887607fbb8dc
 known_false_positives: There are no known false positives.
-name: Osquery pack - ColdRoot detection
 references: []
-search: '| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd
-  OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path |
-  bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - ColdRoot MacOS RAT
   asset_type: Endpoint
   cis20:
@@ -32,5 +35,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/deprecated/processes_created_by_netsh.yml b/detections/deprecated/processes_created_by_netsh.yml
index 28b61e103e..6194f8d3d8 100644
--- a/detections/deprecated/processes_created_by_netsh.yml
+++ b/detections/deprecated/processes_created_by_netsh.yml
@@ -1,5 +1,11 @@
-author: Bhavin Patel, Splunk
+name: Processes created by netsh
+id: b89919ed-fe5f-492c-b139-95dbb162041e
+version: 5
 date: '2020-11-23'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for processes launching netsh.exe to execute various
   commands via the netsh command-line utility. Netsh.exe is a command-line scripting
   utility that allows you to, either locally or remotely, display or modify the network
@@ -7,23 +13,21 @@ description: This search looks for processes launching netsh.exe to execute vari
   proxy technique to execute a helper .dll when netsh.exe is executed. In this search,
   we are looking for processes spawned by netsh.exe that are executing commands via
   the command line. Deprecated because we have another detection of the same type.
-how_to_implement: To successfully implement this search, you must be ingesting logs
-  with the process name, command-line arguments, and parent processes from your endpoints.
-  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-id: b89919ed-fe5f-492c-b139-95dbb162041e
-known_false_positives: It is unusual for netsh.exe to have any child processes in
-  most environments. It makes sense to investigate the child process and verify whether
-  the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe"
-  process path since it is a legitimate process by Mircosoft.
-name: Processes created by netsh
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process
   Processes.parent_process_name Processes.process_name | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `processes_created_by_netsh_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting logs
+  with the process name, command-line arguments, and parent processes from your endpoints.
+  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: It is unusual for netsh.exe to have any child processes in
+  most environments. It makes sense to investigate the child process and verify whether
+  the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe"
+  process path since it is a legitimate process by Mircosoft.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Netsh Abuse
   asset_type: Endpoint
   cis20:
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/deprecated/prohibited_software_on_endpoint.yml b/detections/deprecated/prohibited_software_on_endpoint.yml
index 58e60fa368..d1b4ad4cef 100644
--- a/detections/deprecated/prohibited_software_on_endpoint.yml
+++ b/detections/deprecated/prohibited_software_on_endpoint.yml
@@ -1,27 +1,31 @@
-author: David Dorsey, Splunk
+name: Prohibited Software On Endpoint
+id: a51bfe1a-94f0-48cc-b4e4-b6ae50145893
+version: 2
 date: '2019-10-11'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for applications on the endpoint that you have marked
   as prohibited.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)`
+  | `prohibited_softwares` | `prohibited_software_on_endpoint_filter`'
 how_to_implement: To successfully implement this search, you must be ingesting data
   that records process activity from your hosts to populate the endpoint data model
   in the processes node. This is typically populated via endpoint detection-and-response
-  products, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
   used for this search is usually generated via logs that report process tracking
   in your Windows audit settings. In addition, you must also have only the `process_name`
   (not the entire process path) marked as "prohibited" in the Enterprise Security
   `interesting processes` table. To include the process names marked as "prohibited",
   which is included with ES Content Updates, run the included search <code>Add Prohibited
   Processes to Enterprise Security</code>.
-id: a51bfe1a-94f0-48cc-b4e4-b6ae50145893
 known_false_positives: None identified
-name: Prohibited Software On Endpoint
 references: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name
-  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)`
-  | `prohibited_softwares` | `prohibited_software_on_endpoint_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Monitor for Unauthorized Software
   - 'Emotet Malware  DHS Report TA18-201A '
   - SamSam Ransomware
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/deprecated/reg_exe_used_to_hide_files_directories_via_registry_keys.yml b/detections/deprecated/reg_exe_used_to_hide_files_directories_via_registry_keys.yml
index 7c342ecf6b..b2c90e57cd 100644
--- a/detections/deprecated/reg_exe_used_to_hide_files_directories_via_registry_keys.yml
+++ b/detections/deprecated/reg_exe_used_to_hide_files_directories_via_registry_keys.yml
@@ -1,24 +1,28 @@
-author: Bhavin Patel, Splunk
+name: Reg exe used to hide files directories via registry keys
+id: c77162d3-f93c-45cc-80c8-22f6b5264x9f
+version: 2
 date: '2019-02-27'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks for command-line arguments used to hide a file or directory
   using the reg add command.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: c77162d3-f93c-45cc-80c8-22f6b5264x9f
-known_false_positives: None at the moment
-name: Reg exe used to hide files directories via registry keys
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process) as process
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*"
   Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name
   Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: None at the moment
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Defense Evasion Tactics
   - Suspicious Windows Registry Activities
   - Windows Persistence Techniques
@@ -36,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/deprecated/remote_registry_key_modifications.yml b/detections/deprecated/remote_registry_key_modifications.yml
index 5a27cbfa09..34afa2b49b 100644
--- a/detections/deprecated/remote_registry_key_modifications.yml
+++ b/detections/deprecated/remote_registry_key_modifications.yml
@@ -1,23 +1,26 @@
-author: Bhavin Patel, Splunk
-date: '2020-03-02'
-description: This search monitors for remote modifications to registry keys.
-how_to_implement: To successfully implement this search, you must populate the `Endpoint`
-  data model. This is typically populated via endpoint detection-and-response products,
-  such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for
-  this search is typically generated via logs that report reads and writes to the
-  registry. Deprecated because I don't think the logic is right.
-id: c9f4b923-f8af-4155-b697-1354f5dcbc5e
-known_false_positives: This technique may be legitimately used by administrators to
-  modify remote registries, so it's important to filter these events out.
 name: Remote Registry Key modifications
-references: []
+id: c9f4b923-f8af-4155-b697-1354f5dcbc5e
+version: 3
+date: '2020-03-02'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
+description: This search monitors for remote modifications to registry keys.
 search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name)
   as registry_key_name values(Registry.registry_path) as registry_path min(_time)
   as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where  Registry.registry_path="\\\\*"  by
   Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter`'
+how_to_implement: To successfully implement this search, you must populate the `Endpoint`
+  data model. This is typically populated via endpoint detection-and-response product,
+  such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for
+  this search is typically generated via logs that report reads and writes to the
+  registry. Deprecated because I don't think the logic is right.
+known_false_positives: This technique may be legitimately used by administrators to
+  modify remote registries, so it's important to filter these events out.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Defense Evasion Tactics
   - Suspicious Windows Registry Activities
   - Windows Persistence Techniques
@@ -34,5 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/deprecated/remote_wmi_command_attempt.yml b/detections/deprecated/remote_wmi_command_attempt.yml
index df53386839..69fb12d968 100644
--- a/detections/deprecated/remote_wmi_command_attempt.yml
+++ b/detections/deprecated/remote_wmi_command_attempt.yml
@@ -1,25 +1,29 @@
-author: Rico Valdez, Splunk
+name: Remote WMI Command Attempt
+id: 272df6de-61f1-4784-877c-1fbc3e2d0838
+version: 2
 date: '2018-12-03'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for wmic.exe being launched with parameters to operate
   on remote systems.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model. Deprecated because duplicate of Remote Process Instantiation via WMI.
-id: 272df6de-61f1-4784-877c-1fbc3e2d0838
-known_false_positives: Administrators may use this legitimately to gather info from
-  remote systems.
-name: Remote WMI Command Attempt
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe  AND
   Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name
   Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model. Deprecated because duplicate of Remote Process Instantiation via WMI.
+known_false_positives: Administrators may use this legitimately to gather info from
+  remote systems.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious WMI Use
   asset_type: Endpoint
   cis20:
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/deprecated/scheduled_tasks_used_in_badrabbit_ransomware.yml b/detections/deprecated/scheduled_tasks_used_in_badrabbit_ransomware.yml
index ffb2308c21..2890ec3d5d 100644
--- a/detections/deprecated/scheduled_tasks_used_in_badrabbit_ransomware.yml
+++ b/detections/deprecated/scheduled_tasks_used_in_badrabbit_ransomware.yml
@@ -1,25 +1,29 @@
-author: Bhavin Patel, Splunk
+name: Scheduled tasks used in BadRabbit ransomware
+id: 1297fb80-f42a-4b4a-9c8b-78c066437cf6
+version: 3
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for flags passed to schtasks.exe on the command-line
   that indicate that task names related to the execution of Bad Rabbit ransomware
   were created or deleted. Deprecated because we already have a similar detection
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: 1297fb80-f42a-4b4a-9c8b-78c066437cf6
-known_false_positives: No known false positives
-name: Scheduled tasks used in BadRabbit ransomware
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Processes.process) as process  from datamodel=Endpoint.Processes
   where Processes.process_name=schtasks.exe (Processes.process= "*create*"  OR Processes.process=
   "*delete*") by Processes.parent_process Processes.process_name Processes.user |
   `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
   | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: No known false positives
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Ransomware
   asset_type: Endpoint
   cis20:
@@ -35,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/deprecated/splunk_enterprise_information_disclosure.yml b/detections/deprecated/splunk_enterprise_information_disclosure.yml
index 6558a14289..41267e544d 100644
--- a/detections/deprecated/splunk_enterprise_information_disclosure.yml
+++ b/detections/deprecated/splunk_enterprise_information_disclosure.yml
@@ -1,22 +1,25 @@
-author: David Dorsey, Splunk
+name: Splunk Enterprise Information Disclosure
+id: f6a26b7b-7e80-4963-a9a8-d836e7534ebd
+version: 1
 date: '2018-06-14'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search allows you to look for evidence of exploitation for CVE-2018-11409,
   a Splunk Enterprise Information Disclosure Bug.
-how_to_implement: The REST endpoint that exposes system information is also necessary
-  for the proper operation of Splunk clustering and instrumentation. Whitelisting
-  your Splunk systems will reduce false positives.
-id: f6a26b7b-7e80-4963-a9a8-d836e7534ebd
-known_false_positives: Retrieving server information may be a legitimate API request.
-  Verify that the attempt is a valid request for information.
-name: Splunk Enterprise Information Disclosure
-references: []
 search: index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1
   uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server
   as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri)
   as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_enterprise_information_disclosure_filter`
+how_to_implement: The REST endpoint that exposes system information is also necessary
+  for the proper operation of Splunk clustering and instrumentation. Whitelisting
+  your Splunk systems will reduce false positives.
+known_false_positives: Retrieving server information may be a legitimate API request.
+  Verify that the attempt is a valid request for information.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Splunk Enterprise Vulnerability CVE-2018-11409
   asset_type: Splunk Server
   cis20:
@@ -37,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/deprecated/suspicious_changes_to_file_associations.yml b/detections/deprecated/suspicious_changes_to_file_associations.yml
index 5313a9de58..5fd3eea554 100644
--- a/detections/deprecated/suspicious_changes_to_file_associations.yml
+++ b/detections/deprecated/suspicious_changes_to_file_associations.yml
@@ -1,18 +1,13 @@
-author: Rico Valdez, Splunk
+name: Suspicious Changes to File Associations
+id: 1b989a0e-0129-4446-a695-f193a5b746fc
+version: 4
 date: '2020-07-22'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search looks for changes to registry values that control Windows
   file associations, executed by a process that is not typical for legitimate, routine
   changes to this area.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on registry changes that include the name of the process responsible for the changes
-  from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry`
-  nodes.
-id: 1b989a0e-0129-4446-a695-f193a5b746fc
-known_false_positives: There may be other processes in your environment that users
-  may legitimately use to modify file associations. If this is the case and you are
-  finding false positives, you can modify the search to add those processes as exceptions.
-name: Suspicious Changes to File Associations
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name)
   as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe
@@ -22,8 +17,16 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   as registry_path count  FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts*
   by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table
   process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` '
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on registry changes that include the name of the process responsible for the changes
+  from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry`
+  nodes.
+known_false_positives: There may be other processes in your environment that users
+  may legitimately use to modify file associations. If this is the case and you are
+  finding false positives, you can modify the search to add those processes as exceptions.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Windows Registry Activities
   - Windows File Extension and Association Abuse
   asset_type: Endpoint
@@ -43,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/deprecated/suspicious_file_write.yml b/detections/deprecated/suspicious_file_write.yml
index 24b2611dea..1645800eae 100644
--- a/detections/deprecated/suspicious_file_write.yml
+++ b/detections/deprecated/suspicious_file_write.yml
@@ -1,31 +1,34 @@
-author: Rico Valdez, Splunk
+name: Suspicious File Write
+id: 57f76b8a-32f0-42ed-b358-d9fa3ca7bac8
+version: 3
 date: '2019-04-25'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: The search looks for files created with names that have been linked to
   malicious activity.
+search: '| tstats `security_content_summariesonly` count values(Filesystem.action)
+  as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)`
+  | `suspicious_writes` | `suspicious_file_write_filter`'
 how_to_implement: You must be ingesting data that records the filesystem activity
   from your hosts to populate the Endpoint file-system data model node. This is typically
-  populated via endpoint detection-and-response products, such as Carbon Black, or
+  populated via endpoint detection-and-response product, such as Carbon Black, or
   via other endpoint data sources, such as Sysmon. The data used for this search is
   typically generated via logs that report file system reads and writes. In addition,
   this search leverages an included lookup file that contains the names of the files
   to watch for, as well as a note to communicate why that file name is being monitored.
   This lookup file can be edited to add or remove file the file names you want to
   monitor.
-id: 57f76b8a-32f0-42ed-b358-d9fa3ca7bac8
 known_false_positives: It's possible for a legitimate file to be created with the
   same name as one noted in the lookup file. Filenames listed in the lookup file should
   be unique enough that collisions are rare. Looking at the location of the file and
   the process responsible for the activity can help determine whether or not the activity
   is legitimate.
-name: Suspicious File Write
 references: []
-search: '| tstats `security_content_summariesonly` count values(Filesystem.action)
-  as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time)
-  as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest
-  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)`
-  | `suspicious_writes` | `suspicious_file_write_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Hidden Cobra Malware
   asset_type: Endpoint
   cis20:
@@ -40,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/deprecated/suspicious_writes_to_system_volume_information.yml b/detections/deprecated/suspicious_writes_to_system_volume_information.yml
index 3e8b335211..6008e6015c 100644
--- a/detections/deprecated/suspicious_writes_to_system_volume_information.yml
+++ b/detections/deprecated/suspicious_writes_to_system_volume_information.yml
@@ -1,22 +1,25 @@
-author: Rico Valdez, Splunk
+name: Suspicious writes to System Volume Information
+id: cd6297cd-2bdd-4aa1-84aa-5d2f84228fac
+version: 2
 date: '2020-07-22'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search detects writes to the 'System Volume Information' folder
   by something other than the System process.
-how_to_implement: You need to be ingesting logs with both the process name and command-line
-  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4
-  of the Sysmon TA.
-id: cd6297cd-2bdd-4aa1-84aa-5d2f84228fac
-known_false_positives: It is possible that other utilities or system processes may
-  legitimately write to this folder. Investigate and modify the search to include
-  exceptions as appropriate.
-name: Suspicious writes to System Volume Information
-references: []
 search: (`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\
   Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest,
   Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `suspicious_writes_to_system_volume_information_filter`
+how_to_implement: You need to be ingesting logs with both the process name and command-line
+  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4
+  of the Sysmon TA.
+known_false_positives: It is possible that other utilities or system processes may
+  legitimately write to this folder. Investigate and modify the search to include
+  exceptions as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Collection and Staging
   asset_type: Windows
   cis20:
@@ -30,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/deprecated/uncommon_processes_on_endpoint.yml b/detections/deprecated/uncommon_processes_on_endpoint.yml
index fb7b6b75d6..5a6cd007e6 100644
--- a/detections/deprecated/uncommon_processes_on_endpoint.yml
+++ b/detections/deprecated/uncommon_processes_on_endpoint.yml
@@ -1,7 +1,17 @@
-author: David Dorsey, Splunk
+name: Uncommon Processes On Endpoint
+id: 29ccce64-a10c-4389-a45f-337cb29ba1f7
+version: 4
 date: '2020-07-22'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for applications on the endpoint that you have marked
   as uncommon.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process
+  Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` '
 how_to_implement: You must be ingesting data that records process activity from your
   hosts to populate the Endpoint data model in the Processes node. You must also be
   ingesting logs with both the process name and command line from your endpoints.
@@ -10,16 +20,10 @@ how_to_implement: You must be ingesting data that records process activity from
   various features of process names that are usually uncommon in most environments.
   Please consider updating `uncommon_processes_local.csv` to hunt for processes that
   are uncommon in your environment.
-id: 29ccce64-a10c-4389-a45f-337cb29ba1f7
 known_false_positives: None identified
-name: Uncommon Processes On Endpoint
 references: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process
-  Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Privilege Escalation
   - Unusual Processes
   - Cloud Federated Credential Abuse
@@ -37,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/deprecated/unsigned_image_loaded_by_lsass.yml b/detections/deprecated/unsigned_image_loaded_by_lsass.yml
index c2f37c4ed2..605f07b9ca 100644
--- a/detections/deprecated/unsigned_image_loaded_by_lsass.yml
+++ b/detections/deprecated/unsigned_image_loaded_by_lsass.yml
@@ -1,25 +1,28 @@
-author: Patrick Bareiss, Splunk
+name: Unsigned Image Loaded by LSASS
+id: 56ef054c-76ef-45f9-af4a-a634695dcd65
+version: 1
 date: '2019-12-06'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search detects loading of unsigned images by LSASS. Deprecated because
   too noisy.
+search: '`sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time)
+  as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1
+  | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `unsigned_image_loaded_by_lsass_filter` '
 how_to_implement: This search needs Sysmon Logs with a sysmon configuration, which
   includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`.
   We strongly recommend that you specify your environment-specific configurations
   (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
   with configurations for your Splunk environment. The search also uses a post-filter
   macro designed to filter out known false positives.
-id: 56ef054c-76ef-45f9-af4a-a634695dcd65
 known_false_positives: Other tools could load images into LSASS for legitimate reason.
   But enterprise tools should always use signed DLLs.
-name: Unsigned Image Loaded by LSASS
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
-search: '`sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time)
-  as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1
-  | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `unsigned_image_loaded_by_lsass_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Windows
   cis20:
@@ -36,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/unsuccessful_netbackup_backups.yml b/detections/deprecated/unsuccessful_netbackup_backups.yml
index 712ef9d99a..9ba02a3e78 100644
--- a/detections/deprecated/unsuccessful_netbackup_backups.yml
+++ b/detections/deprecated/unsuccessful_netbackup_backups.yml
@@ -1,21 +1,24 @@
-author: David Dorsey, Splunk
+name: Unsuccessful Netbackup backups
+id: a34aae96-ccf8-4aaa-952c-3ea21444444f
+version: 1
 date: '2017-09-12'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search gives you the hosts where a backup was attempted and then
   failed.
-how_to_implement: To successfully implement this search you need to obtain data from
-  your backup solution, either from the backup logs on your endpoints or from a central
-  server responsible for performing the backups. If you do not use Netbackup, you
-  can modify this search for your specific backup solution.
-id: a34aae96-ccf8-4aaa-952c-3ea21444444f
-known_false_positives: None identified
-name: Unsuccessful Netbackup backups
-references: []
 search: '`netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE
   | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)`
   | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature
   | `unsuccessful_netbackup_backups_filter`'
+how_to_implement: To successfully implement this search you need to obtain data from
+  your backup solution, either from the backup logs on your endpoints or from a central
+  server responsible for performing the backups. If you do not use Netbackup, you
+  can modify this search for your specific backup solution.
+known_false_positives: None identified
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Monitor Backup Solution
   asset_type: Endpoint
   cis20:
@@ -27,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/windows_connhost_exe_force_flag.yml b/detections/deprecated/windows_connhost_exe_force_flag.yml
index 0663888f64..b0918d7154 100644
--- a/detections/deprecated/windows_connhost_exe_force_flag.yml
+++ b/detections/deprecated/windows_connhost_exe_force_flag.yml
@@ -1,26 +1,29 @@
-author: Rod Soto, Jose Hernandez, Splunk
+name: Windows connhost exe started forcefully
+id: c114aaca-68ee-41c2-ad8c-32bf21db8769
+version: 1
 date: '2020-11-06'
+author: Rod Soto, Jose Hernandez, Splunk
+type: batch
+datamodel: []
 description: 'The search looks for the Console Window Host process (connhost.exe)
   executed using the force flag -ForceV1. This is not regular behavior in the Windows
   OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually
   seen in the windows 10 client of attack_range_local. After further testing we realized
   this is not specific to Ryuk. '
-how_to_implement: You must be ingesting data that records the process-system activity
-  from your hosts to populate the Endpoint Processes data-model object. If you are
-  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
-  you want to collect data.
-id: c114aaca-68ee-41c2-ad8c-32bf21db8769
-known_false_positives: This process should not be ran forcefully, we have not see
-  any false positives for this detection
-name: Windows connhost exe started forcefully
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe*
   0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process
   Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `windows_connhost_exe_started_forcefully_filter`'
+how_to_implement: You must be ingesting data that records the process-system activity
+  from your hosts to populate the Endpoint Processes data-model object. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: This process should not be ran forcefully, we have not see
+  any false positives for this detection
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Ryuk Ransomware
   asset_type: Endpoint
   cis20:
@@ -37,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/windows_disableantispyware_reg.yml b/detections/deprecated/windows_disableantispyware_reg.yml
index 3e171ce6ec..bb43cc26ce 100644
--- a/detections/deprecated/windows_disableantispyware_reg.yml
+++ b/detections/deprecated/windows_disableantispyware_reg.yml
@@ -1,25 +1,29 @@
-author: Rod Soto, Jose Hernandez, Splunk
+name: Windows DisableAntiSpyware Registry
+id: 23150a40-9301-4195-b802-5bb4f43067fb
+version: 1
 date: '2020-11-06'
+author: Rod Soto, Jose Hernandez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks for the Registry Key DisableAntiSpyware set to disable.
   This is consistent with Ryuk infections across a fleet of endpoints.
-how_to_implement: You must be ingesting data that records the process-system activity
-  from your hosts to populate the Endpoint Processes data-model object. If you are
-  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
-  you want to collect data.
-id: 23150a40-9301-4195-b802-5bb4f43067fb
-known_false_positives: It is unusual to turn this feature on a Windows system since
-  it is a default security control, although it is not rare for some policies to disable
-  it. Although no false positives have been identified, use the provided filter macro
-  to tune the search.
-name: Windows DisableAntiSpyware Registry
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware"
   AND Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest Registry.user
   Registry.registry_path Registry.registry_value_name | `drop_dm_object_name(Registry)`
   | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter`'
+how_to_implement: You must be ingesting data that records the process-system activity
+  from your hosts to populate the Endpoint Processes data-model object. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: It is unusual to turn this feature on a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Ryuk Ransomware
   asset_type: Endpoint
   cis20:
@@ -36,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/deprecated/windows_hosts_file_modification.yml b/detections/deprecated/windows_hosts_file_modification.yml
index 4f48aad5de..fb19326498 100644
--- a/detections/deprecated/windows_hosts_file_modification.yml
+++ b/detections/deprecated/windows_hosts_file_modification.yml
@@ -1,25 +1,28 @@
-author: Rico Valdez, Splunk
+name: Windows hosts file modification
+id: 06a6fc63-a72d-41dc-8736-7e3dd9612116
+version: 1
 date: '2018-11-02'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: The search looks for modifications to the hosts file on all Windows endpoints
   across your environment.
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records the file-system activity from your hosts to populate the Endpoint.Filesystem
-  data model node. This is typically populated via endpoint detection-and-response
-  products, such as Carbon Black, or by other endpoint data sources, such as Sysmon.
-  The data used for this search is typically generated via logs that report file-system
-  reads and writes.
-id: 06a6fc63-a72d-41dc-8736-7e3dd9612116
-known_false_positives: There may be legitimate reasons for system administrators to
-  add entries to this file.
-name: Windows hosts file modification
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Filesystem  by Filesystem.file_name Filesystem.file_path
   Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\*
   | `drop_dm_object_name(Filesystem)` | `windows_hosts_file_modification_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records the file-system activity from your hosts to populate the Endpoint.Filesystem
+  data model node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black, or by other endpoint data sources, such as Sysmon.
+  The data used for this search is typically generated via logs that report file-system
+  reads and writes.
+known_false_positives: There may be legitimate reasons for system administrators to
+  add entries to this file.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Host Redirection
   asset_type: Endpoint
   cis20:
@@ -39,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/access_lsass_memory_for_dump_creation.yml b/detections/endpoint/access_lsass_memory_for_dump_creation.yml
index 4875d9e0d3..8e56001e02 100644
--- a/detections/endpoint/access_lsass_memory_for_dump_creation.yml
+++ b/detections/endpoint/access_lsass_memory_for_dump_creation.yml
@@ -1,24 +1,27 @@
-author: Patrick Bareiss, Splunk
+name: Access LSASS Memory for Dump Creation
+id: fb4c31b0-13e8-4155-8aa5-24de4b8d6717
+version: 2
 date: '2019-12-06'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: Detect memory dumping of the LSASS process.
+search: '`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll*
+  | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage,
+  TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter` '
 how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which
   includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`.
   We strongly recommend that you specify your environment-specific configurations
   (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
   with configurations for your Splunk environment. The search also uses a post-filter
   macro designed to filter out known false positives.
-id: fb4c31b0-13e8-4155-8aa5-24de4b8d6717
 known_false_positives: Administrators can create memory dumps for debugging purposes,
   but memory dumps of the LSASS process would be unusual.
-name: Access LSASS Memory for Dump Creation
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
-search: '`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll*
-  | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage,
-  TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`|
-  `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Windows
   automated_detection_testing: passed
@@ -38,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml b/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml
index 63901022b3..6b85704d5b 100644
--- a/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml
+++ b/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml
@@ -1,24 +1,28 @@
-author: Patrick Bareiss, Rico Valdez, Splunk
-date: '2020-11-03'
-description: Attempt to add a certificate to the certificate store
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: 6bc5243e-ef36-45dc-9b12-f4a6be131159
-known_false_positives: There may be legitimate reasons for administrators to add a
-  certificate to the untrusted certificate store. In such cases, this will typically
-  be done on a large number of systems.
 name: Attempt To Add Certificate To Untrusted Store
-references: []
+id: 6bc5243e-ef36-45dc-9b12-f4a6be131159
+version: 6
+date: '2020-11-03'
+author: Patrick Bareiss, Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
+description: Attempt to add a certificate to the certificate store
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process)
   as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil*
   (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name
   Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
   |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: There may be legitimate reasons for administrators to add a
+  certificate to the untrusted certificate store. In such cases, this will typically
+  be done on a large number of systems.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Disabling Security Tools
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -42,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 6
diff --git a/detections/endpoint/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml b/detections/endpoint/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml
index 6302591b71..a3db88ef9d 100644
--- a/detections/endpoint/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml
+++ b/detections/endpoint/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml
@@ -1,26 +1,30 @@
-author: Patrick Bareiss, Splunk
+name: Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass
+id: c2590137-0b08-4985-9ec5-6ae23d92f63d
+version: 6
 date: '2020-11-06'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: Monitor for changes of the ExecutionPolicy in the registry to the values
   "unrestricted" or "bypass," which allows the execution of malicious scripts.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Registry node. You must also be
-  ingesting logs with the fields registry_path, registry_key_name, and registry_value_name
-  from your endpoints.
-id: c2590137-0b08-4985-9ec5-6ae23d92f63d
-known_false_positives: Administrators may attempt to change the default execution
-  policy on a system for a variety of reasons. However, setting the policy to "unrestricted"
-  or "bypass" as this search is designed to identify, would be unusual. Hits should
-  be reviewed and investigated as appropriate.
-name: Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell*
   Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted
   OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name
   Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
   | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Registry node. You must also be
+  ingesting logs with the fields registry_path, registry_key_name, and registry_value_name
+  from your endpoints.
+known_false_positives: Administrators may attempt to change the default execution
+  policy on a system for a variety of reasons. However, setting the policy to "unrestricted"
+  or "bypass" as this search is designed to identify, would be unusual. Hits should
+  be reviewed and investigated as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Malicious PowerShell
   - Credential Dumping
   asset_type: Endpoint
@@ -42,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 6
diff --git a/detections/endpoint/attempt_to_stop_security_service.yml b/detections/endpoint/attempt_to_stop_security_service.yml
index e6c669923e..aeda81bccd 100644
--- a/detections/endpoint/attempt_to_stop_security_service.yml
+++ b/detections/endpoint/attempt_to_stop_security_service.yml
@@ -1,7 +1,20 @@
-author: Rico Valdez, Splunk
+name: Attempt To Stop Security Service
+id: c8e349c6-b97c-486e-8949-bd7bcd1f3910
+version: 3
 date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for attempts to stop security-related services on the
   endpoint.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name = net.exe OR  Processes.process_name = sc.exe) Processes.process="*
+  stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  |lookup security_services_lookup service as process OUTPUTNEW category, description
+  | search category=security | `attempt_to_stop_security_service_filter`'
 how_to_implement: You must be ingesting data that records the file-system activity
   from your hosts to populate the Endpoint file-system data-model node. If you are
   using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
@@ -12,20 +25,11 @@ how_to_implement: You must be ingesting data that records the file-system activi
   use on the command line and surround with asterisks (*****), so that they work properly
   when searching the command line. The file should be updated with the names of any
   services you would like to monitor for attempts to stop the service.,
-id: c8e349c6-b97c-486e-8949-bd7bcd1f3910
 known_false_positives: None identified. Attempts to disable security-related services
   should be identified and understood.
-name: Attempt To Stop Security Service
 references: []
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process
-  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
-  where (Processes.process_name = net.exe OR  Processes.process_name = sc.exe) Processes.process="*
-  stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user
-  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  |lookup security_services_lookup service as process OUTPUTNEW category, description
-  | search category=security | `attempt_to_stop_security_service_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Disabling Security Tools
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -49,5 +53,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/attempted_credential_dump_from_registry_via_reg_exe.yml b/detections/endpoint/attempted_credential_dump_from_registry_via_reg_exe.yml
index 569f46c6d6..073111e911 100644
--- a/detections/endpoint/attempted_credential_dump_from_registry_via_reg_exe.yml
+++ b/detections/endpoint/attempted_credential_dump_from_registry_via_reg_exe.yml
@@ -1,15 +1,13 @@
-author: Patrick Bareiss, Splunk
+name: Attempted Credential Dump From Registry via Reg exe
+id: e9fb4a59-c5fb-440a-9f24-191fbc6b2911
+version: 4
 date: '2019-12-02'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: Monitor for execution of reg.exe with parameters specifying an export
   of keys that contain hashed credentials that attackers may try to crack offline.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints, to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-id: e9fb4a59-c5fb-440a-9f24-191fbc6b2911
-known_false_positives: None identified.
-name: Attempted Credential Dump From Registry via Reg exe
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe
   OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security*
@@ -17,8 +15,14 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*)
   by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints, to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: None identified.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/batch_file_write_to_system32.yml b/detections/endpoint/batch_file_write_to_system32.yml
index 522710576f..831414fdba 100644
--- a/detections/endpoint/batch_file_write_to_system32.yml
+++ b/detections/endpoint/batch_file_write_to_system32.yml
@@ -1,12 +1,23 @@
-author: Rico Valdez, Splunk
+name: Batch File Write to System32
+id: 503d17cb-9eab-4cf8-a20e-01d5c6987ae3
+version: 1
 date: '2018-12-14'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks for a batch file (.bat) written to the Windows system
   directory tree.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name
+  values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`|
+  rex field=file_name "(?<file_extension>\.[^\.]+)$" | search file_path=*system32*
+  AND file_extension=.bat | `batch_file_write_to_system32_filter`'
 how_to_implement: You must be ingesting data that records the file-system activity
   from your hosts to populate the Endpoint file-system data-model node. If you are
   using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
   you want to collect data.
-id: 503d17cb-9eab-4cf8-a20e-01d5c6987ae3
 known_false_positives: It is possible for this search to generate a notable event
   for a batch file write to a path that includes the string "system32", but is not
   the actual Windows system directory. As such, you should confirm the path of the
@@ -14,16 +25,9 @@ known_false_positives: It is possible for this search to generate a notable even
   by an administrator copying a legitimate batch file in this directory tree. You
   should confirm that the activity is legitimate and modify the search to add exclusions,
   as necessary.
-name: Batch File Write to System32
 references: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name
-  values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path
-  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`|
-  rex field=file_name "(?<file_extension>\.[^\.]+)$" | search file_path=*system32*
-  AND file_extension=.bat | `batch_file_write_to_system32_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -43,5 +47,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/bcdedit_failure_recovery_modification.yml b/detections/endpoint/bcdedit_failure_recovery_modification.yml
index 2eca5cc0fa..9677362866 100644
--- a/detections/endpoint/bcdedit_failure_recovery_modification.yml
+++ b/detections/endpoint/bcdedit_failure_recovery_modification.yml
@@ -1,24 +1,28 @@
-author: Michael Haag, Splunk
+name: BCDEdit Failure Recovery Modification
+id: 809b31d2-5462-11eb-ae93-0242ac130002
+version: 1
 date: '2020-12-21'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for flags passed to bcdedit.exe modifications to the
   built-in Windows error recovery boot configurations. This is typically used by ransomware
   to prevent recovery.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. Tune based on parent process names.
-id: 809b31d2-5462-11eb-ae93-0242ac130002
-known_false_positives: Administrators may modify the boot configuration.
-name: BCDEdit Failure Recovery Modification
-references:
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe
   Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name
   Processes.process Processes.parent_process_name Processes.dest Processes.user |
   `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `bcdedit_failure_recovery_modification_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. Tune based on parent process names.
+known_false_positives: Administrators may modify the boot configuration.
+references:
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair
 tags:
-  analytics_story:
+  analytic_story:
   - Ryuk Ransomware
   - Ransomware
   asset_type: Endpoint
@@ -38,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/certutil_exe_certificate_extraction.yml b/detections/endpoint/certutil_exe_certificate_extraction.yml
index 18539c0bf7..d92b1af1ae 100644
--- a/detections/endpoint/certutil_exe_certificate_extraction.yml
+++ b/detections/endpoint/certutil_exe_certificate_extraction.yml
@@ -2,28 +2,35 @@ name: Certutil exe certificate extraction
 id: 337a46be-600f-11eb-ae93-0242ac130002
 version: 1
 date: '2021-01-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for arguments to certutil.exe indicating the manipulation
   or extraction of Certificate. This certificate can then be used to sign new authentication
   tokens specially inside Federated environments such as Windows ADFS.
-type: ESCU
-references: []
-author: Rod Soto, Splunk
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process)
   as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe
   Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name
   Processes.process Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
   |`security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter`'
+how_to_implement: ''
 known_false_positives: Unless there are specific use cases, manipulating or exporting
   certificates using certutil is uncommon. Extraction of certificate has been observed
   during attacks such as Golden SAML and other campaigns targeting Federated services.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Persistence Techniques
   - Cloud Federated Credential Abuse
-  kill_chain_phases:
-  - Installation
-  security_domain: endpoint
   asset_type: Endpoint
   automated_detection_testing: passed
   dataset:
   - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log
+  kill_chain_phases:
+  - Installation
+  security_domain: endpoint
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/common_ransomware_extensions.yml b/detections/endpoint/common_ransomware_extensions.yml
index c2635aaea1..ff222a7ad4 100644
--- a/detections/endpoint/common_ransomware_extensions.yml
+++ b/detections/endpoint/common_ransomware_extensions.yml
@@ -1,7 +1,18 @@
-author: David Dorsey, Splunk
+name: Common Ransomware Extensions
+id: a9e5c5db-db11-43ca-86a8-c852d1b2c0ec
+version: 4
 date: '2020-11-09'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks for file modifications with extensions commonly used
   by Ransomware
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path)
+  as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex
+  field=file_name "(?<file_extension>\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter`'
 how_to_implement: 'You must be ingesting data that records the filesystem activity
   from your hosts to populate the Endpoint file-system data model node. If you are
   using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
@@ -20,19 +31,12 @@ how_to_implement: 'You must be ingesting data that records the filesystem activi
 
   Detailed documentation on how to create a new field within Incident Review may be
   found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-id: a9e5c5db-db11-43ca-86a8-c852d1b2c0ec
 known_false_positives: It is possible for a legitimate file with these extensions
   to be created. If this is a true ransomware attack, there will be a large number
   of files created with these extensions.
-name: Common Ransomware Extensions
 references: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path)
-  as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)`
-  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex
-  field=file_name "(?<file_extension>\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   - Ryuk Ransonware
   - Ransomware
@@ -54,5 +58,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/common_ransomware_notes.yml b/detections/endpoint/common_ransomware_notes.yml
index b5ad136f95..a5804398ae 100644
--- a/detections/endpoint/common_ransomware_notes.yml
+++ b/detections/endpoint/common_ransomware_notes.yml
@@ -1,24 +1,28 @@
-author: David Dorsey, Splunk
+name: Common Ransomware Notes
+id: ada0f478-84a8-4641-a3f1-d82362d6bd71
+version: 4
 date: '2020-11-09'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks for files created with names matching those typically
   used in ransomware notes that tell the victim how to get their data back.
-how_to_implement: You must be ingesting data that records file-system activity from
-  your hosts to populate the Endpoint Filesystem data-model node. This is typically
-  populated via endpoint detection-and-response products, such as Carbon Black, or
-  via other endpoint data sources, such as Sysmon. The data used for this search is
-  typically generated via logs that report file-system reads and writes.
-id: ada0f478-84a8-4641-a3f1-d82362d6bd71
-known_false_positives: It's possible that a legitimate file could be created with
-  the same name used by ransomware note files.
-name: Common Ransomware Notes
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path)
   as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)`
   | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes`
   | `common_ransomware_notes_filter`'
+how_to_implement: You must be ingesting data that records file-system activity from
+  your hosts to populate the Endpoint Filesystem data-model node. This is typically
+  populated via endpoint detection-and-response product, such as Carbon Black, or
+  via other endpoint data sources, such as Sysmon. The data used for this search is
+  typically generated via logs that report file-system reads and writes.
+known_false_positives: It's possible that a legitimate file could be created with
+  the same name used by ransomware note files.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   - Ransomware
   - Ryuk Ransomware
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/create_local_admin_accounts_using_net_exe.yml b/detections/endpoint/create_local_admin_accounts_using_net_exe.yml
index 51d1796170..a5ce2ba435 100644
--- a/detections/endpoint/create_local_admin_accounts_using_net_exe.yml
+++ b/detections/endpoint/create_local_admin_accounts_using_net_exe.yml
@@ -1,16 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Create local admin accounts using net exe
+id: b89919ed-fe5f-492c-b139-151bb162040e
+version: 4
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for the creation of local administrator accounts using
   net.exe.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: b89919ed-fe5f-492c-b139-151bb162040e
-known_false_positives: Administrators often leverage net.exe to create admin accounts.
-name: Create local admin accounts using net exe
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.user) as
   user values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe
@@ -18,8 +15,15 @@ search: '| tstats `security_content_summariesonly` count values(Processes.user)
   OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest
   | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   |`create_local_admin_accounts_using_net_exe_filter` '
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: Administrators often leverage net.exe to create admin accounts.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -41,5 +45,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml b/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml
index 60020f3c53..e6a41509ee 100644
--- a/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml
+++ b/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml
@@ -1,26 +1,30 @@
-author: Bhavin Patel, Splunk
+name: Create or delete windows shares using net exe
+id: qw9919ed-fe5f-492c-b139-151bb162140e
+version: 5
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for the creation or deletion of hidden shares using
   net.exe.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: qw9919ed-fe5f-492c-b139-151bb162140e
-known_false_positives: Administrators often leverage net.exe to create or delete network
-  shares. You should verify that the activity was intentional and is legitimate.
-name: Create or delete windows shares using net exe
-references:
-- https://attack.mitre.org/techniques/T1070/005
 search: '| tstats `security_content_summariesonly` count values(Processes.user) as
   user values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe
   OR Processes.process_name=net1.exe) by Processes.process Processes.process_name
   Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` '
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: Administrators often leverage net.exe to create or delete network
+  shares. You should verify that the activity was intentional and is legitimate.
+references:
+- https://attack.mitre.org/techniques/T1070/005
 tags:
-  analytics_story:
+  analytic_story:
   - Hidden Cobra Malware
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/create_remote_thread_into_lsass.yml b/detections/endpoint/create_remote_thread_into_lsass.yml
index 6858be4760..f169cc8f10 100644
--- a/detections/endpoint/create_remote_thread_into_lsass.yml
+++ b/detections/endpoint/create_remote_thread_into_lsass.yml
@@ -1,24 +1,27 @@
-author: Patrick Bareiss, Splunk
+name: Create Remote Thread into LSASS
+id: 67d4dbef-9564-4699-8da8-03a151529edc
+version: 1
 date: '2019-12-06'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: Detect remote thread creation into LSASS consistent with credential dumping.
+search: '`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime
+  max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename
+  Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `create_remote_thread_into_lsass_filter`'
 how_to_implement: This search needs Sysmon Logs with a Sysmon configuration, which
   includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`.
   We strongly recommend that you specify your environment-specific configurations
   (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
   with configurations for your Splunk environment. The search also uses a post-filter
   macro designed to filter out known false positives.
-id: 67d4dbef-9564-4699-8da8-03a151529edc
 known_false_positives: Other tools can access LSASS for legitimate reasons and generate
   an event. In these cases, tweaking the search may help eliminate noise.
-name: Create Remote Thread into LSASS
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
-search: '`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime
-  max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename
-  Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `create_remote_thread_into_lsass_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Windows
   automated_detection_testing: passed
@@ -38,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml b/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml
index c8a7c97941..0cf5fe9399 100644
--- a/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml
+++ b/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml
@@ -2,43 +2,48 @@ name: Creation of lsass Dump with Taskmgr
 id: b2fbe95a-9c62-4c12-8a29-24b97e84c0cd
 version: 1
 date: '2020-02-03'
+author: Michael Haag, Splunk
+type: batch
+datamodel: []
 description: Detect the hands on keyboard behavior of Windows Task Manager creating
   a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification
   will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp,
   cannot be renamed, however if the dump occurs more than once, it will be named lsass
   (2).dmp.
+search: '`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp |
+  stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category,
+  process_name, TargetFilename | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter`'
 how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which
   includes EventCode 11 for detecting file create of lsass.dmp. This search uses an
   input macro named `sysmon`. We strongly recommend that you specify your environment-specific
   configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace
   the macro definition with configurations for your Splunk environment. The search
   also uses a post-filter macro designed to filter out known false positives.
-type: ESCU
+known_false_positives: Administrators can create memory dumps for debugging purposes,
+  but memory dumps of the LSASS process would be unusual.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager
 - https://attack.mitre.org/techniques/T1003/001/
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
-author: Michael Haag, Splunk
-search: '`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp |
-  stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category,
-  process_name, TargetFilename | rename Computer as dest | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter`'
-known_false_positives: Administrators can create memory dumps for debugging purposes,
-  but memory dumps of the LSASS process would be unusual.
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
-  mitre_attack_id:
-  - T1003.001
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Windows
+  automated_detection_testing: passed
   cis20:
   - CIS 6
   - CIS 8
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1003.001
   nist:
   - DE.CM
   security_domain: endpoint
-  asset_type: Windows
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/creation_of_shadow_copy.yml b/detections/endpoint/creation_of_shadow_copy.yml
index dac83e296a..1da256b8b4 100644
--- a/detections/endpoint/creation_of_shadow_copy.yml
+++ b/detections/endpoint/creation_of_shadow_copy.yml
@@ -2,16 +2,12 @@ name: Creation of Shadow Copy
 id: eb120f5f-b879-4a63-97c1-93352b5df844
 version: 1
 date: '2019-12-10'
-description: Monitor for signs that Vssadmin or Wmic has been used to create
-  a shadow copy.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints, to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-type: ESCU
-references:
-- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
+description: Monitor for signs that Vssadmin or Wmic has been used to create a shadow
+  copy.
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe
   Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe
@@ -19,22 +15,32 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   Processes.process_name Processes.process  Processes.parent_process Processes.process_id
   Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter`'
-known_false_positives: Legitimate administrator usage of Vssadmin or Wmic
-  will create false positives.
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints, to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: Legitimate administrator usage of Vssadmin or Wmic will create
+  false positives.
+references:
+- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
-  mitre_attack_id:
-  - T1003.003
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
   - CIS 16
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1003.003
   nist:
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  automated_detection_testing: passed
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml b/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml
index 91bcc9e672..4beda2d029 100644
--- a/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml
+++ b/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml
@@ -1,19 +1,26 @@
-author: Patrick Bareiss, Splunk
+name: Creation of Shadow Copy with wmic and powershell
+id: 2ed8b538-d284-449a-be1d-82ad1dbd186b
+version: 1
 date: '2019-12-10'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search detects the use of wmic and Powershell to create a shadow
   copy.
-id: 2ed8b538-d284-449a-be1d-82ad1dbd186b
-known_false_positives: Legtimate administrator usage of wmic to create a shadow copy.
-name: Creation of Shadow Copy with wmic and powershell
-references:
-- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic*
   OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create*
   by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Legtimate administrator usage of wmic to create a shadow copy.
+references:
+- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -33,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml b/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml
index 19d52b7160..19a4843bc4 100644
--- a/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml
+++ b/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml
@@ -1,16 +1,13 @@
-author: Patrick Bareiss, Splunk
+name: Credential Dumping via Copy Command from Shadow Copy
+id: d8c406fe-23d2-45f3-a983-1abe7b83ff3b
+version: 1
 date: '2019-12-10'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search detects credential dumping using copy command from a shadow
   copy.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-id: d8c406fe-23d2-45f3-a983-1abe7b83ff3b
-known_false_positives: unknown
-name: Credential Dumping via Copy Command from Shadow Copy
-references:
-- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe
   (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security*
@@ -18,8 +15,15 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process
   Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` '
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: unknown
+references:
+- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml b/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml
index c403b66a75..120f0c6a62 100644
--- a/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml
+++ b/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml
@@ -1,23 +1,27 @@
-author: Patrick Bareiss, Splunk
-date: '2019-12-10'
-description: This search detects the creation of a symlink to a shadow copy.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-id: c5eac648-fae0-4263-91a6-773df1f4c903
-known_false_positives: unknown
 name: Credential Dumping via Symlink to Shadow Copy
-references:
-- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+id: c5eac648-fae0-4263-91a6-773df1f4c903
+version: 1
+date: '2019-12-10'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
+description: This search detects the creation of a symlink to a shadow copy.
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe
   Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest
   Processes.user Processes.process_name Processes.process  Processes.parent_process
   Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter` '
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: unknown
+references:
+- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -37,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/deleting_shadow_copies.yml b/detections/endpoint/deleting_shadow_copies.yml
index 0473989d8b..30bc424e63 100644
--- a/detections/endpoint/deleting_shadow_copies.yml
+++ b/detections/endpoint/deleting_shadow_copies.yml
@@ -1,18 +1,14 @@
-author: David Dorsey, Splunk
+name: Deleting Shadow Copies
+id: b89919ed-ee5f-492c-b139-95dbb162039e
+version: 4
 date: '2020-11-09'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The vssadmin.exe utility is used to interact with the Volume Shadow Copy
   Service.  Wmic is an interface to the Windows Management Instrumentation.  This
   search looks for either of these tools being used to delete shadow copies.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-id: b89919ed-ee5f-492c-b139-95dbb162039e
-known_false_positives: vssadmin.exe and wmic.exe are standard applications shipped
-  with modern versions of windows. They may be used by administrators to legitimately
-  delete old backup copies, although this is typically rare.
-name: Deleting Shadow Copies
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe
@@ -20,8 +16,16 @@ search: '| tstats `security_content_summariesonly` count values(Processes.proces
   by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  |
   `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `deleting_shadow_copies_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: vssadmin.exe and wmic.exe are standard applications shipped
+  with modern versions of windows. They may be used by administrators to legitimately
+  delete old backup copies, although this is typically rare.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Log Manipulation
   - SamSam Ransomware
   - Ransomware
@@ -45,5 +49,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/detect_activity_related_to_pass_the_hash_attacks.yml b/detections/endpoint/detect_activity_related_to_pass_the_hash_attacks.yml
index 398a26605e..059103aaf6 100644
--- a/detections/endpoint/detect_activity_related_to_pass_the_hash_attacks.yml
+++ b/detections/endpoint/detect_activity_related_to_pass_the_hash_attacks.yml
@@ -1,21 +1,24 @@
-author: Bhavin Patel, Patrick Bareiss, Splunk
+name: Detect Activity Related to Pass the Hash Attacks
+id: f5939373-8054-40ad-8c64-cec478a22a4b
+version: 5
 date: '2020-10-15'
+author: Bhavin Patel, Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search looks for specific authentication events from the Windows
   Security Event logs to detect potential attempts at using the Pass-the-Hash technique.
-how_to_implement: To successfully implement this search, you must ingest your Windows
-  Security Event logs and leverage the latest TA for Windows.
-id: f5939373-8054-40ad-8c64-cec478a22a4b
-known_false_positives: Legitimate logon activity by authorized NTLM systems may be
-  detected by this search. Please investigate as appropriate.
-name: Detect Activity Related to Pass the Hash Attacks
-references: []
 search: '`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp
   WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9
   Logon_Process=seclogo) | fillnull | stats count min(_time) as firstTime max(_time)
   as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` '
+how_to_implement: To successfully implement this search, you must ingest your Windows
+  Security Event logs and leverage the latest TA for Windows.
+known_false_positives: Legitimate logon activity by authorized NTLM systems may be
+  detected by this search. Please investigate as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Lateral Movement
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -39,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 5
diff --git a/detections/endpoint/detect_computer_changed_with_anonymous_account.yml b/detections/endpoint/detect_computer_changed_with_anonymous_account.yml
index 4d855d0e38..acc30daf08 100644
--- a/detections/endpoint/detect_computer_changed_with_anonymous_account.yml
+++ b/detections/endpoint/detect_computer_changed_with_anonymous_account.yml
@@ -1,23 +1,26 @@
-author: Rod Soto, Jose Hernandez, Splunk
+name: Detect Computer Changed with Anonymous Account
+id: 1400624a-d42d-484d-8843-e6753e6e3645
+version: 1
 date: '2020-09-18'
+author: Rod Soto, Jose Hernandez, Splunk
+type: batch
+datamodel: []
 description: This search looks for Event Code 4742 (Computer Change) or EventCode
   4624 (An account was successfully logged on) with an anonymous account.
+search: '`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS
+  LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName)
+  as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter`'
 how_to_implement: This search requires audit computer account management to be enabled
   on the system in order to generate Event ID 4742. We strongly recommend that you
   specify your environment-specific configurations (index, source, sourcetype, etc.)
   for Windows Event Logs. Replace the macro definition with configurations for your
   Splunk environment. The search also uses a post-filter macro designed to filter
   out known false positives.
-id: 1400624a-d42d-484d-8843-e6753e6e3645
 known_false_positives: None thus far found
-name: Detect Computer Changed with Anonymous Account
 references:
 - https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/
-search: '`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS
-  LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName)
-  as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Detect Zerologon Attack
   asset_type: Windows
   cis20:
@@ -35,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/detect_credential_dumping_through_lsass_access.yml b/detections/endpoint/detect_credential_dumping_through_lsass_access.yml
index 9279a5baec..731bfccaa9 100644
--- a/detections/endpoint/detect_credential_dumping_through_lsass_access.yml
+++ b/detections/endpoint/detect_credential_dumping_through_lsass_access.yml
@@ -1,27 +1,30 @@
-author: Patrick Bareiss, Splunk
+name: Detect Credential Dumping through LSASS access
+id: 2c365e57-4414-4540-8dc0-73ab10729996
+version: 3
 date: '2019-12-03'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search looks for reading lsass memory consistent with credential
   dumping.
+search: '`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410)
+  | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage,
+  SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename
+  Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `detect_credential_dumping_through_lsass_access_filter` '
 how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which
   includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`.
   We strongly recommend that you specify your environment-specific configurations
   (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
   with configurations for your Splunk environment. The search also uses a post-filter
   macro designed to filter out known false positives.
-id: 2c365e57-4414-4540-8dc0-73ab10729996
 known_false_positives: The activity may be legitimate. Other tools can access lsass
   for legitimate reasons, and it's possible this event could be generated in those
   cases. In these cases, false positives should be fairly obvious and you may need
   to tweak the search to eliminate noise.
-name: Detect Credential Dumping through LSASS access
 references: []
-search: '`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410)
-  | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage,
-  SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename
-  Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `detect_credential_dumping_through_lsass_access_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   - Detect Zerologon Attack
   asset_type: Windows
@@ -45,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml b/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml
index 5105df5b8b..042c679c5f 100644
--- a/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml
+++ b/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml
@@ -1,7 +1,19 @@
-author: David Dorsey, Splunk
+name: Detect Excessive Account Lockouts From Endpoint
+id: c026e3dd-7e18-4abb-8f41-929e836efe74
+version: 5
 date: '2020-11-09'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search identifies endpoints that have caused a relatively high number
   of account lockouts in a short period.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where
+  nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest
+  All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`|
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search
+  count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter`'
 how_to_implement: "You must ingest your Windows security event logs in the `Change`\
   \ datamodel under the nodename is `Account_Management`, for this search to execute\
   \ successfully. Please consider updating the cron schedule and the count of lockouts\
@@ -16,18 +28,11 @@ how_to_implement: "You must ingest your Windows security event logs in the `Chan
   \ Actions when configuring this detection search, and set the corresponding Playbook\
   \ to active. \\\n(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\\\
   \n"
-id: c026e3dd-7e18-4abb-8f41-929e836efe74
 known_false_positives: It's possible that a widely used system, such as a kiosk, could
   cause a large number of account lockouts.
-name: Detect Excessive Account Lockouts From Endpoint
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where
-  nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest
-  All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`|
-  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search
-  count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter`'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Account Monitoring and Controls
   asset_type: Windows
   automated_detection_testing: passed
@@ -45,5 +50,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 5
diff --git a/detections/endpoint/detect_excessive_user_account_lockouts.yml b/detections/endpoint/detect_excessive_user_account_lockouts.yml
index c7397568f2..d2cc17c408 100644
--- a/detections/endpoint/detect_excessive_user_account_lockouts.yml
+++ b/detections/endpoint/detect_excessive_user_account_lockouts.yml
@@ -1,23 +1,27 @@
-author: David Dorsey, Splunk
+name: Detect Excessive User Account Lockouts
+id: 95a7f9a5-6096-437e-a19e-86f42ac609bd
+version: 3
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search detects user accounts that have been locked out a relatively
   high number of times in a short period.
-how_to_implement: ou must ingest your Windows security event logs in the `Change`
-  datamodel under the nodename is `Account_Management`, for this search to execute
-  successfully. Please consider updating the cron schedule and the count of lockouts
-  you want to monitor, according to your environment.
-id: 95a7f9a5-6096-437e-a19e-86f42ac609bd
-known_false_positives: It is possible that a legitimate user is experiencing an issue
-  causing multiple account login failures leading to lockouts.
-name: Detect Excessive User Account Lockouts
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management
   All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")`
   |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter`'
+how_to_implement: ou must ingest your Windows security event logs in the `Change`
+  datamodel under the nodename is `Account_Management`, for this search to execute
+  successfully. Please consider updating the cron schedule and the count of lockouts
+  you want to monitor, according to your environment.
+known_false_positives: It is possible that a legitimate user is experiencing an issue
+  causing multiple account login failures leading to lockouts.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Account Monitoring and Controls
   asset_type: Windows
   automated_detection_testing: passed
@@ -35,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 3
diff --git a/detections/endpoint/detect_mshta_inline_hta_execution.yml b/detections/endpoint/detect_mshta_inline_hta_execution.yml
index 190dd55b3f..1418a13549 100644
--- a/detections/endpoint/detect_mshta_inline_hta_execution.yml
+++ b/detections/endpoint/detect_mshta_inline_hta_execution.yml
@@ -1,21 +1,16 @@
-author: Bhavin Patel, Michael Haag, Splunk
+name: Detect mshta inline hta execution
+id: a0873b32-5b68-11eb-ae93-0242ac130002
+version: 5
 date: '2021-01-20'
+author: Bhavin Patel, Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies "mshta.exe" execution with inline protocol
   handlers. "JavaScript", "VBScript", and "About" are the only supported options when
   invoking HTA content directly on the command-line. The search will return the first
   time and last time these command-line arguments were used for these executions,
   as well as the target system, the user, process "mshta.exe" and its parent process.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-id: a0873b32-5b68-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, some legitimate applications may exhibit
-  this behavior, triggering a false positive.
-name: Detect mshta inline hta execution
-references:
-- https://github.com/redcanaryco/AtomicTestHarnesses
-- https://redcanary.com/blog/introducing-atomictestharnesses/
-- https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe
@@ -23,8 +18,17 @@ search: '| tstats `security_content_summariesonly` count values(Processes.proces
   by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  |
   `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `detect_mshta_inline_hta_execution_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
+references:
+- https://github.com/redcanaryco/AtomicTestHarnesses
+- https://redcanary.com/blog/introducing-atomictestharnesses/
+- https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious MSHTA Activity
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -44,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/detect_mshta_renamed.yml b/detections/endpoint/detect_mshta_renamed.yml
index c52e1462b5..eb067a7225 100644
--- a/detections/endpoint/detect_mshta_renamed.yml
+++ b/detections/endpoint/detect_mshta_renamed.yml
@@ -1,28 +1,31 @@
-author: Michael Haag, Splunk
+name: Detect mshta renamed
+id: 8f45fcf0-5b68-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-20'
+author: Michael Haag, Splunk
+type: batch
+datamodel: []
 description: The following analytic identifies renamed instances of mshta.exe executing.
   Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This
   analytic utilizes the internal name of the PE to identify if is the legitimate mshta
   binary. Further analysis should be performed to review the executed content and
   validation it is the real mshta.
-how_to_implement: To successfully implement this search, you need to be ingesting
-  logs with the process name, parent process, and command-line executions from your
-  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
-  Sysmon TA.
-id: 8f45fcf0-5b68-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, some legitimate applications may use a moved
-  copy of mshta.exe, but never renamed, triggering a false positive.
-name: Detect mshta renamed
-references:
-- https://github.com/redcanaryco/AtomicTestHarnesses
-- https://redcanary.com/blog/introducing-atomictestharnesses/
 search: '`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe)
   | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User,
   parent_process_name, process_name, OriginalFileName, process_path, CommandLine |
   rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
   `detect_mshta_renamed_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of mshta.exe, but never renamed, triggering a false positive.
+references:
+- https://github.com/redcanaryco/AtomicTestHarnesses
+- https://redcanary.com/blog/introducing-atomictestharnesses/
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious MSHTA Activity
   asset_type: Endpoint
   cis20:
@@ -41,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/detect_mshta_url_in_command_line.yml b/detections/endpoint/detect_mshta_url_in_command_line.yml
index d3146a70c4..7d03e42ed7 100644
--- a/detections/endpoint/detect_mshta_url_in_command_line.yml
+++ b/detections/endpoint/detect_mshta_url_in_command_line.yml
@@ -1,5 +1,11 @@
-author: Michael Haag, Splunk
+name: Detect MSHTA Url in Command Line
+id: 9b3af1e6-5b68-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-20'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This analytic identifies when Microsoft HTML Application Host (mshta.exe)
   utility is used to make remote http connections. Adversaries may use mshta.exe to
   proxy the download and execution of remote .hta files. The analytic identifies command
@@ -7,25 +13,23 @@ description: This analytic identifies when Microsoft HTML Application Host (msht
   malicious software to bypass preventative controls. The search will return the first
   time and last time these command-line arguments were used for these executions,
   as well as the target system, the user, process "rundll32.exe" and its parent process.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-id: 9b3af1e6-5b68-11eb-ae93-0242ac130002
-known_false_positives: It is possible legitimate applications may perform this behavior
-  and will need to be filtered.
-name: Detect MSHTA Url in Command Line
-references:
-- https://github.com/redcanaryco/AtomicTestHarnesses
-- https://redcanary.com/blog/introducing-atomictestharnesses/
-- https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe
   (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user
   Processes.process_name Processes.parent_process_name Processes.dest  | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: It is possible legitimate applications may perform this behavior
+  and will need to be filtered.
+references:
+- https://github.com/redcanaryco/AtomicTestHarnesses
+- https://redcanary.com/blog/introducing-atomictestharnesses/
+- https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious MSHTA Activity
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -45,5 +49,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/detect_new_local_admin_account.yml b/detections/endpoint/detect_new_local_admin_account.yml
index 8690119b02..0cb8e5f687 100644
--- a/detections/endpoint/detect_new_local_admin_account.yml
+++ b/detections/endpoint/detect_new_local_admin_account.yml
@@ -1,20 +1,25 @@
-author: David Dorsey, Splunk
+name: Detect New Local Admin account
+id: b25f6f62-0712-43c1-b203-083231ffd97d
+version: 2
 date: '2020-07-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for newly created accounts that have been elevated
   to local administrators.
-id: b25f6f62-0712-43c1-b203-083231ffd97d
-known_false_positives: The activity may be legitimate. For this reason, it's best
-  to verify the account with an administrator and ask whether there was a valid service
-  request for the account creation. If your local administrator group name is not
-  "Administrators", this search may generate an excessive number of false positives
-name: Detect New Local Admin account
-references: []
 search: '`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators)
   | transaction member_id connected=false maxspan=180m | rename member_id as user
   | stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter`'
+how_to_implement: You must be ingesting Windows event logs using the Splunk Windows
+  TA and collecting event code 4720 and 4732
+known_false_positives: The activity may be legitimate. For this reason, it's best
+  to verify the account with an administrator and ask whether there was a valid service
+  request for the account creation. If your local administrator group name is not
+  "Administrators", this search may generate an excessive number of false positives
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   asset_type: Windows
   automated_detection_testing: passed
@@ -37,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 2
diff --git a/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml b/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml
index 794d32c88a..6c2692c202 100644
--- a/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml
+++ b/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml
@@ -1,13 +1,14 @@
-author: Patrick Bareiss, Splunk
+name: Detect Path Interception By Creation Of program exe
+id: c77162d3-f93c-45cc-80c8-22f6v5264g9f
+version: 3
 date: '2020-07-03'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: 'The detection Detect Path Interception By Creation Of program exe is
   detecting the abuse of unquoted service paths, which is a popular technique for
   privilege escalation. '
-id: c77162d3-f93c-45cc-80c8-22f6v5264g9f
-known_false_positives: unknown
-name: Detect Path Interception By Creation Of program exe
-references:
-- https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe
   by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)`
@@ -15,8 +16,12 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   process_name = lower(process_name) | eval service_process = lower(service_process)|
   where process_name != service_process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `detect_path_interception_by_creation_of_program_exe_filter`'
+how_to_implement: ''
+known_false_positives: unknown
+references:
+- https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Persistence Techniques
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -36,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/detect_processes_used_for_system_network_configuration_discovery.yml b/detections/endpoint/detect_processes_used_for_system_network_configuration_discovery.yml
index f005ec4db8..c1e2159084 100644
--- a/detections/endpoint/detect_processes_used_for_system_network_configuration_discovery.yml
+++ b/detections/endpoint/detect_processes_used_for_system_network_configuration_discovery.yml
@@ -1,19 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Detect processes used for System Network Configuration Discovery
+id: a51bfe1a-94f0-48cc-b1e4-16ae10145893
+version: 2
 date: '2020-11-10'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for fast execution of processes used for system network
   configuration discovery on the endpoint.
-how_to_implement: You must be ingesting data that records registry activity from your
-  hosts to populate the Endpoint data model in the processes node. This is typically
-  populated via endpoint detection-and-response products, such as Carbon Black, or
-  endpoint data sources, such as Sysmon. The data used for this search is usually
-  generated via logs that report reads and writes to the registry or that are populated
-  via Windows event logs, after enabling process tracking in your Windows audit settings.
-id: a51bfe1a-94f0-48cc-b1e4-16ae10145893
-known_false_positives: It is uncommon for normal users to execute a series of commands
-  used for network discovery. System administrators often use scripts to execute these
-  commands. These can generate false positives.
-name: Detect processes used for System Network Configuration Discovery
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name
@@ -21,8 +15,18 @@ search: '| tstats `security_content_summariesonly` count values(Processes.proces
   | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools`
   | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime
   lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter`'
+how_to_implement: You must be ingesting data that records registry activity from your
+  hosts to populate the Endpoint data model in the processes node. This is typically
+  populated via endpoint detection-and-response product, such as Carbon Black, or
+  endpoint data sources, such as Sysmon. The data used for this search is usually
+  generated via logs that report reads and writes to the registry or that are populated
+  via Windows event logs, after enabling process tracking in your Windows audit settings.
+known_false_positives: It is uncommon for normal users to execute a series of commands
+  used for network discovery. System administrators often use scripts to execute these
+  commands. These can generate false positives.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Unusual Processes
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -44,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml b/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml
index f2e1899160..509d2c5873 100644
--- a/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml
+++ b/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml
@@ -1,25 +1,29 @@
-author: Bhavin Patel, Splunk
+name: Detect Prohibited Applications Spawning cmd exe
+id: dcfd6b40-42f9-469d-a433-2e53f7486664
+version: 5
 date: '2020-11-10'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for executions of cmd.exe spawned by a process that
   is often abused by attackers and that does not typically launch cmd.exe.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts and populates the Endpoint data model with the resultant dataset. This search
-  includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list
-  of processes that should not be spawning cmd.exe. You can modify this lookup to
-  better suit your environment.
-id: dcfd6b40-42f9-469d-a433-2e53f7486664
-known_false_positives: There are circumstances where an application may legitimately
-  execute and interact with the Windows command-line interface. Investigate and modify
-  the lookup file, as appropriate.
-name: Detect Prohibited Applications Spawning cmd exe
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name
   Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts and populates the Endpoint data model with the resultant dataset. This search
+  includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list
+  of processes that should not be spawning cmd.exe. You can modify this lookup to
+  better suit your environment.
+known_false_positives: There are circumstances where an application may legitimately
+  execute and interact with the Windows command-line interface. Investigate and modify
+  the lookup file, as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Command-Line Executions
   - Suspicious MSHTA Activity
   - Suspicious Zoom Child Processes
@@ -42,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/detect_psexec_with_accepteula_flag.yml b/detections/endpoint/detect_psexec_with_accepteula_flag.yml
index 403a31998f..1750a7346a 100644
--- a/detections/endpoint/detect_psexec_with_accepteula_flag.yml
+++ b/detections/endpoint/detect_psexec_with_accepteula_flag.yml
@@ -1,5 +1,11 @@
-author: Bhavin Patel, Splunk
+name: Detect PsExec With accepteula Flag
+id: b89919ed-fe5f-492c-b139-151xb162040e
+version: 3
 date: '2020-11-10'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for events where `PsExec.exe` is run with the `accepteula`
   flag in the command line. PsExec is a built-in Windows utility that enables you
   to execute processes on other systems. It is fully interactive for console applications.
@@ -8,25 +14,23 @@ description: This search looks for events where `PsExec.exe` is run with the `ac
   If an attacker is running PsExec for the first time, they will be prompted to accept
   the end-user license agreement (EULA), which can be passed as the argument `accepteula`
   within the command line.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: b89919ed-fe5f-492c-b139-151xb162040e
-known_false_positives: Administrators can leverage PsExec for accessing remote systems
-  and might pass `accepteula` as an argument if they are running this tool for the
-  first time. However, it is not likely that you'd see multiple occurrences of this
-  event on a machine
-name: Detect PsExec With accepteula Flag
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process) as process
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name
   Processes.dest  Processes.parent_process_name | `drop_dm_object_name(Processes)`|
   `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: Administrators can leverage PsExec for accessing remote systems
+  and might pass `accepteula` as an argument if they are running this tool for the
+  first time. However, it is not likely that you'd see multiple occurrences of this
+  event on a machine
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   - DHS Report TA18-074A
   asset_type: Endpoint
@@ -47,5 +51,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/detect_rare_executables.yml b/detections/endpoint/detect_rare_executables.yml
index f321d3668c..edb7bb1b93 100644
--- a/detections/endpoint/detect_rare_executables.yml
+++ b/detections/endpoint/detect_rare_executables.yml
@@ -1,7 +1,20 @@
-author: Bhavin Patel, Splunk
+name: Detect Rare Executables
+id: 44fddcb2-8d3b-454c-874e-7c6de5a4f7ac
+version: 5
 date: '2020-03-16'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search will return a table of rare processes, the names of the systems
   running them, and the users who initiated each process.
+search: '| tstats `security_content_summariesonly` count values(Processes.dest) as
+  dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime
+  from datamodel=Endpoint.Processes by Processes.process_name  | rename Processes.process_name
+  as process | rex field=user "(?<user_domain>.*)\\\\(?<user_name>.*)" | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes
+  by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name
+  as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_filter` '
 how_to_implement: To successfully implement this search, you must be ingesting data
   that records process activity from your hosts and populating the endpoint data model
   with the resultant dataset. The macro `filter_rare_process_allow_list` searches
@@ -11,21 +24,12 @@ how_to_implement: To successfully implement this search, you must be ingesting d
   entry from the default lookup file, you will have to modify the macro itself to
   set the allow_list value for that process to false. You can modify the limit parameter
   and search scheduling to better suit your environment.
-id: 44fddcb2-8d3b-454c-874e-7c6de5a4f7ac
 known_false_positives: Some legitimate processes may be only rarely executed in your
   environment. As these are identified, update `rare_process_allow_list_local.csv`
   to filter them out of your search results.
-name: Detect Rare Executables
 references: []
-search: '| tstats `security_content_summariesonly` count values(Processes.dest) as
-  dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime
-  from datamodel=Endpoint.Processes by Processes.process_name  | rename Processes.process_name
-  as process | rex field=user "(?<user_domain>.*)\\\\(?<user_name>.*)" | `security_content_ctime(firstTime)`|
-  `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes
-  by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name
-  as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Unusual Processes
   - Cloud Federated Credential Abuse
@@ -47,5 +51,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/detect_regsvr32_application_control_bypass.yml b/detections/endpoint/detect_regsvr32_application_control_bypass.yml
index 1a7ab59f8b..8fe28bc168 100644
--- a/detections/endpoint/detect_regsvr32_application_control_bypass.yml
+++ b/detections/endpoint/detect_regsvr32_application_control_bypass.yml
@@ -2,6 +2,10 @@ name: Detect Regsvr32 Application Control Bypass
 id: 070e9b80-6252-11eb-ae93-0242ac130002
 version: 1
 date: '2021-01-28'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: 'Adversaries may abuse Regsvr32.exe to proxy execution of malicious code.
   Regsvr32.exe is a command-line program used to register and unregister object linking
   and embedding controls, including dynamic link libraries (DLLs), on Windows systems.
@@ -12,39 +16,41 @@ description: 'Adversaries may abuse Regsvr32.exe to proxy execution of malicious
   or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll"
   is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon
   execution. '
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints, to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe.
-type: ESCU
-references:
-- https://attack.mitre.org/techniques/T1218/010/
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
-- https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/
-- https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5
-author: Michael Haag, Splunk
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe
   OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest
   Processes.user Processes.parent_process Processes.process_name Processes.process
   Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints, to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe.
 known_false_positives: Limited false positives related to third party software registering
   .DLL's.
+references:
+- https://attack.mitre.org/techniques/T1218/010/
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
+- https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/
+- https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Regsvr32 Activity
-  mitre_attack_id:
-  - T1218.010
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
   - CIS 16
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1218.010
   nist:
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/detect_rundll32_application_control_bypass___advpack.yml b/detections/endpoint/detect_rundll32_application_control_bypass___advpack.yml
index 179bbb3187..cbce008255 100644
--- a/detections/endpoint/detect_rundll32_application_control_bypass___advpack.yml
+++ b/detections/endpoint/detect_rundll32_application_control_bypass___advpack.yml
@@ -2,46 +2,53 @@ name: Detect Rundll32 Application Control Bypass - advpack
 id: 4aefadfe-9abd-4bf8-b3fd-867e9ef95bf8
 version: 1
 date: '2021-02-04'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies rundll32.exe loading advpack.dll and
-  ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique
-  will load script code from a file. Upon a successful execution, the following module
-  loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify
-  script content origination. Generally, a child process will spawn from rundll32.exe,
-  but that may be bypassed based on script code contents. Rundll32.exe is natively
-  found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any
-  network connections and obtain the script content executed. It's possible other files are on disk.
+  ieadvpack.dll by calling the LaunchINFSection function on the command line. This
+  particular technique will load script code from a file. Upon a successful execution,
+  the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During
+  investigation, identify script content origination. Generally, a child process will
+  spawn from rundll32.exe, but that may be bypassed based on script code contents.
+  Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During
+  investigation, review any network connections and obtain the script content executed.
+  It's possible other files are on disk.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
+  Processes.process=*advpack* by Processes.user Processes.process_name Processes.process
+  Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___advpack_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node.
-type: ESCU
+known_false_positives: Although unlikely, some legitimate applications may use advpack.dll
+  or ieadvpack.dll, triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
 - https://lolbas-project.github.io/lolbas/Binaries/Rundll32
 - https://lolbas-project.github.io/lolbas/Libraries/Advpack/
 - https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
-author: Michael Haag, Splunk
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
-  Processes.process=*advpack* by Processes.user Processes.process_name
-  Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)`
-  | `detect_rundll32_application_control_bypass___advpack_filter`'
-known_false_positives: Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. 
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Rundll32 Activity
-  mitre_attack_id:
-  - T1218.011
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1218.011
   nist:
   - PR.PT
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/detect_rundll32_application_control_bypass___setupapi.yml b/detections/endpoint/detect_rundll32_application_control_bypass___setupapi.yml
index 6e28a0a422..b1c6165425 100644
--- a/detections/endpoint/detect_rundll32_application_control_bypass___setupapi.yml
+++ b/detections/endpoint/detect_rundll32_application_control_bypass___setupapi.yml
@@ -2,6 +2,10 @@ name: Detect Rundll32 Application Control Bypass - setupapi
 id: 61e7b44a-6088-4f26-b788-9a96ba13b37a
 version: 1
 date: '2021-02-04'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies rundll32.exe loading setupapi.dll and
   iesetupapi.dll by calling the LaunchINFSection function on the command line. This
   particular technique will load script code from a file. Upon a successful execution,
@@ -11,38 +15,40 @@ description: The following analytic identifies rundll32.exe loading setupapi.dll
   Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During
   investigation, review any network connections and obtain the script content executed.
   It's possible other files are on disk.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
+  Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process
+  Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node.
-type: ESCU
+known_false_positives: Although unlikely, some legitimate applications may use setupapi
+  triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
 - https://lolbas-project.github.io/lolbas/Binaries/Rundll32
 - https://lolbas-project.github.io/lolbas/Libraries/Setupapi/
 - https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
-author: Michael Haag, Splunk
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
-  Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process
-  Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter`'
-known_false_positives: Although unlikely, some legitimate applications may use setupapi
-  triggering a false positive.
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Rundll32 Activity
-  mitre_attack_id:
-  - T1218.011
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1218.011
   nist:
   - PR.PT
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/detect_rundll32_application_control_bypass___syssetup.yml b/detections/endpoint/detect_rundll32_application_control_bypass___syssetup.yml
index de2b4b6ce6..ba2468553e 100644
--- a/detections/endpoint/detect_rundll32_application_control_bypass___syssetup.yml
+++ b/detections/endpoint/detect_rundll32_application_control_bypass___syssetup.yml
@@ -2,6 +2,10 @@ name: Detect Rundll32 Application Control Bypass - syssetup
 id: 71b9bf37-cde1-45fb-b899-1b0aa6fa1183
 version: 1
 date: '2021-02-04'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies rundll32.exe loading syssetup.dll by
   calling the LaunchINFSection function on the command line. This particular technique
   will load script code from a file. Upon a successful execution, the following module
@@ -11,38 +15,40 @@ description: The following analytic identifies rundll32.exe loading syssetup.dll
   found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review
   any network connections and obtain the script content executed. It's possible other
   files are on disk.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
+  Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process
+  Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node.
-type: ESCU
+known_false_positives: Although unlikely, some legitimate applications may use syssetup.dll,
+  triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
 - https://lolbas-project.github.io/lolbas/Binaries/Rundll32
 - https://lolbas-project.github.io/lolbas/Libraries/Syssetup/
 - https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
-author: Michael Haag, Splunk
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
-  Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process
-  Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter`'
-known_false_positives: Although unlikely, some legitimate applications may use syssetup.dll,
-  triggering a false positive.
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Rundll32 Activity
-  mitre_attack_id:
-  - T1218.011
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1218.011
   nist:
   - PR.PT
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/detect_rundll32_inline_hta_execution.yml b/detections/endpoint/detect_rundll32_inline_hta_execution.yml
index 296da3b451..0bf9d3d37a 100644
--- a/detections/endpoint/detect_rundll32_inline_hta_execution.yml
+++ b/detections/endpoint/detect_rundll32_inline_hta_execution.yml
@@ -1,5 +1,11 @@
-author: Michael Haag, Splunk
+name: Detect Rundll32 Inline HTA Execution
+id: 91c79f14-5b41-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-20'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies "rundll32.exe" execution with inline
   protocol handlers. "JavaScript", "VBScript", and "About" are the only supported
   options when invoking HTA content directly on the command-line. This type of behavior
@@ -7,17 +13,6 @@ description: The following analytic identifies "rundll32.exe" execution with inl
   The search will return the first time and last time these command-line arguments
   were used for these executions, as well as the target system, the user, process
   "rundll32.exe" and its parent process.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-id: 91c79f14-5b41-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, some legitimate applications may exhibit
-  this behavior, triggering a false positive.
-name: Detect Rundll32 Inline HTA Execution
-references:
-- https://github.com/redcanaryco/AtomicTestHarnesses
-- https://redcanary.com/blog/introducing-atomictestharnesses/
-- https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
@@ -25,8 +20,17 @@ search: '| tstats `security_content_summariesonly` count values(Processes.proces
   by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  |
   `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `detect_rundll32_inline_hta_execution_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
+references:
+- https://github.com/redcanaryco/AtomicTestHarnesses
+- https://redcanary.com/blog/introducing-atomictestharnesses/
+- https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious MSHTA Activity
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -46,5 +50,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml b/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml
index cc192e5550..d2323a96f4 100644
--- a/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml
+++ b/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml
@@ -1,25 +1,29 @@
-author: Bhavin Patel, Splunk
+name: Detect Use of cmd exe to Launch Script Interpreters
+id: b89919ed-fe5f-492c-b139-95dbb162039e
+version: 4
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for the execution of the cscript.exe or wscript.exe
   processes, with a parent of cmd.exe. The search will return the count, the first
   and last time this execution was seen on a machine, the user, and the destination
   of the machine
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records process activity from your hosts to populate the endpoint data model
-  in the processes node. If you are using Sysmon, you must have at least version 6.0.4
-  of the Sysmon TA.
-id: b89919ed-fe5f-492c-b139-95dbb162039e
-known_false_positives: Some legitimate applications may exhibit this behavior.
-name: Detect Use of cmd exe to Launch Script Interpreters
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe
   OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name
   Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
   | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records process activity from your hosts to populate the endpoint data model
+  in the processes node. If you are using Sysmon, you must have at least version 6.0.4
+  of the Sysmon TA.
+known_false_positives: Some legitimate applications may exhibit this behavior.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Suspicious Command-Line Executions
   asset_type: Endpoint
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/disabling_remote_user_account_control.yml b/detections/endpoint/disabling_remote_user_account_control.yml
index 84d29cc4a1..42a31fc477 100644
--- a/detections/endpoint/disabling_remote_user_account_control.yml
+++ b/detections/endpoint/disabling_remote_user_account_control.yml
@@ -1,25 +1,28 @@
-author: David Dorsey, Patrick Bareiss, Splunk
+name: Disabling Remote User Account Control
+id: bbc644bc-37df-4e1a-9c88-ec9a53e2038c
+version: 4
 date: '2020-11-18'
+author: David Dorsey, Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: The search looks for modifications to registry keys that control the
   enforcement of Windows User Account Control (UAC).
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records registry activity from your hosts to populate the endpoint data model
-  in the registry node. This is typically populated via endpoint detection-and-response
-  products, such as Carbon Black, or via other endpoint data sources, such as Sysmon.
-  The data used for this search is typically generated via logs that report registry
-  modifications.
-id: bbc644bc-37df-4e1a-9c88-ec9a53e2038c
-known_false_positives: This registry key may be modified via administrators to implement
-  a change in system policy. This type of change should be a very rare occurrence.
-name: Disabling Remote User Account Control
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA*
   Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name
   Registry.user Registry.registry_path Registry.registry_value_name Registry.action
   | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black, or via other endpoint data sources, such as Sysmon.
+  The data used for this search is typically generated via logs that report registry
+  modifications.
+known_false_positives: This registry key may be modified via administrators to implement
+  a change in system policy. This type of change should be a very rare occurrence.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Defense Evasion Tactics
   - Suspicious Windows Registry Activities
   asset_type: Endpoint
@@ -40,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/dump_lsass_via_comsvcs_dll.yml b/detections/endpoint/dump_lsass_via_comsvcs_dll.yml
index 55763d0c30..f91f46fca9 100644
--- a/detections/endpoint/dump_lsass_via_comsvcs_dll.yml
+++ b/detections/endpoint/dump_lsass_via_comsvcs_dll.yml
@@ -2,38 +2,44 @@ name: Dump LSASS via comsvcs DLL
 id: 8943b567-f14d-4ee8-a0bb-2121d4ce3184
 version: 1
 date: '2020-02-21'
-description: Detect the usage of comsvcs.dll for dumping the lsass process.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints, to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-type: ESCU
-references:
-- https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/
-- https://twitter.com/SBousseaden/status/1167417096374050817
 author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
+description: Detect the usage of comsvcs.dll for dumping the lsass process.
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
   Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name
   Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints, to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
 known_false_positives: None identified.
+references:
+- https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/
+- https://twitter.com/SBousseaden/status/1167417096374050817
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   - Suspicious Rundll32 Activity
-  mitre_attack_id:
-  - T1003.001
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 3
   - CIS 5
   - CIS 16
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1003.001
   nist:
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  automated_detection_testing: passed
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/dump_lsass_via_procdump.yml b/detections/endpoint/dump_lsass_via_procdump.yml
index cbb385c1ca..d1e67bcf07 100644
--- a/detections/endpoint/dump_lsass_via_procdump.yml
+++ b/detections/endpoint/dump_lsass_via_procdump.yml
@@ -2,45 +2,52 @@ name: Dump LSASS via procdump
 id: 3742ebfe-64c2-11eb-ae93-0242ac130002
 version: 1
 date: '2021-02-01'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: 'Detect procdump.exe dumping the lsass process. This query looks for
   both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump
   file with all process memory. Both are highly suspect and should be reviewed. This
-  query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe.
-  Modify the query as needed.\
+  query does not monitor for the internal name (OriginalFileName=procdump) of the
+  PE or look for procdump64.exe. Modify the query as needed.\
 
   During triage, confirm this is procdump.exe executing. If it is the first time a
-  Sysinternals utility has been ran, it is possible there will be a -accepteula on the command
-  line. Review other endpoint data sources for cross process (injection) into lsass.exe.'
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-type: ESCU
-references:
-- https://attack.mitre.org/techniques/T1003/001/
-- https://docs.microsoft.com/en-us/sysinternals/downloads/procdump
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump
-author: Michael Haag, Splunk
+  Sysinternals utility has been ran, it is possible there will be a -accepteula on
+  the command line. Review other endpoint data sources for cross process (injection)
+  into lsass.exe.'
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe
   (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by
   Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
 known_false_positives: None identified.
+references:
+- https://attack.mitre.org/techniques/T1003/001/
+- https://docs.microsoft.com/en-us/sysinternals/downloads/procdump
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
-  mitre_attack_id:
-  - T1003.001
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 3
   - CIS 5
   - CIS 16
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1003.001
   nist:
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/dump_lsass_via_procdump_rename.yml b/detections/endpoint/dump_lsass_via_procdump_rename.yml
index c42b56ca30..44ee9b9808 100644
--- a/detections/endpoint/dump_lsass_via_procdump_rename.yml
+++ b/detections/endpoint/dump_lsass_via_procdump_rename.yml
@@ -2,6 +2,9 @@ name: Dump LSASS via procdump Rename
 id: 21276daa-663d-11eb-ae93-0242ac130002
 version: 1
 date: '2021-02-01'
+author: Michael Haag, Splunk
+type: batch
+datamodel: []
 description: 'Detect a renamed instance of procdump.exe dumping the lsass process.
   This query looks for both -mm and -ma usage. -mm will produce a mini dump file and
   -ma will write a dump file with all process memory. Both are highly suspect and
@@ -11,36 +14,38 @@ description: 'Detect a renamed instance of procdump.exe dumping the lsass proces
   Sysinternals utility has been ran, it is possible there will be a -accepteula on
   the command line. Review other endpoint data sources for cross process (injection)
   into lsass.exe.'
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-type: ESCU
-references:
-- https://attack.mitre.org/techniques/T1003/001/
-- https://docs.microsoft.com/en-us/sysinternals/downloads/procdump
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump
-author: Michael Haag, Splunk
 search: '`sysmon` OriginalFileName=procdump  process_name!=procdump*.exe  EventID=1
   (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* | rename Computer as
   dest |  stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name,
   process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
 known_false_positives: None identified.
+references:
+- https://attack.mitre.org/techniques/T1003/001/
+- https://docs.microsoft.com/en-us/sysinternals/downloads/procdump
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
-  mitre_attack_id:
-  - T1003.001
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 3
   - CIS 5
   - CIS 16
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1003.001
   nist:
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/execution_of_file_with_multiple_extensions.yml b/detections/endpoint/execution_of_file_with_multiple_extensions.yml
index c5caf51394..633473b006 100644
--- a/detections/endpoint/execution_of_file_with_multiple_extensions.yml
+++ b/detections/endpoint/execution_of_file_with_multiple_extensions.yml
@@ -1,24 +1,28 @@
-author: Rico Valdez, Splunk
+name: Execution of File with Multiple Extensions
+id: b06a555e-dce0-417d-a2eb-28a5d8d66ef7
+version: 3
 date: '2020-11-18'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for processes launched from files that have double
   extensions in the file name. This is typically done to obscure the "real" file extension
   and make it appear as though the file being accessed is a data file, as opposed
   to executable content.
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records process activity from your hosts to populate the endpoint data model
-  in the processes node.
-id: b06a555e-dce0-417d-a2eb-28a5d8d66ef7
-known_false_positives: None identified.
-name: Execution of File with Multiple Extensions
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe
   OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process
   = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by
   Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records process activity from your hosts to populate the endpoint data model
+  in the processes node.
+known_false_positives: None identified.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows File Extension and Association Abuse
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/file_with_samsam_extension.yml b/detections/endpoint/file_with_samsam_extension.yml
index 45c348bc19..1f28151793 100644
--- a/detections/endpoint/file_with_samsam_extension.yml
+++ b/detections/endpoint/file_with_samsam_extension.yml
@@ -1,16 +1,13 @@
-author: Rico Valdez, Splunk
+name: File with Samsam Extension
+id: 02c6cfc2-ae66-4735-bfc7-6291da834cbf
+version: 1
 date: '2018-12-14'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks for file writes with extensions consistent with a SamSam
   ransomware attack.
-how_to_implement: You must be ingesting data that records file-system activity from
-  your hosts to populate the Endpoint file-system data-model node. If you are using
-  Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you
-  want to collect data.
-id: 02c6cfc2-ae66-4735-bfc7-6291da834cbf
-known_false_positives: Because these extensions are not typically used in normal operations,
-  you should investigate all results.
-name: File with Samsam Extension
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path)
   as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)`
@@ -18,8 +15,15 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   field=file_name "(?<file_extension>\.[^\.]+)$" | search file_extension=.stubbin
   OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos
   OR file_extension=.keyxml | `file_with_samsam_extension_filter`'
+how_to_implement: You must be ingesting data that records file-system activity from
+  your hosts to populate the Endpoint file-system data-model node. If you are using
+  Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you
+  want to collect data.
+known_false_positives: Because these extensions are not typically used in normal operations,
+  you should investigate all results.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -37,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/first_time_seen_child_process_of_zoom.yml b/detections/endpoint/first_time_seen_child_process_of_zoom.yml
index 1d182c7181..ed2f96bb2f 100644
--- a/detections/endpoint/first_time_seen_child_process_of_zoom.yml
+++ b/detections/endpoint/first_time_seen_child_process_of_zoom.yml
@@ -1,21 +1,13 @@
-author: David Dorsey, Splunk
+name: First Time Seen Child Process of Zoom
+id: e91bd102-d630-4e76-ab73-7e3ba22c5961
+version: 1
 date: '2020-05-20'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for child processes spawned by zoom.exe or zoom.us
   that has not previously been seen.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You should run
-  the baseline search `Previously Seen Zoom Child Processes - Initial` to build the
-  initial table of child processes and hostnames for this search to work. You should
-  also schedule at the same interval as this search the second baseline search `Previously
-  Seen Zoom Child Processes - Update` to keep this table up to date and to age out
-  old child processes. Please update the `previously_seen_zoom_child_processes_window`
-  macro to adjust the time window.
-id: e91bd102-d630-4e76-ab73-7e3ba22c5961
-known_false_positives: A new child process of zoom isn't malicious by that fact alone.
-  Further investigation of the actions of the child process is needed to verify any
-  malicious behavior is taken.
-name: First Time Seen Child Process of Zoom
-references: []
 search: '| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name)
   as parent_process_name values(Processes.parent_process_id) as parent_process_id
   values(Processes.process_name) as process_name values(Processes.process) as process
@@ -26,8 +18,20 @@ search: '| tstats `security_content_summariesonly` min(_time) as firstTime value
   OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`")
   | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name,
   parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You should run
+  the baseline search `Previously Seen Zoom Child Processes - Initial` to build the
+  initial table of child processes and hostnames for this search to work. You should
+  also schedule at the same interval as this search the second baseline search `Previously
+  Seen Zoom Child Processes - Update` to keep this table up to date and to age out
+  old child processes. Please update the `previously_seen_zoom_child_processes_window`
+  macro to adjust the time window.
+known_false_positives: A new child process of zoom isn't malicious by that fact alone.
+  Further investigation of the actions of the child process is needed to verify any
+  malicious behavior is taken.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Zoom Child Processes
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -49,5 +53,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml b/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml
index c333e56c94..04875404b5 100644
--- a/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml
+++ b/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml
@@ -1,26 +1,30 @@
-author: Bhavin Patel, Splunk
+name: Hiding Files And Directories With Attrib exe
+id: c77162d3-f93c-45cc-80c8-22f6b5264g9f
+version: 4
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: Attackers leverage an existing Windows binary, attrib.exe, to mark specific
   as hidden by using specific flags so that the victim does not see the file.  The
   search looks for specific command-line arguments to detect the use of attrib.exe
   to hide files.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: c77162d3-f93c-45cc-80c8-22f6b5264g9f
-known_false_positives: 'Some applications and users may legitimately use attrib.exe
-  to interact with the files. '
-name: Hiding Files And Directories With Attrib exe
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process)
   as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe
   (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user
   Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`|
   `hiding_files_and_directories_with_attrib_exe_filter` '
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: 'Some applications and users may legitimately use attrib.exe
+  to interact with the files. '
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Defense Evasion Tactics
   - Windows Persistence Techniques
   asset_type: ''
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml b/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml
index f5436ed36d..68e3409ef1 100644
--- a/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml
+++ b/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml
@@ -1,22 +1,25 @@
-author: Jose Hernandez, Patrick Bareiss, Splunk
+name: Kerberoasting spn request with RC4 encryption
+id: 5cc67381-44fa-4111-8a37-7a230943f027
+version: 3
 date: '2020-10-16'
+author: Jose Hernandez, Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search detects a potential kerberoasting attack via service principal
   name requests
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  and include the windows security event logs that contain kerberos
-id: 5cc67381-44fa-4111-8a37-7a230943f027
-known_false_positives: Older systems that support kerberos RC4 by default NetApp may
-  generate false positives
-name: Kerberoasting spn request with RC4 encryption
-references:
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md
-- https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity
 search: '`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17
   | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id,
   Ticket_Encryption_Type, Ticket_Options | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   | `kerberoasting_spn_request_with_rc4_encryption_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  and include the windows security event logs that contain kerberos
+known_false_positives: Older systems that support kerberos RC4 by default NetApp may
+  generate false positives
+references:
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md
+- https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity
 tags:
-  analytics_story:
+  analytic_story:
   - Lateral Movement
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -36,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/malicious_powershell_process___connect_to_internet_with_hidden_window.yml b/detections/endpoint/malicious_powershell_process___connect_to_internet_with_hidden_window.yml
index 152f1c15ff..1ca64442d6 100644
--- a/detections/endpoint/malicious_powershell_process___connect_to_internet_with_hidden_window.yml
+++ b/detections/endpoint/malicious_powershell_process___connect_to_internet_with_hidden_window.yml
@@ -1,21 +1,17 @@
-author: David Dorsey, Splunk
+name: Malicious PowerShell Process - Connect To Internet With Hidden Window
+id: ee18ed37-0802-4268-9435-b3b91aaa18db
+version: 5
 date: '2020-11-20'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for PowerShell processes started with parameters to
   modify the execution policy of the run, run in a hidden window, and connect to the
   Internet. This combination of command-line options is suspicious because it's overriding
   the default PowerShell execution policy, attempts to hide its activity from the
   user, and connects to the Internet. Deprecated becaue hidden is not needed when
   download file with System.Net.WebClient.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: ee18ed37-0802-4268-9435-b3b91aaa18db
-known_false_positives: Legitimate process can have this combination of command-line
-  options, but it's not common.
-name: Malicious PowerShell Process - Connect To Internet With Hidden Window
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe
@@ -23,8 +19,16 @@ search: '| tstats `security_content_summariesonly` count values(Processes.proces
   by Processes.user Processes.process_name Processes.parent_process_name Processes.process
   Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: Legitimate process can have this combination of command-line
+  options, but it's not common.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Malicious PowerShell
   - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
   asset_type: Endpoint
@@ -49,5 +53,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/malicious_powershell_process___encoded_command.yml b/detections/endpoint/malicious_powershell_process___encoded_command.yml
index 01eb1bf5fc..ae5c7cde68 100644
--- a/detections/endpoint/malicious_powershell_process___encoded_command.yml
+++ b/detections/endpoint/malicious_powershell_process___encoded_command.yml
@@ -1,25 +1,29 @@
-author: David Dorsey, Splunk
+name: Malicious PowerShell Process - Encoded Command
+id: c4db14d9-7909-48b4-a054-aa14d89dbb19
+version: 4
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for PowerShell processes that have encoded the script
   within the command-line. Malware has been seen using this parameter, as it obfuscates
   the code and makes it relatively easy to pass a script on the command-line.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: c4db14d9-7909-48b4-a054-aa14d89dbb19
-known_false_positives: System administrators may use this option, but it's not common.
-name: Malicious PowerShell Process - Encoded Command
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe
   (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user
   Processes.process_name Processes.process Processes.parent_process_name Processes.dest
   Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `malicious_powershell_process___encoded_command_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: System administrators may use this option, but it's not common.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Malicious PowerShell
   - Sunburst Malware
   asset_type: Endpoint
@@ -44,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml b/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml
index e72c322914..ce4c1b25d8 100644
--- a/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml
+++ b/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml
@@ -1,28 +1,32 @@
-author: Rico Valdez, Splunk
+name: Malicious PowerShell Process - Execution Policy Bypass
+id: 9be56c82-b1cc-4318-87eb-d138afaaca39
+version: 4
 date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for PowerShell processes started with parameters used
   to bypass the local execution policy for scripts. These parameters are often observed
   in attacks leveraging PowerShell scripts as they override the default PowerShell
   execution policy.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: 9be56c82-b1cc-4318-87eb-d138afaaca39
-known_false_positives: There may be legitimate reasons to bypass the PowerShell execution
-  policy. The PowerShell script being run with this parameter should be validated
-  to ensure that it is legitimate.
-name: Malicious PowerShell Process - Execution Policy Bypass
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process_id) as
   process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process)
   as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="*
   bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: There may be legitimate reasons to bypass the PowerShell execution
+  policy. The PowerShell script being run with this parameter should be validated
+  to ensure that it is legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -46,5 +50,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml b/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml
index 83954f0815..84b9ad0d70 100644
--- a/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml
+++ b/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml
@@ -1,17 +1,13 @@
-author: David Dorsey, Splunk
+name: Malicious PowerShell Process With Obfuscation Techniques
+id: cde75cf6-3c7a-4dd6-af01-27cdb4511fd4
+version: 4
 date: '2021-01-19'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for PowerShell processes launched with arguments that
   have characters indicative of obfuscation on the command-line.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: cde75cf6-3c7a-4dd6-af01-27cdb4511fd4
-known_false_positives: These characters might be legitimately on the command-line,
-  but it is not common.
-name: Malicious PowerShell Process With Obfuscation Techniques
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe
@@ -20,8 +16,16 @@ search: '| tstats `security_content_summariesonly` count values(Processes.proces
   `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1)
   + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "''"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter`
   | search num_obfuscation > 10 '
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: These characters might be legitimately on the command-line,
+  but it is not common.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Malicious PowerShell
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -45,5 +49,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/monitor_registry_keys_for_print_monitors.yml b/detections/endpoint/monitor_registry_keys_for_print_monitors.yml
index a8b3d83d1b..28370f53e5 100644
--- a/detections/endpoint/monitor_registry_keys_for_print_monitors.yml
+++ b/detections/endpoint/monitor_registry_keys_for_print_monitors.yml
@@ -1,28 +1,31 @@
-author: Bhavin Patel, Splunk
+name: Monitor Registry Keys for Print Monitors
+id: f5f6af30-7ba7-4295-bfe9-07de87c01bbc
+version: 2
 date: '2020-11-23'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for registry activity associated with modifications
   to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this
   scenario, an attacker can load an arbitrary .dll into the print-monitor registry
   by giving the full path name to the after.dll. The system will execute the .dll
   with elevated (SYSTEM) permissions and will persist after reboot.
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records registry activity from your hosts to populate the endpoint data model
-  in the registry node. This is typically populated via endpoint detection-and-response
-  products, such as Carbon Black, or via other endpoint data sources, such as Sysmon.
-  The data used for this search is typically generated via logs that report registry
-  modifications.
-id: f5f6af30-7ba7-4295-bfe9-07de87c01bbc
-known_false_positives: You will encounter noise from legitimate print-monitor registry
-  entries.
-name: Monitor Registry Keys for Print Monitors
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND
   Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest,
   Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name
   Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black, or via other endpoint data sources, such as Sysmon.
+  The data used for this search is typically generated via logs that report registry
+  modifications.
+known_false_positives: You will encounter noise from legitimate print-monitor registry
+  entries.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Windows Registry Activities
   - Windows Persistence Techniques
   asset_type: Endpoint
@@ -45,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/endpoint/nltest_domain_trust_discovery.yml b/detections/endpoint/nltest_domain_trust_discovery.yml
index 200c626697..86d04768fb 100644
--- a/detections/endpoint/nltest_domain_trust_discovery.yml
+++ b/detections/endpoint/nltest_domain_trust_discovery.yml
@@ -1,17 +1,27 @@
-author: Michael Haag, Splunk
+name: NLTest Domain Trust Discovery
+id: c3e05466-5f22-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-25'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for the execution of `nltest.exe` with command-line
   arguments utilized to query for Domain Trust information. Two arguments `/domain
   trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted
   domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current
   domain to assist with further understanding where to pivot next.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe
+  OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*)
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node.
-id: c3e05466-5f22-11eb-ae93-0242ac130002
 known_false_positives: Administrators may use nltest for troubleshooting purposes,
   otherwise, rarely used.
-name: NLTest Domain Trust Discovery
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md
 - https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104
@@ -20,14 +30,8 @@ references:
 - https://ss64.com/nt/nltest.html
 - https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/
 - https://thedfirreport.com/2020/10/08/ryuks-return/
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe
-  OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*)
-  by Processes.dest Processes.user Processes.parent_process Processes.process_name
-  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Ryuk Ransomware
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -47,5 +51,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/ntdsutil_export_ntds.yml b/detections/endpoint/ntdsutil_export_ntds.yml
index 3c704fc6b8..ce423c9ec8 100644
--- a/detections/endpoint/ntdsutil_export_ntds.yml
+++ b/detections/endpoint/ntdsutil_export_ntds.yml
@@ -2,6 +2,10 @@ name: Ntdsutil export ntds
 id: da63bc76-61ae-11eb-ae93-0242ac130002
 version: 1
 date: '2021-01-28'
+author: Michael Haag, Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: 'Monitor for signs that Ntdsutil is being used to Extract Active Directory
   database - NTDS.dit, typically used for offline password cracking. It may be used
   in normal circumstances with no command line arguments or shorthand variations of
@@ -13,39 +17,41 @@ description: 'Monitor for signs that Ntdsutil is being used to Extract Active Di
   This technique uses "Install from Media" (IFM), which will extract a copy of the
   Active Directory database. A successful export of the Active Directory database
   will yield a file modification named ntds.dit to the destination.'
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints, to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-type: ESCU
-references:
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil
-- https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11)
-- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
-- https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html
-author: Michael Haag, Patrick Bareiss, Splunk
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe
   Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user
   Processes.parent_process Processes.process_name Processes.process Processes.process_id
   Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints, to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
 known_false_positives: Highly possible Server Administrators will troubleshoot with
   ntdsutil.exe, generating false positives.
+references:
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil
+- https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11)
+- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+- https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
-  mitre_attack_id:
-  - T1003.003
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
   - CIS 16
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1003.003
   nist:
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/overwriting_accessibility_binaries.yml b/detections/endpoint/overwriting_accessibility_binaries.yml
index f810de1820..f7d474c40e 100644
--- a/detections/endpoint/overwriting_accessibility_binaries.yml
+++ b/detections/endpoint/overwriting_accessibility_binaries.yml
@@ -1,18 +1,15 @@
-author: David Dorsey, Splunk
+name: Overwriting Accessibility Binaries
+id: 13c2f6c3-10c5-4deb-9ba1-7c4460ebe4ae
+version: 4
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: Microsoft Windows contains accessibility features that can be launched
   with a key combination before a user has logged in. An adversary can modify or replace
   these programs so they can get a command prompt or backdoor without logging in to
   the system. This search looks for modifications to these binaries.
-how_to_implement: You must be ingesting data that records the filesystem activity
-  from your hosts to populate the Endpoint file-system data model node. If you are
-  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
-  you want to collect data.
-id: 13c2f6c3-10c5-4deb-9ba1-7c4460ebe4ae
-known_false_positives: Microsoft may provide updates to these binaries. Verify that
-  these changes do not correspond with your normal software update cycle.
-name: Overwriting Accessibility Binaries
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path)
   as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe*
@@ -21,8 +18,15 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*)
   by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)`
   | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter`'
+how_to_implement: You must be ingesting data that records the filesystem activity
+  from your hosts to populate the Endpoint file-system data model node. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: Microsoft may provide updates to these binaries. Verify that
+  these changes do not correspond with your normal software update cycle.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Privilege Escalation
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -42,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml b/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml
index 72679a3a3e..929bdc172f 100644
--- a/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml
+++ b/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml
@@ -1,18 +1,12 @@
-author: Jose Hernandez, Splunk
+name: Process Creating LNK file in Suspicious Location
+id: 5d814af1-1041-47b5-a9ac-d754e82e9a26
+version: 4
 date: '2021-01-28'
+author: Jose Hernandez, Splunk
+type: batch
+datamodel: []
 description: This search looks for a process launching an `*.lnk` file under `C:\User*`
   or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools.
-how_to_implement: You must be ingesting data that records filesystem and process activity
-  from your hosts to populate the Endpoint data model. This is typically populated
-  via endpoint detection-and-response products, such as Carbon Black, or endpoint
-  data sources, such as Sysmon.
-id: 5d814af1-1041-47b5-a9ac-d754e82e9a26
-known_false_positives: This detection should yield little or no false positive results.
-  It is uncommon for LNK files to be executed from temporary or user directories.
-name: Process Creating LNK file in Suspicious Location
-references:
-- https://attack.mitre.org/techniques/T1566/001/
-- https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk"
   AND Filesystem.file_path="C:\\Temp*"  by _time span=1h Filesystem.process_id Filesystem.file_name
@@ -25,8 +19,17 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path,
   process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter` '
+how_to_implement: You must be ingesting data that records filesystem and process activity
+  from your hosts to populate the Endpoint data model. This is typically populated
+  via endpoint detection-and-response product, such as Carbon Black, or endpoint
+  data sources, such as Sysmon.
+known_false_positives: This detection should yield little or no false positive results.
+  It is uncommon for LNK files to be executed from temporary or user directories.
+references:
+- https://attack.mitre.org/techniques/T1566/001/
+- https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html
 tags:
-  analytics_story:
+  analytic_story:
   - Phishing Payloads
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -48,5 +51,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 4
diff --git a/detections/endpoint/process_execution_via_wmi.yml b/detections/endpoint/process_execution_via_wmi.yml
index 2f6eb07522..1de8f1b1ed 100644
--- a/detections/endpoint/process_execution_via_wmi.yml
+++ b/detections/endpoint/process_execution_via_wmi.yml
@@ -1,22 +1,25 @@
-author: Rico Valdez, Splunk
-date: '2020-03-16'
-description: This search looks for processes launched via WMI.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-id: 24869767-8579-485d-9a4f-d9ddfd8f0cac
-known_false_positives: Although unlikely, administrators may use wmi to execute commands
-  for legitimate purposes.
 name: Process Execution via WMI
-references: []
+id: 24869767-8579-485d-9a4f-d9ddfd8f0cac
+version: 3
+date: '2020-03-16'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
+description: This search looks for processes launched via WMI.
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
   where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest
   Processes.process_name  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)`| `process_execution_via_wmi_filter` '
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: Although unlikely, administrators may use wmi to execute commands
+  for legitimate purposes.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious WMI Use
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -39,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/processes_launching_netsh.yml b/detections/endpoint/processes_launching_netsh.yml
index 1695100cc1..885bcbb14d 100644
--- a/detections/endpoint/processes_launching_netsh.yml
+++ b/detections/endpoint/processes_launching_netsh.yml
@@ -1,26 +1,30 @@
-author: Josef Kuepker, Splunk
+name: Processes launching netsh
+id: b89919ed-fe5f-492c-b139-95dbb162040e
+version: 3
 date: '2020-07-10'
+author: Josef Kuepker, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for processes launching netsh.exe. Netsh is a command-line
   scripting utility that allows you to, either locally or remotely, display or modify
   the network configuration of a computer that is currently running. Netsh can be
   used as a persistence proxy technique to execute a helper DLL when netsh.exe is
   executed. In this search, we are looking for processes spawned by netsh.exe and
   executing commands via the command line.
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records process activity from your hosts to populate the endpoint data model
-id: b89919ed-fe5f-492c-b139-95dbb162040e
-known_false_positives: Some VPN applications are known to launch netsh.exe. Outside
-  of these instances, it is unusual for an executable to launch netsh.exe and run
-  commands.
-name: Processes launching netsh
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process
   Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")`
   |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records process activity from your hosts to populate the endpoint data model
+known_false_positives: Some VPN applications are known to launch netsh.exe. Outside
+  of these instances, it is unusual for an executable to launch netsh.exe and run
+  commands.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Netsh Abuse
   - Disabling Security Tools
   - DHS Report TA18-074A
@@ -42,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml b/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml
index fcc7105c7c..75a4f307c7 100644
--- a/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml
+++ b/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml
@@ -1,13 +1,13 @@
-author: Rico Valdez, Splunk
+name: Reg exe Manipulating Windows Services Registry Keys
+id: 8470d755-0c13-45b3-bd63-387a373c10cf
+version: 5
 date: '2020-11-26'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks for reg.exe modifying registry keys that define Windows
   services and their configurations.
-id: 8470d755-0c13-45b3-bd63-387a373c10cf
-known_false_positives: It is unusual for a service to be created or modified by directly
-  manipulating the registry. However, there may be legitimate instances of this behavior.
-  It is important to validate and investigate, as appropriate.
-name: Reg exe Manipulating Windows Services Registry Keys
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name)
   as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes
@@ -15,8 +15,18 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   Processes.process=*Services* by Processes.process_id Processes.dest Processes.process
   | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `reg_exe_manipulating_windows_services_registry_keys_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
+known_false_positives: It is unusual for a service to be created or modified by directly
+  manipulating the registry. However, there may be legitimate instances of this behavior.
+  It is important to validate and investigate, as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Service Abuse
   - Windows Persistence Techniques
   asset_type: Endpoint
@@ -42,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/registry_keys_for_creating_shim_databases.yml b/detections/endpoint/registry_keys_for_creating_shim_databases.yml
index 4642b77040..08e0a43252 100644
--- a/detections/endpoint/registry_keys_for_creating_shim_databases.yml
+++ b/detections/endpoint/registry_keys_for_creating_shim_databases.yml
@@ -1,24 +1,27 @@
-author: Bhavin Patel, Patrick Bareiss, Splunk
+name: Registry Keys for Creating SHIM Databases
+id: f5f6af30-7aa7-4295-bfe9-07fe87c01bbb
+version: 3
 date: '2020-11-26'
+author: Bhavin Patel, Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: This search looks for registry activity associated with application compatibility
   shims, which can be leveraged by attackers for various nefarious purposes.
-how_to_implement: To successfully implement this search, you must populate the Change_Analysis
-  data model. This is typically populated via endpoint detection and response products,
-  such as Carbon Black or other endpoint data sources such as Sysmon. The data used
-  for this search is typically generated via logs that report reads and writes to
-  the registry.
-id: f5f6af30-7aa7-4295-bfe9-07fe87c01bbb
-known_false_positives: There are many legitimate applications that leverage shim databases
-  for compatibility purposes for legacy applications
-name: Registry Keys for Creating SHIM Databases
-references: []
 search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name)
   as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry
   where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB*
   by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   | `drop_dm_object_name(Registry)` | `registry_keys_for_creating_shim_databases_filter`'
+how_to_implement: To successfully implement this search, you must populate the Change_Analysis
+  data model. This is typically populated via endpoint detection and response product,
+  such as Carbon Black or other endpoint data sources such as Sysmon. The data used
+  for this search is typically generated via logs that report reads and writes to
+  the registry.
+known_false_positives: There are many legitimate applications that leverage shim databases
+  for compatibility purposes for legacy applications
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Windows Registry Activities
   - Windows Persistence Techniques
   asset_type: Endpoint
@@ -39,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/registry_keys_used_for_persistence.yml b/detections/endpoint/registry_keys_used_for_persistence.yml
index 9653639e18..4fd1b76902 100644
--- a/detections/endpoint/registry_keys_used_for_persistence.yml
+++ b/detections/endpoint/registry_keys_used_for_persistence.yml
@@ -1,18 +1,12 @@
-author: David Dorsey, Splunk
+name: Registry Keys Used For Persistence
+id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b
+version: 5
 date: '2020-11-27'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: The search looks for modifications to registry keys that can be used
   to launch an application or service at system startup.
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records registry activity from your hosts to populate the endpoint data model
-  in the registry node. This is typically populated via endpoint detection-and-response
-  products, such as Carbon Black or endpoint data sources, such as Sysmon. The data
-  used for this search is typically generated via logs that report reads and writes
-  to the registry.
-id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b
-known_false_positives: There are many legitimate applications that must execute on
-  system startup and will use these registry keys to accomplish that task.
-name: Registry Keys Used For Persistence
-references: []
 search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name)
   as registry_key_name values(Registry.registry_path) as registry_path min(_time)
   as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run*
@@ -23,8 +17,17 @@ search: '| tstats `security_content_summariesonly` count values(Registry.registr
   NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*)
   by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   | `drop_dm_object_name(Registry)` | `registry_keys_used_for_persistence_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
+known_false_positives: There are many legitimate applications that must execute on
+  system startup and will use these registry keys to accomplish that task.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Windows Registry Activities
   - Suspicious MSHTA Activity
   - DHS Report TA18-074A
@@ -51,5 +54,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/registry_keys_used_for_privilege_escalation.yml b/detections/endpoint/registry_keys_used_for_privilege_escalation.yml
index a9276249bc..3eb1f7ebcb 100644
--- a/detections/endpoint/registry_keys_used_for_privilege_escalation.yml
+++ b/detections/endpoint/registry_keys_used_for_privilege_escalation.yml
@@ -1,29 +1,32 @@
-author: David Dorsey, Splunk
+name: Registry Keys Used For Privilege Escalation
+id: c9f4b923-f8af-4155-b697-1354f5bcbc5e
+version: 4
 date: '2020-11-27'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for modifications to registry keys that can be used
   to elevate privileges. The registry keys under "Image File Execution Options" are
   used to intercept calls to an executable and can be used to attach malicious binaries
   to benign system binaries.
-how_to_implement: To successfully implement this search, you must be ingesting data
-  that records registry activity from your hosts to populate the endpoint data model
-  in the registry node. This is typically populated via endpoint detection-and-response
-  products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data
-  used for this search is typically generated via logs that report reads and writes
-  to the registry.
-id: c9f4b923-f8af-4155-b697-1354f5bcbc5e
-known_false_positives: There are many legitimate applications that must execute upon
-  system startup and will use these registry keys to accomplish that task.
-name: Registry Keys Used For Privilege Escalation
-references:
-- https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/
 search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime
   max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows
   NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag
   OR Registry.registry_key_name=Debugger) by Registry.dest  Registry.user Registry.registry_path
   Registry.registry_key_name | `security_content_ctime(lastTime)`  | `security_content_ctime(firstTime)`
   | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
+known_false_positives: There are many legitimate applications that must execute upon
+  system startup and will use these registry keys to accomplish that task.
+references:
+- https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Privilege Escalation
   - Suspicious Windows Registry Activities
   - Cloud Federated Credential Abuse
@@ -44,5 +47,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/remote_process_instantiation_via_wmi.yml b/detections/endpoint/remote_process_instantiation_via_wmi.yml
index b3020029c0..8730f187a9 100644
--- a/detections/endpoint/remote_process_instantiation_via_wmi.yml
+++ b/detections/endpoint/remote_process_instantiation_via_wmi.yml
@@ -1,26 +1,30 @@
-author: Rico Valdez, Splunk
+name: Remote Process Instantiation via WMI
+id: d25d2c3d-d9d8-40ec-8fdf-e86fe155a3da
+version: 5
 date: '2020-11-30'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for wmic.exe being launched with parameters to spawn
   a process on a remote system.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: d25d2c3d-d9d8-40ec-8fdf-e86fe155a3da
-known_false_positives: The wmic.exe utility is a benign Windows application. It may
-  be used legitimately by Administrators with these parameters for remote system administration,
-  but it's relatively uncommon.
-name: Remote Process Instantiation via WMI
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process) as process
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*"
   Processes.process="*call*" Processes.process="*create*"   by Processes.process_name
   Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: The wmic.exe utility is a benign Windows application. It may
+  be used legitimately by Administrators with these parameters for remote system administration,
+  but it's relatively uncommon.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Ransomware
   - Suspicious WMI Use
   asset_type: Endpoint
@@ -44,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/rundll_loading_dll_by_ordinal.yml b/detections/endpoint/rundll_loading_dll_by_ordinal.yml
index 7344015802..365530006f 100644
--- a/detections/endpoint/rundll_loading_dll_by_ordinal.yml
+++ b/detections/endpoint/rundll_loading_dll_by_ordinal.yml
@@ -1,27 +1,31 @@
-author: David Dorsey, Splunk
+name: RunDLL Loading DLL By Ordinal
+id: 6c135f8d-5e60-454e-80b7-c56eed739833
+version: 4
 date: '2020-11-30'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for executing scripts with rundll32. Adversaries may
   abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice
   executing directly, may avoid triggering security tools that may not monitor execution
   of the rundll32.exe process because of allowlists or false positives from normal
   operations.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: 6c135f8d-5e60-454e-80b7-c56eed739833
-known_false_positives: While not common, loading a DLL under %AppData% and calling
-  a function by ordinal is possible by a legitimate process
-name: RunDLL Loading DLL By Ordinal
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process) as process
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name
   Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll_loading_dll_by_ordinal_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: While not common, loading a DLL under %AppData% and calling
+  a function by ordinal is possible by a legitimate process
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Unusual Processes
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -41,5 +45,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/ryuk_test_files_detected.yml b/detections/endpoint/ryuk_test_files_detected.yml
index c5971dea5e..2b3e7386f4 100644
--- a/detections/endpoint/ryuk_test_files_detected.yml
+++ b/detections/endpoint/ryuk_test_files_detected.yml
@@ -1,22 +1,25 @@
-author: Rod Soto, Jose Hernandez, Splunk
+name: Ryuk Test Files Detected
+id: 57d44d70-28d9-4ed1-acf5-1c80ae2bbce3
+version: 1
 date: '2020-11-06'
+author: Rod Soto, Jose Hernandez, Splunk
+type: batch
+datamodel: []
 description: The search looks for files that contain the key word *Ryuk* under any
   folder in the C drive, which is consistent with Ryuk propagation.
-how_to_implement: You must be ingesting data that records the filesystem activity
-  from your hosts to populate the Endpoint Filesystem data-model object. If you are
-  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
-  you want to collect data.
-id: 57d44d70-28d9-4ed1-acf5-1c80ae2bbce3
-known_false_positives: If there are files with this keywoord as file names it might
-  trigger false possitives, please make use of our filters to tune out potential FPs.
-name: Ryuk Test Files Detected
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk*
   BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)`
   | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter`'
+how_to_implement: You must be ingesting data that records the filesystem activity
+  from your hosts to populate the Endpoint Filesystem data-model object. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: If there are files with this keywoord as file names it might
+  trigger false possitives, please make use of our filters to tune out potential FPs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Ryuk Ransomware
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -36,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/samsam_test_file_write.yml b/detections/endpoint/samsam_test_file_write.yml
index 93409aea4d..19c7c97446 100644
--- a/detections/endpoint/samsam_test_file_write.yml
+++ b/detections/endpoint/samsam_test_file_write.yml
@@ -1,22 +1,26 @@
-author: Rico Valdez, Splunk
+name: Samsam Test File Write
+id: 493a879d-519d-428f-8f57-a06a0fdc107e
+version: 1
 date: '2018-12-14'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The search looks for a file named "test.txt" written to the windows system
   directory tree, which is consistent with Samsam propagation.
-how_to_implement: You must be ingesting data that records the file-system activity
-  from your hosts to populate the Endpoint file-system data-model node. If you are
-  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
-  you want to collect data.
-id: 493a879d-519d-428f-8f57-a06a0fdc107e
-known_false_positives: No false positives have been identified.
-name: Samsam Test File Write
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name)
   as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt
   by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)`
   | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter`'
+how_to_implement: You must be ingesting data that records the file-system activity
+  from your hosts to populate the Endpoint file-system data-model node. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: No false positives have been identified.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -36,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/sc_exe_manipulating_windows_services.yml b/detections/endpoint/sc_exe_manipulating_windows_services.yml
index d39591da5b..1d634d0a46 100644
--- a/detections/endpoint/sc_exe_manipulating_windows_services.yml
+++ b/detections/endpoint/sc_exe_manipulating_windows_services.yml
@@ -1,21 +1,28 @@
-author: Rico Valdez, Splunk
+name: Sc exe Manipulating Windows Services
+id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d
+version: 4
 date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for arguments to sc.exe indicating the creation or
   modification of a Windows service.
-id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d
-known_false_positives: Using sc.exe to manipulate Windows services is uncommon. However,
-  there may be legitimate instances of this behavior. It is important to validate
-  and investigate as appropriate.
-name: Sc exe Manipulating Windows Services
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process) as process
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="*
   config *") by Processes.process_name Processes.parent_process_name Processes.dest
   Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Using sc.exe to manipulate Windows services is uncommon. However,
+  there may be legitimate instances of this behavior. It is important to validate
+  and investigate as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Service Abuse
   - DHS Report TA18-074A
   - Orangeworm Attack Group
@@ -45,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml b/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml
index a1ab432e49..6041f950c7 100644
--- a/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml
+++ b/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml
@@ -1,25 +1,29 @@
-author: Bhavin Patel, Splunk
+name: Scheduled Task Deleted Or Created via CMD
+id: d5af132c-7c17-439c-9d31-13d55340f36c
+version: 5
 date: '2020-12-17'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for flags passed to schtasks.exe on the command-line
   that indicate a task was created via command like. This has been associated with
   the Dragonfly threat actor, and the SUNBURST attack against Solarwinds.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-id: d5af132c-7c17-439c-9d31-13d55340f36c
-known_false_positives: Tasks should not be manually created via CLI, this is rarely
-  done by admins as well
-name: Scheduled Task Deleted Or Created via CMD
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe
   (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name
   Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter` '
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: Tasks should not be manually created via CLI, this is rarely
+  done by admins as well
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   - Sunburst Malware
   asset_type: Endpoint
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml b/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml
index 18f3bb801c..445e44861c 100644
--- a/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml
+++ b/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml
@@ -1,26 +1,30 @@
-author: David Dorsey, Splunk
+name: Schtasks scheduling job on remote system
+id: 1297fb80-f42a-4b4a-9c8a-88c066237cf6
+version: 4
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for flags passed to schtasks.exe on the command-line
   that indicate a job is being scheduled on a remote system.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: 1297fb80-f42a-4b4a-9c8a-88c066237cf6
-known_false_positives: Administrators may create jobs on remote systems, but this
-  activity is usually limited to a small set of hosts or users. It is important to
-  validate and investigate as appropriate.
-name: Schtasks scheduling job on remote system
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe
   Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="*
   /S *") by Processes.process_name Processes.process Processes.parent_process_name
   Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: Administrators may create jobs on remote systems, but this
+  activity is usually limited to a small set of hosts or users. It is important to
+  validate and investigate as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Lateral Movement
   - Sunburst Malware
   asset_type: Endpoint
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml b/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml
index 1c19e2bec7..c6c443a99e 100644
--- a/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml
+++ b/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml
@@ -1,23 +1,27 @@
-author: Bhavin Patel, Splunk
+name: Schtasks used for forcing a reboot
+id: 1297fb80-f42a-4b4a-9c8a-88c066437cf6
+version: 4
 date: '2020-12-07'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for flags passed to schtasks.exe on the command-line
   that indicate that a forced reboot of system is scheduled.
-how_to_implement: To successfully implement this search you need to be ingesting logs
-  with both the process name and command-line from your endpoints. If you are using
-  Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-id: 1297fb80-f42a-4b4a-9c8a-88c066437cf6
-known_false_positives: Administrators may create jobs on systems forcing reboots to
-  perform updates, maintenance, etc.
-name: Schtasks used for forcing a reboot
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process) as process
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create
   *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user
   | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `schtasks_used_for_forcing_a_reboot_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting logs
+  with both the process name and command-line from your endpoints. If you are using
+  Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: Administrators may create jobs on systems forcing reboots to
+  perform updates, maintenance, etc.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Persistence Techniques
   - Ransomware
   asset_type: Endpoint
@@ -37,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/script_execution_via_wmi.yml b/detections/endpoint/script_execution_via_wmi.yml
index 19a7af0444..f871561958 100644
--- a/detections/endpoint/script_execution_via_wmi.yml
+++ b/detections/endpoint/script_execution_via_wmi.yml
@@ -1,22 +1,25 @@
-author: Rico Valdez, Splunk
-date: '2020-03-16'
-description: This search looks for scripts launched via WMI.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-id: aa73f80d-d728-4077-b226-81ea0c8be589
-known_false_positives: Although unlikely, administrators may use wmi to launch scripts
-  for legitimate purposes.
 name: Script Execution via WMI
-references: []
+id: aa73f80d-d728-4077-b226-81ea0c8be589
+version: 3
+date: '2020-03-16'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
+description: This search looks for scripts launched via WMI.
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
   where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name  |
   `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
   `script_execution_via_wmi_filter` '
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: Although unlikely, administrators may use wmi to launch scripts
+  for legitimate purposes.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious WMI Use
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -39,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/shim_database_file_creation.yml b/detections/endpoint/shim_database_file_creation.yml
index 7180a3a3f0..942141552a 100644
--- a/detections/endpoint/shim_database_file_creation.yml
+++ b/detections/endpoint/shim_database_file_creation.yml
@@ -1,27 +1,30 @@
-author: David Dorsey, Splunk
+name: Shim Database File Creation
+id: 6e4c4588-ba2f-42fa-97e6-9f6f548eaa33
+version: 3
 date: '2020-12-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for shim database files being written to default directories.
   The sdbinst.exe application is used to install shim database files (.sdb). According
   to Microsoft, a shim is a small library that transparently intercepts an API, changes
   the parameters passed, handles the operation itself, or redirects the operation
   elsewhere.
-how_to_implement: You must be ingesting data that records the filesystem activity
-  from your hosts to populate the Endpoint file-system data model node. If you are
-  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
-  you want to collect data.
-id: 6e4c4588-ba2f-42fa-97e6-9f6f548eaa33
-known_false_positives: Because legitimate shim files are created and used all the
-  time, this event, in itself, is not suspicious. However, if there are other correlating
-  events, it may warrant further investigation.
-name: Shim Database File Creation
-references: []
 search: '| tstats `security_content_summariesonly` count values(Filesystem.action)
   values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path  min(_time)
   as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom*
   by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter`'
+how_to_implement: You must be ingesting data that records the filesystem activity
+  from your hosts to populate the Endpoint file-system data model node. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: Because legitimate shim files are created and used all the
+  time, this event, in itself, is not suspicious. However, if there are other correlating
+  events, it may warrant further investigation.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Persistence Techniques
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -40,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml b/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml
index c71fcd12e9..0ab6149713 100644
--- a/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml
+++ b/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml
@@ -1,26 +1,30 @@
-author: David Dorsey, Splunk
+name: Shim Database Installation With Suspicious Parameters
+id: 404620de-46d8-48b6-90cc-8a8d7b0876a3
+version: 4
 date: '2020-11-23'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search detects the process execution and arguments required to silently
   create a shim database.  The sdbinst.exe application is used to install shim database
   files (.sdb). A shim is a small library which transparently intercepts an API, changes
   the parameters passed, handles the operation itself, or redirects the operation
   elsewhere.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: 404620de-46d8-48b6-90cc-8a8d7b0876a3
-known_false_positives: None identified
-name: Shim Database Installation With Suspicious Parameters
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process) as process
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name
   Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: None identified
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Persistence Techniques
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/short_lived_windows_accounts.yml b/detections/endpoint/short_lived_windows_accounts.yml
index 83de551b77..01b6224326 100644
--- a/detections/endpoint/short_lived_windows_accounts.yml
+++ b/detections/endpoint/short_lived_windows_accounts.yml
@@ -1,15 +1,13 @@
-author: David Dorsey, Splunk
+name: Short Lived Windows Accounts
+id: b25f6f62-0782-43c1-b403-083231ffd97d
+version: 2
 date: '2020-07-06'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Change
 description: This search detects accounts that were created and deleted in a short
   time period.
-how_to_implement: 'This search requires you to have enabled your Group Management
-  Audit Logs in your Local Windows Security Policy and be ingesting those logs.  More
-  information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/'
-id: b25f6f62-0782-43c1-b403-083231ffd97d
-known_false_positives: It is possible that an administrator created and deleted an
-  account in a short time period.  Verifying activity with an administrator is advised.
-name: Short Lived Windows Accounts
-references: []
 search: '| tstats `security_content_summariesonly` values(All_Changes.result_id) as
   result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change
   where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h
@@ -17,8 +15,14 @@ search: '| tstats `security_content_summariesonly` values(All_Changes.result_id)
   | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726
   | transaction user connected=false maxspan=240m | table firstTime lastTime count
   user dest result_id | `short_lived_windows_accounts_filter`'
+how_to_implement: 'This search requires you to have enabled your Group Management
+  Audit Logs in your Local Windows Security Policy and be ingesting those logs.  More
+  information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/'
+known_false_positives: It is possible that an administrator created and deleted an
+  account in a short time period.  Verifying activity with an administrator is advised.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Account Monitoring and Controls
   asset_type: Windows
   automated_detection_testing: passed
@@ -37,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: access
-type: ESCU
-version: 2
diff --git a/detections/endpoint/single_letter_process_on_endpoint.yml b/detections/endpoint/single_letter_process_on_endpoint.yml
index 73aedc349b..b9e1602470 100644
--- a/detections/endpoint/single_letter_process_on_endpoint.yml
+++ b/detections/endpoint/single_letter_process_on_endpoint.yml
@@ -1,16 +1,12 @@
-author: David Dorsey, Splunk
-date: '2020-12-08'
-description: This search looks for process names that consist only of a single letter.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: a4214f0b-e01c-41bc-8cc4-d2b71e3056b4
-known_false_positives: Single-letter executables are not always malicious. Investigate
-  this activity with your normal incident-response process.
 name: Single Letter Process On Endpoint
-references: []
+id: a4214f0b-e01c-41bc-8cc4-d2b71e3056b4
+version: 3
+date: '2020-12-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
+description: This search looks for process names that consist only of a single letter.
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user,
   Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)`
@@ -18,8 +14,16 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5
   AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name
   | `single_letter_process_on_endpoint_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: Single-letter executables are not always malicious. Investigate
+  this activity with your normal incident-response process.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/ssa___applying_stolen_credentials_via_mimikatz_modules.yml b/detections/endpoint/ssa___applying_stolen_credentials_via_mimikatz_modules.yml
index a7b529d706..86fc6419ed 100644
--- a/detections/endpoint/ssa___applying_stolen_credentials_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___applying_stolen_credentials_via_mimikatz_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Applying Stolen Credentials via Mimikatz modules
+id: 759a653f-cb92-40f9-94c9-ec4e47b0f709
+version: 1
 date: '2020-11-03'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection indicates use of Mimikatz modules that facilitate Pass-the-Token
   attack, Golden or Silver kerberos ticket attack, and Skeleton key attack.
-eli5: This detection identifies use of Mimikatz modules which apply stolen credentials.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 759a653f-cb92-40f9-94c9-ec4e47b0f709
-known_false_positives: None identified.
-name: Applying Stolen Credentials via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
-- https://adsecurity.org/?p=1275
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -22,6 +18,12 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
+- https://adsecurity.org/?p=1275
 tags:
   asset_type: Windows
   cis20:
@@ -53,5 +55,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___applying_stolen_credentials_via_powersploit_modules.yml b/detections/endpoint/ssa___applying_stolen_credentials_via_powersploit_modules.yml
index 2c15d21a0e..76033240cc 100644
--- a/detections/endpoint/ssa___applying_stolen_credentials_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___applying_stolen_credentials_via_powersploit_modules.yml
@@ -1,19 +1,14 @@
-author: Stanislav Miskovic, Splunk
+name: Applying Stolen Credentials via PowerSploit modules
+id: 270b482d-2af2-448f-9923-9cf005f61be4
+version: 1
 date: '2020-11-03'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Stolen credentials are applied by methods such as user impersonation,
   credential injection, spoofing of authentication processes or getting hold of critical
   accounts. This detection indicates such activities carried out by PowerSploit exploit
   kit APIs.
-eli5: This detection identifies use of PowerSploit modules that apply stolen credentials,
-  including the module for credential injection, token manipulation, user impersonation,
-  getting the system, and change of identities.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 270b482d-2af2-448f-9923-9cf005f61be4
-known_false_positives: None identified.
-name: Applying Stolen Credentials via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -26,6 +21,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -57,5 +57,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___assess_credential_strength_via_dsinternals_modules.yml b/detections/endpoint/ssa___assess_credential_strength_via_dsinternals_modules.yml
index 2b787f072c..a9fbfe364a 100644
--- a/detections/endpoint/ssa___assess_credential_strength_via_dsinternals_modules.yml
+++ b/detections/endpoint/ssa___assess_credential_strength_via_dsinternals_modules.yml
@@ -1,15 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Assessment of Credential Strength via DSInternals modules
+id: 5526d3a4-2497-4e8d-9d3c-7a34c9aace2f
+version: 1
 date: '2020-11-03'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of DSInternals modules that verify password
   strength, i.e., identify week accounts that would be easily compromised.
-eli5: This detection identifies use of DSInternals modules which assess password strength.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 5526d3a4-2497-4e8d-9d3c-7a34c9aace2f
-known_false_positives: None identified.
-name: Assessment of Credential Strength via DSInternals modules
-references:
-- https://github.com/MichaelGrafnetter/DSInternals
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -19,6 +16,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/MichaelGrafnetter/DSInternals
 tags:
   asset_type: Windows
   cis20:
@@ -45,5 +47,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___attempted_credential_dump_from_registry_via_reg_exe.yml b/detections/endpoint/ssa___attempted_credential_dump_from_registry_via_reg_exe.yml
index 8bc4ee3dd8..0d8fc65ffd 100644
--- a/detections/endpoint/ssa___attempted_credential_dump_from_registry_via_reg_exe.yml
+++ b/detections/endpoint/ssa___attempted_credential_dump_from_registry_via_reg_exe.yml
@@ -1,14 +1,12 @@
-author: Jose Hernandez, Splunk
+name: Attempted Credential Dump From Registry via Reg exe - SSA
+id: 14038953-e5f2-4daf-acff-5452062baf03
+version: 1
 date: 2020-6-04
+author: Jose Hernandez, Splunk
+type: streaming
+datamodel: []
 description: Monitor for execution of reg.exe with parameters specifying an export
   of keys that contain hashed credentials that attackers may try to crack offline.
-how_to_implement: You must be ingesting windows endpoint data that tracks process
-  activity, including parent-child relationships from your endpoints.
-id: 14038953-e5f2-4daf-acff-5452062baf03
-known_false_positives: None identified.
-name: Attempted Credential Dump From Registry via Reg exe - SSA
-references:
-- https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml
 search: ' | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event,
   "_time"), "string", null)) | eval process_name=lower(ucast(map_get(input_event,
   "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"),
@@ -21,8 +19,13 @@ search: ' | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(ma
   /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true
   ) | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id,
   dest_user_id), body = "TBD" | into write_ssa_detected_events(); '
+how_to_implement: You must be ingesting windows endpoint data that tracks process
+  activity, including parent-child relationships from your endpoints.
+known_false_positives: None identified.
+references:
+- https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Endpoint
   cis20:
@@ -45,5 +48,3 @@ tags:
   - process
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_dsinternals_conversion_modules.yml b/detections/endpoint/ssa___credential_extraction_dsinternals_conversion_modules.yml
index 4002328d02..10669bcbc2 100644
--- a/detections/endpoint/ssa___credential_extraction_dsinternals_conversion_modules.yml
+++ b/detections/endpoint/ssa___credential_extraction_dsinternals_conversion_modules.yml
@@ -1,19 +1,15 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction indicative of use of DSInternals credential conversion
+  modules
+id: 73e23834-c7ad-4860-bfd0-7d8ffe6527c2
+version: 1
 date: '2020-10-21'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. DSInternals
   is a collection of PowerShell modules commonly employed in exploits.
-eli5: This detection identifies use of DSInternals modules for credential extraction
-  and conversion.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 73e23834-c7ad-4860-bfd0-7d8ffe6527c2
-known_false_positives: None identified.
-name: Credential Extraction indicative of use of DSInternals credential conversion
-  modules
-references:
-- https://github.com/MichaelGrafnetter/DSInternals
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -31,6 +27,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/MichaelGrafnetter/DSInternals
 tags:
   asset_type: Windows
   cis20:
@@ -54,5 +55,3 @@ tags:
   - process
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_dsinternals_modules.yml b/detections/endpoint/ssa___credential_extraction_dsinternals_modules.yml
index b72b432b63..99d03f526a 100644
--- a/detections/endpoint/ssa___credential_extraction_dsinternals_modules.yml
+++ b/detections/endpoint/ssa___credential_extraction_dsinternals_modules.yml
@@ -1,17 +1,14 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction indicative of use of DSInternals modules
+id: 5d2172f0-8a7d-4ecd-aad9-2dcc95699e0d
+version: 1
 date: '2020-10-21'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. DSInternals
   is a collection of PowerShell modules commonly employed in exploits.
-eli5: This detection identifies use of DSInternals modules for credential extraction.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 5d2172f0-8a7d-4ecd-aad9-2dcc95699e0d
-known_false_positives: None identified.
-name: Credential Extraction indicative of use of DSInternals modules
-references:
-- https://github.com/MichaelGrafnetter/DSInternals
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -31,6 +28,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/MichaelGrafnetter/DSInternals
 tags:
   asset_type: Windows
   cis20:
@@ -54,5 +56,3 @@ tags:
   - process
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_fgdump_cachedump_s_option.yml b/detections/endpoint/ssa___credential_extraction_fgdump_cachedump_s_option.yml
index e18dbbad83..9c8372ab3e 100644
--- a/detections/endpoint/ssa___credential_extraction_fgdump_cachedump_s_option.yml
+++ b/detections/endpoint/ssa___credential_extraction_fgdump_cachedump_s_option.yml
@@ -1,20 +1,16 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction indicative of FGDump and CacheDump with s option
+id: 312582f2-5e91-42c1-a275-cd67f31373c8
+version: 1
 date: '2020-10-18'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. FGdump is
   a newer version of pwdump tool that extracts NTLM and LanMan password hashes from
   Windows. Cachedump is a publicly-available tool that extracts cached password hashes
   from a system's registry.
-eli5: This detection identifies one of the inevitable stages of FGdump in which CacheDump
-  is called. Note, CacheDump activity may also be embedded in other exploit tools.
-  For more details on FGdump stages see https://github.com/interference-security/kali-windows-binaries/tree/master/fgdump
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 312582f2-5e91-42c1-a275-cd67f31373c8
-known_false_positives: None identified.
-name: Credential Extraction indicative of FGDump and CacheDump with s option
-references: []
 search: ' | from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -29,6 +25,10 @@ search: ' | from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references: []
 tags:
   asset_type: Windows
   cis20:
@@ -52,5 +52,3 @@ tags:
   - process
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_fgdump_cachedump_v_option.yml b/detections/endpoint/ssa___credential_extraction_fgdump_cachedump_v_option.yml
index 4882b9bf59..deb6c19961 100644
--- a/detections/endpoint/ssa___credential_extraction_fgdump_cachedump_v_option.yml
+++ b/detections/endpoint/ssa___credential_extraction_fgdump_cachedump_v_option.yml
@@ -1,20 +1,16 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction indicative of FGDump and CacheDump with v option
+id: 3c40b0ef-a03f-460a-9484-e4b9117cbb38
+version: 1
 date: '2020-10-18'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. FGdump is
   a newer version of pwdump tool that extracts NTLM and LanMan password hashes from
   Windows. Cachedump is a publicly-available tool that extracts cached password hashes
   from a system's registry.
-eli5: This detection identifies one of the stages of FGdump in which CacheDump is
-  called. Note, CacheDump activity may also be embedded in other exploit tools. For
-  more details on FGdump stages see https://github.com/interference-security/kali-windows-binaries/tree/master/fgdump
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 3c40b0ef-a03f-460a-9484-e4b9117cbb38
-known_false_positives: None identified.
-name: Credential Extraction indicative of FGDump and CacheDump with v option
-references: []
 search: ' | from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -27,6 +23,10 @@ search: ' | from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references: []
 tags:
   asset_type: Windows
   cis20:
@@ -49,5 +49,3 @@ tags:
   - process
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_getaddbaccount_from_dump.yml b/detections/endpoint/ssa___credential_extraction_getaddbaccount_from_dump.yml
index dddf72a75a..312e52193e 100644
--- a/detections/endpoint/ssa___credential_extraction_getaddbaccount_from_dump.yml
+++ b/detections/endpoint/ssa___credential_extraction_getaddbaccount_from_dump.yml
@@ -1,19 +1,16 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction via Get-ADDBAccount module present in PowerSploit and
+  DSInternals
+id: e4f126b5-e6bc-4a5c-b1a8-d07bc6c4a49f
+version: 1
 date: '2020-10-18'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. PowerSploit
   and DSInternals are common exploit APIs offering PowerShell modules for various
   exploits of Windows and Active Directory environments.
-eli5: This detection identifies triggering of the PowerSploit or DSInternals for extraction
-  of all accounts from a previously dumped ntds.dit credential store.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: e4f126b5-e6bc-4a5c-b1a8-d07bc6c4a49f
-known_false_positives: None identified.
-name: Credential Extraction via Get-ADDBAccount module present in PowerSploit and
-  DSInternals
-references: []
 search: ' | from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -24,6 +21,10 @@ search: ' | from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references: []
 tags:
   asset_type: Windows
   cis20:
@@ -44,5 +45,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_lazagne_command_options.yml b/detections/endpoint/ssa___credential_extraction_lazagne_command_options.yml
index efc6041973..e2bdad87a2 100644
--- a/detections/endpoint/ssa___credential_extraction_lazagne_command_options.yml
+++ b/detections/endpoint/ssa___credential_extraction_lazagne_command_options.yml
@@ -1,19 +1,15 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction indicative of Lazagne command line options
+id: 341975fa-4ad0-4f01-9acc-df4f69742db7
+version: 1
 date: '2020-10-18'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. LaZagne is
   a tool that extracts various kinds of credentials from a local computer, including
   account passwords, domain passwords, browser passwords, etc.
-eli5: This detection identifies the most common LaZagne invocation, in which it is
-  instructed to extract all available passwords and output them to a file. For more
-  details on LaZagne see https://github.com/AlessandroZ/LaZagne
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 341975fa-4ad0-4f01-9acc-df4f69742db7
-known_false_positives: None identified.
-name: Credential Extraction indicative of Lazagne command line options
-references: []
 search: ' | from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -23,6 +19,10 @@ search: ' | from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references: []
 tags:
   asset_type: Windows
   cis20:
@@ -44,5 +44,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_mimikatz_modules.yml b/detections/endpoint/ssa___credential_extraction_mimikatz_modules.yml
index 681d19d718..e0596efb21 100644
--- a/detections/endpoint/ssa___credential_extraction_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___credential_extraction_mimikatz_modules.yml
@@ -1,20 +1,14 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction indicative of use of Mimikatz modules
+id: 966b635f-98e8-4aa4-9b49-47ed2cedcc85
+version: 1
 date: '2020-10-21'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. Mimikatz
   is a collection of tools and modules commonly employed in Windows exploits.
-eli5: This detection identifies use of Mimikatz modules when they are specified directly
-  in a command line. This is less common use of Mimikatz, as most demos show interactive
-  use of this tool. Nevertheless, specifying Mimikatz modules in a command line is
-  possible and enables scripted/programmed attacks.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 966b635f-98e8-4aa4-9b49-47ed2cedcc85
-known_false_positives: None identified.
-name: Credential Extraction indicative of use of Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -29,6 +23,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -49,5 +48,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_ms_debuggers_kernel_peek.yml b/detections/endpoint/ssa___credential_extraction_ms_debuggers_kernel_peek.yml
index 047d28778c..319a629ae4 100644
--- a/detections/endpoint/ssa___credential_extraction_ms_debuggers_kernel_peek.yml
+++ b/detections/endpoint/ssa___credential_extraction_ms_debuggers_kernel_peek.yml
@@ -1,22 +1,15 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction native Microsoft debuggers peek into the kernel
+id: c20bb8ec-e1b0-4640-b0ef-3a4c54f8c112
+version: 1
 date: '2020-10-18'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. Native Microsoft
   debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential
   material directly from memory and process dumps.
-eli5: This detection spots when native Microsoft debuggers ask for something inside
-  the kernel (via ForceV1) while retrieving credentials.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: c20bb8ec-e1b0-4640-b0ef-3a4c54f8c112
-known_false_positives: Although unlikely, using debuggers this way may be indicative
-  of developers analyzing crash dumps of their code. Note, even for developers this
-  is an unusual way of working on code - debuggers are mostly used to step through
-  code, not analyze its crash dumps.
-name: Credential Extraction native Microsoft debuggers peek into the kernel
-references:
-- https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29
 search: ' | from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -31,6 +24,14 @@ search: ' | from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: Although unlikely, using debuggers this way may be indicative
+  of developers analyzing crash dumps of their code. Note, even for developers this
+  is an unusual way of working on code - debuggers are mostly used to step through
+  code, not analyze its crash dumps.
+references:
+- https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29
 tags:
   asset_type: Windows
   cis20:
@@ -53,5 +54,3 @@ tags:
   - process
   risk_severity: medium
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_ms_debuggers_z_option.yml b/detections/endpoint/ssa___credential_extraction_ms_debuggers_z_option.yml
index 603cdf2d05..f01749197a 100644
--- a/detections/endpoint/ssa___credential_extraction_ms_debuggers_z_option.yml
+++ b/detections/endpoint/ssa___credential_extraction_ms_debuggers_z_option.yml
@@ -1,21 +1,15 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction native Microsoft debuggers via z command line option
+id: adc51a77-90c9-4358-b43c-f10dd1a27d05
+version: 1
 date: '2020-10-18'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. Native Microsoft
   debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential
   material directly from memory and process dumps.
-eli5: This detects use of -z command line option which specifies location of the credentials
-  dump file to native Microsoft debuggers.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: adc51a77-90c9-4358-b43c-f10dd1a27d05
-known_false_positives: Although unlikely, using debuggers this way may be indicative
-  of developers analyzing crash dumps of their code. Note, even for developers this
-  is an unusual way of working on code - debuggers are mostly used to step through
-  code, not analyze its crash dumps.
-name: Credential Extraction native Microsoft debuggers via z command line option
-references: []
 search: ' | from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -27,6 +21,13 @@ search: ' | from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: Although unlikely, using debuggers this way may be indicative
+  of developers analyzing crash dumps of their code. Note, even for developers this
+  is an unusual way of working on code - debuggers are mostly used to step through
+  code, not analyze its crash dumps.
+references: []
 tags:
   asset_type: Windows
   cis20:
@@ -48,5 +49,3 @@ tags:
   - process
   risk_severity: medium
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___credential_extraction_powersploit_modules.yml b/detections/endpoint/ssa___credential_extraction_powersploit_modules.yml
index ee648a4ae8..dd77c7a10e 100644
--- a/detections/endpoint/ssa___credential_extraction_powersploit_modules.yml
+++ b/detections/endpoint/ssa___credential_extraction_powersploit_modules.yml
@@ -1,17 +1,14 @@
-author: Stanislav Miskovic, Splunk
+name: Credential Extraction indicative of use of PowerSploit modules
+id: 5f1186a4-e681-446e-851c-dc9574ad28eb
+version: 1
 date: '2020-10-21'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: Credential extraction is often an illegal recovery of credential material
   from secured authentication resources and repositories. This process may also involve
   decryption or other transformations of the stored credential material. PowerSploit
   is a collection of Microsoft PowerShell modules commonly employed in exploits.
-eli5: This detection identifies use of PowerSploit modules for credential extraction.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 5f1186a4-e681-446e-851c-dc9574ad28eb
-known_false_positives: None identified.
-name: Credential Extraction indicative of use of PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -27,6 +24,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -47,5 +49,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___detect_dump_lsass_memory_using_comsvcs.yml b/detections/endpoint/ssa___detect_dump_lsass_memory_using_comsvcs.yml
index bc9166ae1e..a216a7dac2 100644
--- a/detections/endpoint/ssa___detect_dump_lsass_memory_using_comsvcs.yml
+++ b/detections/endpoint/ssa___detect_dump_lsass_memory_using_comsvcs.yml
@@ -1,16 +1,12 @@
-author: Jose Hernandez, Splunk
+name: Detect Dump LSASS Memory using comsvcs - SSA
+id: 76bb9e35-f314-4c3d-a385-83c72a13ce4e
+version: 1
 date: '2020-09-15'
+author: Jose Hernandez, Splunk
+type: streaming
+datamodel: []
 description: This search detects the memory of lsass.exe being dumped for offline
   credential theft attack.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including Windows command line logging. You can see how we test this with [Event
-  Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a)
-  on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml).
-id: 76bb9e35-f314-4c3d-a385-83c72a13ce4e
-known_false_positives: None identified.
-name: Detect Dump LSASS Memory using comsvcs - SSA
-references:
-- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 search: '| from read_ssa_enriched_events() | eval tenant=ucast(map_get(input_event,
   "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"),
   "string", null), process_name=lower(ucast(map_get(input_event, "process_name"),
@@ -19,8 +15,15 @@ search: '| from read_ssa_enriched_events() | eval tenant=ucast(map_get(input_eve
   where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine),
   body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including Windows command line logging. You can see how we test this with [Event
+  Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a)
+  on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml).
+known_false_positives: None identified.
+references:
+- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
   asset_type: Endpoint
   cis20:
@@ -42,5 +45,3 @@ tags:
   - process
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___detect_kerberoasting.yml b/detections/endpoint/ssa___detect_kerberoasting.yml
index e2c53569e4..c69e765be1 100644
--- a/detections/endpoint/ssa___detect_kerberoasting.yml
+++ b/detections/endpoint/ssa___detect_kerberoasting.yml
@@ -1,15 +1,12 @@
-author: Xiao Lin, Splunk
+name: Detect Kerberoasting - SSA
+id: dabdd6d7-3e10-42be-8711-4e124f7a3850
+version: 1
 date: '2020-10-21'
+author: Xiao Lin, Splunk
+type: streaming
+datamodel: []
 description: This search detects a potential kerberoasting attack via service principal
   name requests
-how_to_implement: The test data is converted from Windows Security Event logs generated
-  from Attach Range simulation and used in SPL search and extended to SPL2
-id: dabdd6d7-3e10-42be-8711-4e124f7a3850
-known_false_positives: Older systems that support kerberos RC4 by default NetApp may
-  generate false positives
-name: Detect Kerberoasting - SSA
-references:
-- Initial ESCU implementation by Jose Hernandez and Patrick Bareiss
 search: ' | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_time"),
   EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event,
   "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"),
@@ -19,6 +16,12 @@ search: ' | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_
   | where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID
   | eval start_time=_time, end_time=_time, body="TBD", entities="TBD" | select start_time,
   end_time, entities, body | into write_ssa_detected_events(); '
+how_to_implement: The test data is converted from Windows Security Event logs generated
+  from Attach Range simulation and used in SPL search and extended to SPL2
+known_false_positives: Older systems that support kerberos RC4 by default NetApp may
+  generate false positives
+references:
+- Initial ESCU implementation by Jose Hernandez and Patrick Bareiss
 tags:
   cis20:
   - CIS 8
@@ -40,5 +43,3 @@ tags:
   - ticket_options
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___detect_pass_hash.yml b/detections/endpoint/ssa___detect_pass_hash.yml
index e7f5b813e1..5c8e01cdc4 100644
--- a/detections/endpoint/ssa___detect_pass_hash.yml
+++ b/detections/endpoint/ssa___detect_pass_hash.yml
@@ -1,15 +1,12 @@
-author: Xiao Lin, Splunk
+name: Detect Pass the Hash - SSA
+id: 7cd8b9fa-6b0c-424f-92a6-9c5287a72f5f
+version: 1
 date: '2020-10-21'
+author: Xiao Lin, Splunk
+type: streaming
+datamodel: []
 description: This search looks for specific authentication events from the Windows
   Security Event logs to detect potential attempts using Pass-the-Hash technique.
-how_to_implement: The test data is converted from Windows Security Event logs generated
-  from Attach Range simulation and used in SPL search and extended to SPL2
-id: 7cd8b9fa-6b0c-424f-92a6-9c5287a72f5f
-known_false_positives: Legitimate logon activity by authorized NTLM systems may be
-  detected by this search. Please investigate as appropriate.
-name: Detect Pass the Hash - SSA
-references:
-- Initial ESCU implementation by Bhavin Patel and Patrick Bareiss
 search: ' | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_time"),
   EventCode=map_get(input_event, "event_code"), LogonType=map_get(input_event, "logon_type"),
   LogonProcess=map_get(input_event, "logon_process"), ComputerName=map_get(input_event,
@@ -19,6 +16,12 @@ search: ' | from read_ssa_enriched_events() | eval _time=map_get(input_event, "_
   | where first_time_EventCode_LogonProcess_ComputerName | eval start_time=_time,
   end_time=_time, body="TBD", entities="TBD" | select start_time, end_time, entities,
   body | into write_ssa_detected_events(); '
+how_to_implement: The test data is converted from Windows Security Event logs generated
+  from Attach Range simulation and used in SPL search and extended to SPL2
+known_false_positives: Legitimate logon activity by authorized NTLM systems may be
+  detected by this search. Please investigate as appropriate.
+references:
+- Initial ESCU implementation by Bhavin Patel and Patrick Bareiss
 tags:
   cis20:
   - CIS 3
@@ -44,5 +47,3 @@ tags:
   - logon_type
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___first_time_seen_cmd_line.yml b/detections/endpoint/ssa___first_time_seen_cmd_line.yml
index 73d5a2e4ba..13dbb48ce4 100644
--- a/detections/endpoint/ssa___first_time_seen_cmd_line.yml
+++ b/detections/endpoint/ssa___first_time_seen_cmd_line.yml
@@ -1,43 +1,35 @@
+name: First time seen command line argument - SSA
+id: fc0edc95-ff2b-48b0-9f6f-63da3789fd23
+version: 2
+date: 2021-2-1
 author: Ignacio Bermudez Corrales, Splunk
-date: '2021-2-1'
+type: streaming
+datamodel: []
 description: This search looks for command-line arguments that use a `/c` parameter
   to execute a command that has not previously been seen. This is an implementation
   on SPL2 of the rule `First time seen command line argument` by @bpatel.
-id: fc0edc95-ff2b-48b0-9f6f-63da3789fd23
-how_to_implement: "You must be populating the endpoint data model for SSA and specifically the process_name and the process fields"
-eli5: "The subsearch returns all events where `cmd.exe` was used with a `/c` parameter
-  in the command-line arguments to execute other commands/programs. It appends the
-  historical data to those results in the lookup file. Next, it recalculates the `firstTime`
-  and `lastTime` field for command-line execution and outputs this data to the lookup
-  file to update the local cache. It returns only those events that have first been
-  seen in the past one hour. This is combined with the main search to return the time,
-  user, destination, process, parent process, and value of the command-line argument."
+search: '| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event,
+  "_time"), "string", null)) | eval dest_user_id=ucast(map_get(input_event, "dest_user_id"),
+  "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string",
+  null), process_name=ucast(map_get(input_event, "process_name"), "string", null),
+  cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line),
+  cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/,
+  "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/,
+  "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext"
+  */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"),
+  /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm,
+  /\d+/, "N") | where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string",
+  ""), /.* \/[cC] .*/)=true | select cmd_line, cmd_line_norm, timestamp, dest_device_id,
+  dest_user_id | first_time_event input_columns=["cmd_line_norm"] | where first_time_cmd_line_norm
+  | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id,
+  dest_user_id), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be populating the endpoint data model for SSA and specifically
+  the process_name and the process fields
 known_false_positives: Legitimate programs can also use command-line arguments to
   execute. Please verify the command-line arguments to check what command/program
   is being executed. We recommend customizing the `first_time_seen_cmd_line_filter`
   macro to exclude legitimate parent_process_name
-name: First time seen command line argument - SSA
-search: '| from read_ssa_enriched_events()
-| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null))
-| eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null),
-       dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null),
-       process_name=ucast(map_get(input_event, "process_name"), "string", null),
-       cmd_line=ucast(map_get(input_event, "process"), "string", null),
-       cmd_line_norm=lower(cmd_line),
-       cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"),
-       cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */
-       cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */
-       cmd_line_norm=replace(cmd_line_norm, /\d+/, "N")
-| where process_name="cmd.exe" AND
-        match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true
-| select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id
-| first_time_event input_columns=["cmd_line_norm"]
-| where first_time_cmd_line_norm
-| eval start_time = timestamp,
-       end_time = timestamp,
-       entities = mvappend(dest_device_id, dest_user_id),
-       body = "TBD"
-| into write_ssa_detected_events();'
+references: []
 tags:
   cis20:
   - CIS 3
@@ -68,5 +60,3 @@ tags:
   - process
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 2
diff --git a/detections/endpoint/ssa___illegal_access_user_content_via_powersploit_modules.yml b/detections/endpoint/ssa___illegal_access_user_content_via_powersploit_modules.yml
index 4e0df4a799..932ff82f8b 100644
--- a/detections/endpoint/ssa___illegal_access_user_content_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___illegal_access_user_content_via_powersploit_modules.yml
@@ -1,18 +1,13 @@
-author: Stanislav Miskovic, Splunk
+name: Illegal Access To User Content via PowerSploit modules
+id: 01fc7d91-eb0c-478e-8633-e4fa4904463a
+version: 1
 date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that enable illegaly
   access user content, such as key logging, audio recording, screenshots, tapping
   into http and RDP sessions, etc.
-eli5: This detection identifies access to PowerSploit modules that enable illegaly
-  access user content, such as key logging, audio recording, screenshots, tapping
-  into http and RDP sessions, etc.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 01fc7d91-eb0c-478e-8633-e4fa4904463a
-known_false_positives: None identified.
-name: Illegal Access To User Content via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -26,6 +21,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -50,5 +50,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_account_creation_via_powersploit_modules.yml b/detections/endpoint/ssa___illegal_account_creation_via_powersploit_modules.yml
index 3108f60bf5..7295c87e80 100644
--- a/detections/endpoint/ssa___illegal_account_creation_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___illegal_account_creation_via_powersploit_modules.yml
@@ -1,15 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Illegal Account Creation via PowerSploit modules
+id: 20fba62a-fa5b-46cc-b39f-473fa248fee2
+version: 1
 date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that create accounts
   illegaly.
-eli5: This detection identifies use of PowerSploit modules that create accounts illegaly.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 20fba62a-fa5b-46cc-b39f-473fa248fee2
-known_false_positives: None identified.
-name: Illegal Account Creation via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -19,6 +16,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -40,5 +42,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_account_enable_disable_via_dsinternals_modules.yml b/detections/endpoint/ssa___illegal_account_enable_disable_via_dsinternals_modules.yml
index e07dd91688..708b8caa17 100644
--- a/detections/endpoint/ssa___illegal_account_enable_disable_via_dsinternals_modules.yml
+++ b/detections/endpoint/ssa___illegal_account_enable_disable_via_dsinternals_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Illegal Enabling or Disabling of Accounts via DSInternals modules
+id: 3e0f9962-9989-445f-878c-939443326b63
+version: 1
 date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of DSInternals modules that enable or disable
   accounts illegaly.
-eli5: This detection identifies use of DSInternals modules that enable or disable
-  accounts illegaly.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 3e0f9962-9989-445f-878c-939443326b63
-known_false_positives: None identified.
-name: Illegal Enabling or Disabling of Accounts via DSInternals modules
-references:
-- https://github.com/MichaelGrafnetter/DSInternals
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -21,6 +17,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/MichaelGrafnetter/DSInternals
 tags:
   asset_type: Windows
   cis20:
@@ -43,5 +44,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_log_deletion_via_mimikatz_modules.yml b/detections/endpoint/ssa___illegal_log_deletion_via_mimikatz_modules.yml
index 646fabb0f1..801c3a48a3 100644
--- a/detections/endpoint/ssa___illegal_log_deletion_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___illegal_log_deletion_via_mimikatz_modules.yml
@@ -1,15 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Illegal Deletion of Logs via Mimikatz modules
+id: 4ddb3b0d-f95f-4ae2-b4e8-663296453a7b
+version: 1
 date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that delete event
   logs.
-eli5: This detection identifies access to PowerSploit modules that delete event logs.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 4ddb3b0d-f95f-4ae2-b4e8-663296453a7b
-known_false_positives: None identified.
-name: Illegal Deletion of Logs via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -20,6 +17,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -41,5 +43,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_management_AD_elements_and_policies_via_dsinternals_modules.yml b/detections/endpoint/ssa___illegal_management_AD_elements_and_policies_via_dsinternals_modules.yml
index 21f8403896..9cae8d5559 100644
--- a/detections/endpoint/ssa___illegal_management_AD_elements_and_policies_via_dsinternals_modules.yml
+++ b/detections/endpoint/ssa___illegal_management_AD_elements_and_policies_via_dsinternals_modules.yml
@@ -1,17 +1,13 @@
-author: Stanislav Miskovic, Splunk
-date: '2020-11-09'
-description: This detection identifies use of DSInternals modules for illegal management
-  of Active Directoty elements and policies.
-eli5: This detection identifies use of DSInternals modules for illegal management
-  of Active Directoty elements and policies.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: a587ca9f-c138-47b4-ba51-699f319b8cc5
-known_false_positives: None identified.
 name: Illegal Management of Active Directory Elements and Policies via DSInternals
   modules
-references:
-- https://github.com/MichaelGrafnetter/DSInternals
+id: a587ca9f-c138-47b4-ba51-699f319b8cc5
+version: 1
+date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
+description: This detection identifies use of DSInternals modules for illegal management
+  of Active Directoty elements and policies.
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -23,6 +19,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/MichaelGrafnetter/DSInternals
 tags:
   asset_type: Windows
   cis20:
@@ -46,5 +47,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_management_computers_and_AD_elements_via_powersploit_modules.yml b/detections/endpoint/ssa___illegal_management_computers_and_AD_elements_via_powersploit_modules.yml
index 679fc2a5c3..b10f0022d7 100644
--- a/detections/endpoint/ssa___illegal_management_computers_and_AD_elements_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___illegal_management_computers_and_AD_elements_via_powersploit_modules.yml
@@ -1,17 +1,13 @@
-author: Stanislav Miskovic, Splunk
-date: '2020-11-09'
-description: This detection identifies access to PowerSploit modules that enable illegal
-  management of computers and Active Directory elements.
-eli5: This detection identifies access to PowerSploit modules that enable illegal
-  management of computers and Active Directory elements.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 75760c11-7d48-4968-b828-013b299e8f6d
-known_false_positives: None identified.
 name: Illegal Management of Computers and Active Directory Elements via PowerSploit
   modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
+id: 75760c11-7d48-4968-b828-013b299e8f6d
+version: 1
+date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
+description: This detection identifies access to PowerSploit modules that enable illegal
+  management of computers and Active Directory elements.
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -24,6 +20,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -47,5 +48,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_privilege_elevation_and_persistence_via_powersploit_modules.yml b/detections/endpoint/ssa___illegal_privilege_elevation_and_persistence_via_powersploit_modules.yml
index 36260dcfd1..5fca5d3491 100644
--- a/detections/endpoint/ssa___illegal_privilege_elevation_and_persistence_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___illegal_privilege_elevation_and_persistence_via_powersploit_modules.yml
@@ -1,18 +1,13 @@
-author: Stanislav Miskovic, Splunk
+name: Illegal Privilege Elevation and Persistence via PowerSploit modules
+id: 88c10ee9-fe72-4bce-b343-5b129044b991
+version: 1
 date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that illegaly
   elevate general privileges or ensure persistence, e.g., enable manipulation of registry,
   task scheduling, persistent WMI, access to OS objects under desired identities.
-eli5: This detection identifies access to PowerSploit modules that illegaly elevate
-  general privileges or ensure persistence, e.g., enable registry manipulation, task
-  scheduling, persistent WMI,  access to OS objects under desired identities.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 88c10ee9-fe72-4bce-b343-5b129044b991
-known_false_positives: None identified.
-name: Illegal Privilege Elevation and Persistence via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -25,6 +20,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -48,5 +48,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_privilege_elevation_via_mimikatz_modules.yml b/detections/endpoint/ssa___illegal_privilege_elevation_via_mimikatz_modules.yml
index a0b91871b2..dfc7caecd4 100644
--- a/detections/endpoint/ssa___illegal_privilege_elevation_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___illegal_privilege_elevation_via_mimikatz_modules.yml
@@ -1,15 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Illegal Privilege Elevation via Mimikatz modules
+id: 2f873b1f-6352-4844-b7b9-b419f09a42c7
+version: 1
 date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of Mimikatz modules for illegal privilege
   elevation.
-eli5: This detection identifies use of Mimikatz modules for illegal privilege elevation.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 2f873b1f-6352-4844-b7b9-b419f09a42c7
-known_false_positives: None identified.
-name: Illegal Privilege Elevation via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -20,6 +17,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -42,5 +44,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_service_and_process_control_via_mimikatz_modules.yml b/detections/endpoint/ssa___illegal_service_and_process_control_via_mimikatz_modules.yml
index f485fba61e..e48c0e2dc7 100644
--- a/detections/endpoint/ssa___illegal_service_and_process_control_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___illegal_service_and_process_control_via_mimikatz_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Illegal Service and Process Control via Mimikatz modules
+id: aaf3adf1-73e1-4477-b4ee-3771898964f1
+version: 1
 date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of Mimikatz modules for illegal control
   over services and processes, including the authentication service.
-eli5: This detection identifies use of Mimikatz modules for illegal control over services
-  and processes, including the authentication service.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: aaf3adf1-73e1-4477-b4ee-3771898964f1
-known_false_positives: None identified.
-name: Illegal Service and Process Control via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -24,6 +20,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -47,5 +48,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___illegal_service_and_process_control_via_powersploit_modules.yml b/detections/endpoint/ssa___illegal_service_and_process_control_via_powersploit_modules.yml
index f4d4b6e8f6..f4873171d8 100644
--- a/detections/endpoint/ssa___illegal_service_and_process_control_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___illegal_service_and_process_control_via_powersploit_modules.yml
@@ -1,20 +1,14 @@
-author: Stanislav Miskovic, Splunk
+name: Illegal Service and Process Control via PowerSploit modules
+id: 0e910e5b-309d-4bc3-8af2-0030c02aa353
+version: 1
 date: '2020-11-09'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that enable illegal
   control of services and processes, such as installing or spoofing of malicious services,
   injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands,
   modifying access to service objects, etc.
-eli5: This detection identifies access to PowerSploit modules that enable illegal
-  control of services and processes, such as installing or spoofing of malicious services,
-  injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands,
-  modifying access to service objects, etc.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 0e910e5b-309d-4bc3-8af2-0030c02aa353
-known_false_positives: None identified.
-name: Illegal Service and Process Control via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -34,6 +28,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -57,5 +56,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___probing_access_with_stolen_credentials_via_powersploit_modules.yml b/detections/endpoint/ssa___probing_access_with_stolen_credentials_via_powersploit_modules.yml
index 4714b73694..49aabffa48 100644
--- a/detections/endpoint/ssa___probing_access_with_stolen_credentials_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___probing_access_with_stolen_credentials_via_powersploit_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Probing Access with Stolen Credentials via PowerSploit modules
+id: d405af5d-99f1-45af-8dfb-b8f98b764247
+version: 1
 date: '2020-11-04'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of PowerSploit modules that facilitate
   access probing with admin credentials as well as probing access to system services.
-eli5: This detection identifies use of PowerSploit modules that probe use of admin
-  credentials as well as access to system services.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: d405af5d-99f1-45af-8dfb-b8f98b764247
-known_false_positives: None identified.
-name: Probing Access with Stolen Credentials via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -22,6 +18,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -44,5 +45,3 @@ tags:
   - dest_device_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___prohibited_apps_spawning_cmdprompt.yml b/detections/endpoint/ssa___prohibited_apps_spawning_cmdprompt.yml
index f0d43db6c4..51185fe9cb 100644
--- a/detections/endpoint/ssa___prohibited_apps_spawning_cmdprompt.yml
+++ b/detections/endpoint/ssa___prohibited_apps_spawning_cmdprompt.yml
@@ -1,25 +1,14 @@
-author: Ignacio Bermudez Corrales, Splunk
+name: Detect Prohibited Applications Spawning cmd exe - SSA
+id: c10a18cb-fd80-4ffa-a844-25026e0a0c94
+version: 1
 date: 2020-7-13
+author: Ignacio Bermudez Corrales, Splunk
+type: streaming
+datamodel: []
 description: This search looks for executions of cmd.exe spawned by a process that
   is often abused by attackers and that does not typically launch cmd.exe. This is
   a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe`
   by @bpatel.
-eli5: Obtaining access to the Command-Line Interface (CLI) is typically a primary
-  attacker goal. Once an attacker has obtained the ability to execute code on a target
-  system, they will often further manipulate the system via commands passed to the
-  CLI. It is also unusual for many applications to spawn a command shell during normal
-  operation, while it is often observed if an application has been compromised in
-  some way. As such, it is often beneficial to look for cmd.exe being executed by
-  processes that are often targeted for exploitation, or that would not spawn cmd.exe
-  in any other circumstances. A lookup file is provided to easily modify the processes
-  that are being watched for execution of cmd.exe.
-how_to_implement: You must be ingesting sysmon logs. This search has been modified
-  to process raw sysmon data from attack_range's nxlogs on DSP.
-id: c10a18cb-fd80-4ffa-a844-25026e0a0c94
-known_false_positives: There are circumstances where an application may legitimately
-  execute and interact with the Windows command-line interface. Investigate and modify
-  the lookup file, as appropriate.
-name: Detect Prohibited Applications Spawning cmd exe - SSA
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null))
@@ -36,6 +25,12 @@ search: '| from read_ssa_enriched_events()
 
   | eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id,
   dest_user_id), body="TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting sysmon logs. This search has been modified
+  to process raw sysmon data from attack_range's nxlogs on DSP.
+known_false_positives: There are circumstances where an application may legitimately
+  execute and interact with the Windows command-line interface. Investigate and modify
+  the lookup file, as appropriate.
+references: []
 tags:
   cis20:
   - CIS 8
@@ -56,5 +51,3 @@ tags:
   - dest_user_id
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml b/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml
index baf88556f6..cae920db5c 100644
--- a/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml
+++ b/detections/endpoint/ssa___rare_parent_process_relationship_lolbas.yml
@@ -1,18 +1,14 @@
-author: Ignacio Bermudez Corrales, Splunk
+name: Rare Parent-Child Process Relationship
+id: e03aa905-6549-4e34-b304-7a922185b2c4
+version: 1
 date: '2020-08-13'
+author: Ignacio Bermudez Corrales, Splunk
+type: streaming
+datamodel: []
 description: An attacker may use LOLBAS tools spawned from vulnerable applications
   not typically used by system administrators. This search leverages the Splunk Streaming
   ML DSP plugin to find rare parent/child relationships. The list of application has
   been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries
-how_to_implement: Collect endpoint data such as sysmon or 4688 events.
-id: e03aa905-6549-4e34-b304-7a922185b2c4
-known_false_positives: 'Some custom tools used by admins could be used rarely to launch
-  remotely applications. This might trigger false positives at the beginning when
-  it hasn''t collected yet enough data to construct the baseline.
-
-  '
-name: Rare Parent-Child Process Relationship
-references: []
 search: '| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event,
   "_time"), "string", null)) | eval parent_process=lower(ucast(map_get(input_event,
   "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process,
@@ -52,6 +48,13 @@ search: '| from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map
 
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id,
   dest_user_id), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: Collect endpoint data such as sysmon or 4688 events.
+known_false_positives: 'Some custom tools used by admins could be used rarely to launch
+  remotely applications. This might trigger false positives at the beginning when
+  it hasn''t collected yet enough data to construct the baseline.
+
+  '
+references: []
 tags:
   cis20:
   - CIS 8
@@ -75,5 +78,3 @@ tags:
   - dest_user_id
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_access_and_persistence_opportunities_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_access_and_persistence_opportunities_via_powersploit_modules.yml
index 242fca9c04..c9e5137bbb 100644
--- a/detections/endpoint/ssa___recon_access_and_persistence_opportunities_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_access_and_persistence_opportunities_via_powersploit_modules.yml
@@ -1,22 +1,15 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance of Access and Persistence Opportunities via PowerSploit modules
+id: 3d8bd7f3-1061-4ac7-9225-6764cc0684d7
+version: 1
 date: '2020-11-05'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of PowerSploit modules that discover opportunities
   for malicious access and persistence. Some examples include access to admin accounts,
   weak access control policies, landing paths for dropping malicious software or data
   to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities,
   as well as services and system files that can be compromised.
-eli5: This detection identifies use of PowerSploit modules that discover access and
-  persistence opportunities, including access to admin accounts, weak access control
-  policies, landing paths for dropping malicious software or data to exfiltrate, registry
-  locations to land autorun parameters, task scheduling opportunities, as well as
-  services and system files that can be compromised.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 3d8bd7f3-1061-4ac7-9225-6764cc0684d7
-known_false_positives: None identified.
-name: Reconnaissance of Access and Persistence Opportunities via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -36,6 +29,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -62,5 +60,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_and_use_accounts_groups_policies_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_and_use_accounts_groups_policies_via_powersploit_modules.yml
index 696abe8b15..3b17eac833 100644
--- a/detections/endpoint/ssa___recon_and_use_accounts_groups_policies_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_and_use_accounts_groups_policies_via_powersploit_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules
+id: 63422f8e-766c-468f-8133-2ba6795e263b
+version: 1
 date: '2020-11-05'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that discover
   accounts, groups and policies that can be accessed or taken over.
-eli5: This detection identifies use of PowerSploit modules that discover accounts,
-  groups and policies that can be accessed or taken over.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 63422f8e-766c-468f-8133-2ba6795e263b
-known_false_positives: None identified.
-name: Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -42,6 +38,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -65,5 +66,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_and_use_accounts_groups_via_mimikatz_modules.yml b/detections/endpoint/ssa___recon_and_use_accounts_groups_via_mimikatz_modules.yml
index 6167760a40..2f883249b5 100644
--- a/detections/endpoint/ssa___recon_and_use_accounts_groups_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___recon_and_use_accounts_groups_via_mimikatz_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Accounts and Groups via Mimikatz modules
+id: 1bce67aa-3fc4-4886-9089-67f0bfebbef6
+version: 1
 date: '2020-11-05'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of Mimikatz modules for discovery of accounts
   and groups and access to them.
-eli5: This detection identifies use of Mimikatz modules for discovery of accounts
-  and groups and access to them.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 1bce67aa-3fc4-4886-9089-67f0bfebbef6
-known_false_positives: None identified.
-name: Reconnaissance and Access to Accounts and Groups via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -21,6 +17,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -44,5 +45,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_and_use_active_directory_infrastructure_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_and_use_active_directory_infrastructure_via_powersploit_modules.yml
index fe8e0fe659..79294b6bc3 100644
--- a/detections/endpoint/ssa___recon_and_use_active_directory_infrastructure_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_and_use_active_directory_infrastructure_via_powersploit_modules.yml
@@ -1,19 +1,14 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit
+  modules
+id: db08ac40-ee14-43e9-9a75-dddd059ef812
+version: 1
 date: '2020-11-06'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules for reconnaissance
   and access to elements of Active Directory infrastructure, such as domain identifiers,
   AD sites and forests, and trust relations.
-eli5: This detection identifies use of PowerSploit modules for reconnaissance and
-  access to Active Directory infrastructure - identifiers, sites, forests and trust
-  relations.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: db08ac40-ee14-43e9-9a75-dddd059ef812
-known_false_positives: None identified.
-name: Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit
-  modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -33,6 +28,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -58,5 +58,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_and_use_computers_domains_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_and_use_computers_domains_via_powersploit_modules.yml
index bc3bb683c6..85e186cde5 100644
--- a/detections/endpoint/ssa___recon_and_use_computers_domains_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_and_use_computers_domains_via_powersploit_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Computers and Domains via PowerSploit modules
+id: fe1c4c5a-09f3-4b43-8129-560a7f38a08b
+version: 1
 date: '2020-11-06'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that discover
   computers, servers and domains that can be accessed or taken over.
-eli5: This detection identifies use of PowerSploit modules that discover computers,
-  servers and domains that can be accessed or taken over.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: fe1c4c5a-09f3-4b43-8129-560a7f38a08b
-known_false_positives: None identified.
-name: Reconnaissance and Access to Computers and Domains via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -25,6 +21,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -48,5 +49,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_and_use_computers_via_mimikatz_modules.yml b/detections/endpoint/ssa___recon_and_use_computers_via_mimikatz_modules.yml
index b41f3d12a9..8cd9d7b26b 100644
--- a/detections/endpoint/ssa___recon_and_use_computers_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___recon_and_use_computers_via_mimikatz_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Computers via Mimikatz modules
+id: 48664505-7d22-44ee-87d2-4c8a5bdc3d14
+version: 1
 date: '2020-11-06'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of Mimikatz modules for discovery of computers
   and servers and access to them.
-eli5: This detection identifies use of Mimikatz modules for discovery of computers
-  and servers and access to them.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 48664505-7d22-44ee-87d2-4c8a5bdc3d14
-known_false_positives: None identified.
-name: Reconnaissance and Access to Computers via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -20,6 +16,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -41,5 +42,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_and_use_operating_system_elements_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_and_use_operating_system_elements_via_powersploit_modules.yml
index 6ffcd379f5..943a54c3d8 100644
--- a/detections/endpoint/ssa___recon_and_use_operating_system_elements_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_and_use_operating_system_elements_via_powersploit_modules.yml
@@ -1,18 +1,13 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Operating System Elements via PowerSploit modules
+id: c1d33ad9-1727-4f9f-a474-4adbe4fed68a
+version: 1
 date: '2020-11-06'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that discover
   and access operating system elements, such as processes, services, registry locations,
   security packages and files.
-eli5: This detection identifies use of PowerSploit modules that discover and access
-  system resources, such as processes, services, registry locations, security packages
-  and files.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: c1d33ad9-1727-4f9f-a474-4adbe4fed68a
-known_false_positives: None identified.
-name: Reconnaissance and Access to Operating System Elements via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -28,6 +23,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -56,5 +56,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_and_use_shares_via_mimikatz_modules.yml b/detections/endpoint/ssa___recon_and_use_shares_via_mimikatz_modules.yml
index 53319215c3..9e5d48b30b 100644
--- a/detections/endpoint/ssa___recon_and_use_shares_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___recon_and_use_shares_via_mimikatz_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Shared Resources via Mimikatz modules
+id: c97b6eb9-1d8b-4017-bbbb-2af7fc17bc3f
+version: 1
 date: '2020-11-06'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of Mimikatz modules for discovery and access
   to network shares.
-eli5: This detection identifies use of Mimikatz modules for discovery and access to
-  network shares.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: c97b6eb9-1d8b-4017-bbbb-2af7fc17bc3f
-known_false_positives: None identified.
-name: Reconnaissance and Access to Shared Resources via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -20,6 +16,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -43,5 +44,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_and_use_shares_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_and_use_shares_via_powersploit_modules.yml
index ee26a9e979..585c65b3ab 100644
--- a/detections/endpoint/ssa___recon_and_use_shares_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_and_use_shares_via_powersploit_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Shared Resources via PowerSploit modules
+id: 6b7ca431-6b1e-4b40-9589-21cb368e369e
+version: 1
 date: '2020-11-06'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules that discover
   and access network and distributed file system shares.
-eli5: This detection identifies use of PowerSploit modules that discover and access
-  network and distributed file system shares.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 6b7ca431-6b1e-4b40-9589-21cb368e369e
-known_false_positives: None identified.
-name: Reconnaissance and Access to Shared Resources via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -25,6 +21,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -48,5 +49,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_connectivity_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_connectivity_via_powersploit_modules.yml
index 11160c5528..fb6b7dc776 100644
--- a/detections/endpoint/ssa___recon_connectivity_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_connectivity_via_powersploit_modules.yml
@@ -1,15 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance of Connectivity via PowerSploit modules
+id: 525d32fd-65dd-4732-9b72-3cfc7ddddbd2
+version: 1
 date: '2020-11-06'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies access to PowerSploit modules for reconnaissance
   of connectivity.
-eli5: This detection identifies use of PowerSploit modules for reconnaissance of connectivity.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 525d32fd-65dd-4732-9b72-3cfc7ddddbd2
-known_false_positives: None identified.
-name: Reconnaissance of Connectivity via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -25,6 +22,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -48,5 +50,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_credential_stores_and_services_via_mimikatz_modules.yml b/detections/endpoint/ssa___recon_credential_stores_and_services_via_mimikatz_modules.yml
index 8f7f515161..9acc663a70 100644
--- a/detections/endpoint/ssa___recon_credential_stores_and_services_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___recon_credential_stores_and_services_via_mimikatz_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance of Credential Stores and Services via Mimikatz modules
+id: 5facee5b-79e4-47ab-b0e6-c625acc0554f
+version: 1
 date: '2020-11-03'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies reconnaissance of credential stores and use
   of CryptoAPI services by Mimikatz modules.
-eli5: This detection identifies use of Mimikatz modules for reconnaissance of credential
-  stores and use of CryptoAPIs for malicious purposes.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 5facee5b-79e4-47ab-b0e6-c625acc0554f
-known_false_positives: None identified.
-name: Reconnaissance of Credential Stores and Services via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -23,6 +19,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -49,5 +50,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_defensive_tools_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_defensive_tools_via_powersploit_modules.yml
index 761c0ae7ef..c4c33b45d7 100644
--- a/detections/endpoint/ssa___recon_defensive_tools_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_defensive_tools_via_powersploit_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance of Defensive Tools via PowerSploit modules
+id: 24b4e659-63a2-4e7b-89ac-87dd659c7110
+version: 1
 date: '2020-11-05'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of PowerSploit modules for assessment of
   presence of defensive tools.
-eli5: This detection identifies use of PowerSploit modules that assess presence of
-  defensive tools on a compromised host.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 24b4e659-63a2-4e7b-89ac-87dd659c7110
-known_false_positives: None identified.
-name: Reconnaissance of Defensive Tools via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -20,6 +16,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -42,5 +43,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_privilege_escalation_opportunities_via_powersploit_modules.yml b/detections/endpoint/ssa___recon_privilege_escalation_opportunities_via_powersploit_modules.yml
index 6f7c296c7e..0bbbbd47da 100644
--- a/detections/endpoint/ssa___recon_privilege_escalation_opportunities_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___recon_privilege_escalation_opportunities_via_powersploit_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules
+id: b9b4492c-2af8-449b-beb4-b1b78d963321
+version: 1
 date: '2020-11-05'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of PowerSploit modules for assessment of
   privilege escalation opportunities.
-eli5: This detection identifies use of PowerSploit modules that assess privilege escalation
-  opportunities.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: b9b4492c-2af8-449b-beb4-b1b78d963321
-known_false_positives: None identified.
-name: Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -20,6 +16,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -43,5 +44,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_process_service_hijacking_via_mimikatz_modules.yml b/detections/endpoint/ssa___recon_process_service_hijacking_via_mimikatz_modules.yml
index b31e8a5d26..131c82ca42 100644
--- a/detections/endpoint/ssa___recon_process_service_hijacking_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___recon_process_service_hijacking_via_mimikatz_modules.yml
@@ -1,21 +1,16 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules
+id: fc5c1cbd-7494-4314-aad2-458d6fd4fada
+version: 1
 date: '2020-11-05'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of Mimikatz modules for discovery of process
   or service hijacking opportunities via Microsoft Detours compatibility. Microsoft
   Detours is an open source library for intercepting, monitoring and instrumenting
   binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing
   the in-memory code for target functions. The Detours package also contains utilities
   to attach arbitrary DLLs and data segments called payloads to any Win32 binary.
-eli5: This detection identifies use of Mimikatz modules for discovery of process or
-  service hijacking opportunities.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: fc5c1cbd-7494-4314-aad2-458d6fd4fada
-known_false_positives: None identified.
-name: Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
-- https://en.wikipedia.org/wiki/Microsoft_Detours
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -25,6 +20,12 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
+- https://en.wikipedia.org/wiki/Microsoft_Detours
 tags:
   asset_type: Windows
   cis20:
@@ -48,5 +49,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___recon_processes_and_services_via_mimikatz_modules.yml b/detections/endpoint/ssa___recon_processes_and_services_via_mimikatz_modules.yml
index eb40c39247..f6f89efba7 100644
--- a/detections/endpoint/ssa___recon_processes_and_services_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___recon_processes_and_services_via_mimikatz_modules.yml
@@ -1,16 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Reconnaissance and Access to Processes and Services via Mimikatz modules
+id: 0243d37c-57c1-4182-bfd1-39b212255fc8
+version: 1
 date: '2020-11-06'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies use of Mimikatz modules for discovery and access
   to services and processes.
-eli5: This detection identifies use of Mimikatz modules for discovery and access to
-  services and processes.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 0243d37c-57c1-4182-bfd1-39b212255fc8
-known_false_positives: None identified.
-name: Reconnaissance and Access to Processes and Services via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -21,6 +17,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -44,5 +45,3 @@ tags:
   - dest_user_id
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___setting_credentials_via_dsinternals_modules.yml b/detections/endpoint/ssa___setting_credentials_via_dsinternals_modules.yml
index 773be29708..d59251a5a9 100644
--- a/detections/endpoint/ssa___setting_credentials_via_dsinternals_modules.yml
+++ b/detections/endpoint/ssa___setting_credentials_via_dsinternals_modules.yml
@@ -1,15 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Setting Credentials via DSInternals modules
+id: d5ef590f-9bde-49eb-9c63-2f5b62a65b9c
+version: 1
 date: '2020-11-03'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies illegal setting of credentials via DSInternals
   modules.
-eli5: This detection identifies use of DSInternals modules that set credentials illegaly.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: d5ef590f-9bde-49eb-9c63-2f5b62a65b9c
-known_false_positives: None identified.
-name: Setting Credentials via DSInternals modules
-references:
-- https://github.com/MichaelGrafnetter/DSInternals
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -25,6 +22,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/MichaelGrafnetter/DSInternals
 tags:
   asset_type: Windows
   cis20:
@@ -51,5 +53,3 @@ tags:
   - process
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___setting_credentials_via_mimikatz_modules.yml b/detections/endpoint/ssa___setting_credentials_via_mimikatz_modules.yml
index 292f9c2290..7e48a5013d 100644
--- a/detections/endpoint/ssa___setting_credentials_via_mimikatz_modules.yml
+++ b/detections/endpoint/ssa___setting_credentials_via_mimikatz_modules.yml
@@ -1,15 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Setting Credentials via Mimikatz modules
+id: c8b84699-7652-4363-910f-efd1ca82f780
+version: 1
 date: '2020-11-03'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies illegal setting of credentials via Mimikatz
   modules.
-eli5: This detection identifies use of Mimikatz modules that set credentials illegaly.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: c8b84699-7652-4363-910f-efd1ca82f780
-known_false_positives: None identified.
-name: Setting Credentials via Mimikatz modules
-references:
-- https://github.com/gentilkiwi/mimikatz
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -20,6 +17,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/gentilkiwi/mimikatz
 tags:
   asset_type: Windows
   cis20:
@@ -43,5 +45,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___setting_credentials_via_powersploit_modules.yml b/detections/endpoint/ssa___setting_credentials_via_powersploit_modules.yml
index a626cfc0ea..b06a294d1c 100644
--- a/detections/endpoint/ssa___setting_credentials_via_powersploit_modules.yml
+++ b/detections/endpoint/ssa___setting_credentials_via_powersploit_modules.yml
@@ -1,15 +1,12 @@
-author: Stanislav Miskovic, Splunk
+name: Setting Credentials via PowerSploit modules
+id: 07b2a501-f967-4ddc-9f56-2dce46dfce44
+version: 1
 date: '2020-11-03'
+author: Stanislav Miskovic, Splunk
+type: streaming
+datamodel: []
 description: This detection identifies illegal setting of credentials via PowerSploit
   modules.
-eli5: This detection identifies use of PowerSploit modules that set credentials illegaly.
-how_to_implement: You must be ingesting Windows Security logs from devices of interest,
-  including the event ID 4688 with enabled command line logging.
-id: 07b2a501-f967-4ddc-9f56-2dce46dfce44
-known_false_positives: None identified.
-name: Setting Credentials via PowerSploit modules
-references:
-- https://github.com/PowerShellMafia/PowerSploit
 search: '| from read_ssa_enriched_events()
 
   | eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)),
@@ -19,6 +16,11 @@ search: '| from read_ssa_enriched_events()
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event,
   "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"),
   "string", null)), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting Windows Security logs from devices of interest,
+  including the event ID 4688 with enabled command line logging.
+known_false_positives: None identified.
+references:
+- https://github.com/PowerShellMafia/PowerSploit
 tags:
   asset_type: Windows
   cis20:
@@ -42,5 +44,3 @@ tags:
   - _time
   risk_severity: high
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___system_process_running_unexpected_location.yml b/detections/endpoint/ssa___system_process_running_unexpected_location.yml
index 81248c09b2..7dae442017 100644
--- a/detections/endpoint/ssa___system_process_running_unexpected_location.yml
+++ b/detections/endpoint/ssa___system_process_running_unexpected_location.yml
@@ -1,16 +1,16 @@
-author: Ignacio Bermudez Corrales, Splunk
+name: System Process Running from Unexpected Location - SSA
+id: 28179107-099a-464a-94d3-08301e6c055f
+version: 1
 date: '2020-08-25'
+author: Ignacio Bermudez Corrales, Splunk
+type: streaming
+datamodel: []
 description: An attacker tries might try to use different version of a system command
   without overriding original, or they might try to avoid some detection running the
   process from a different folder. This detection checks that a list of system processes
   run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes
   has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv
   and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml
-how_to_implement: Collect endpoint data such as sysmon or 4688 events.
-id: 28179107-099a-464a-94d3-08301e6c055f
-known_false_positives: None
-name: System Process Running from Unexpected Location - SSA
-references: []
 search: ' $ssa_input = | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event,
   "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"),
   "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string",
@@ -227,6 +227,9 @@ search: ' $ssa_input = | from read_ssa_enriched_events() | eval device=ucast(map
   union $cond_6 | where process_path!="c:\\windows\\system32" AND process_path!="c:\\windows\\syswow64"
   | eval start_time = timestamp, end_time = timestamp, entities = mvappend(device,
   user), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: Collect endpoint data such as sysmon or 4688 events.
+known_false_positives: None
+references: []
 tags:
   cis20:
   - CIS 8
@@ -247,5 +250,3 @@ tags:
   - process_path
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___unusual_lolbas_in_short_period_of_time.yml b/detections/endpoint/ssa___unusual_lolbas_in_short_period_of_time.yml
index 916c805465..c12189596e 100644
--- a/detections/endpoint/ssa___unusual_lolbas_in_short_period_of_time.yml
+++ b/detections/endpoint/ssa___unusual_lolbas_in_short_period_of_time.yml
@@ -1,18 +1,13 @@
-author: Ignacio Bermudez Corrales, Splunk
+name: More than usual number of LOLBAS applications in short time period - SSA
+id: 59c0dd70-169c-4900-9a1f-bfcf13302f93
+version: 1
 date: '2020-08-25'
+author: Ignacio Bermudez Corrales, Splunk
+type: streaming
+datamodel: []
 description: Attacker activity may compromise executing several LOLBAS applications
   in conjunction to accomplish their objectives. We are looking for more than usual
   LOLBAS applications over a window of time, by building profiles per machine.
-how_to_implement: Collect endpoint data such as sysmon or 4688 events.
-id: 59c0dd70-169c-4900-9a1f-bfcf13302f93
-known_false_positives: 'Some administrative tasks may involve multiple use of LOLBAS
-  applications in a short period of time. This might trigger false positives at the
-  beginning when it hasn''t collected yet enough data to construct the baseline.
-
-  '
-name: More than usual number of LOLBAS applications in short time period - SSA
-references:
-- https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries
 search: ' | from read_ssa_enriched_events() | eval device=ucast(map_get(input_event,
   "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event,
   "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event,
@@ -46,6 +41,14 @@ search: ' | from read_ssa_enriched_events() | eval device=ucast(map_get(input_ev
   as timestamp | adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device"
   window=2419200000L | where label AND quantile>0.99 | eval start_time = window_start,
   end_time = timestamp, entities = mvappend(device), body = "TBD" | into write_ssa_detected_events();'
+how_to_implement: Collect endpoint data such as sysmon or 4688 events.
+known_false_positives: 'Some administrative tasks may involve multiple use of LOLBAS
+  applications in a short period of time. This might trigger false positives at the
+  beginning when it hasn''t collected yet enough data to construct the baseline.
+
+  '
+references:
+- https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries
 tags:
   cis20:
   - CIS 8
@@ -65,5 +68,3 @@ tags:
   - process_name
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/ssa___unusually_long_command_line.yml b/detections/endpoint/ssa___unusually_long_command_line.yml
index cd19383736..60b3d22433 100644
--- a/detections/endpoint/ssa___unusually_long_command_line.yml
+++ b/detections/endpoint/ssa___unusually_long_command_line.yml
@@ -1,20 +1,14 @@
-author: Ignacio Bermudez Corrales, Splunk
+name: Unusually Long Command Line - SSA
+id: 58f43aba-1775-445e-b19c-be2b87d83ae3
+version: 1
 date: '2020-10-06'
+author: Ignacio Bermudez Corrales, Splunk
+type: streaming
+datamodel: []
 description: Command lines that are extremely long may be indicative of malicious
   activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin
   to help identify command lines with lengths that are unusual for a given user. This
   detection is inspired on Unusually Long Command Line authored by Rico Valdez.
-how_to_implement: You must be ingesting sysmon endpoint data that monitors command
-  lines.
-id: 58f43aba-1775-445e-b19c-be2b87d83ae3
-known_false_positives: This detection may flag suspiciously long command lines when
-  there is not sufficient evidence (samples) for a given process that this detection
-  is tracking; or when there is high variability in the length of the command line
-  for the tracked process. Also, some legitimate applications may use long command
-  lines. Such is the case of Ansible, that encodes Powershell scripts using long base64.
-  Attackers may use this technique to obfuscate their payloads.
-name: Unusually Long Command Line - SSA
-references: []
 search: ' | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(map_get(input_event,
   "_time"), "string", null)) | eval cmd_line=ucast(map_get(input_event, "process"),
   "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string",
@@ -29,6 +23,15 @@ search: ' | from read_ssa_enriched_events() | eval timestamp=parse_long(ucast(ma
   "cmd_line"] | where first_time_dest_device_id_cmd_line | eval start_time = timestamp,
   end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body =
   "TBD" | into write_ssa_detected_events();'
+how_to_implement: You must be ingesting sysmon endpoint data that monitors command
+  lines.
+known_false_positives: This detection may flag suspiciously long command lines when
+  there is not sufficient evidence (samples) for a given process that this detection
+  is tracking; or when there is high variability in the length of the command line
+  for the tracked process. Also, some legitimate applications may use long command
+  lines. Such is the case of Ansible, that encodes Powershell scripts using long base64.
+  Attackers may use this technique to obfuscate their payloads.
+references: []
 tags:
   cis20:
   - CIS 8
@@ -47,5 +50,3 @@ tags:
   - process
   risk_severity: low
   security_domain: endpoint
-type: SSA
-version: 1
diff --git a/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml b/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml
index ddcd44c028..4252a742a7 100644
--- a/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml
+++ b/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml
@@ -1,29 +1,32 @@
-author: Michael Haag, Splunk
+name: Suspicious microsoft workflow compiler rename
+id: f0db4464-55d9-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-12'
+author: Michael Haag, Splunk
+type: batch
+datamodel: []
 description: The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe.
   Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319
   and is rarely utilized. When investigating, identify the executed code on disk and
   review. A spawned child process from microsoft.workflow.compiler.exe is uncommon.
   In any instance, microsoft.workflow.compiler.exe spawning from an Office product
   or any living off the land binary is highly suspect.
-how_to_implement: To successfully implement this search, you need to be ingesting
-  logs with the process name, parent process, and command-line executions from your
-  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
-  Sysmon TA.
-id: f0db4464-55d9-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, some legitimate applications may use a moved
-  copy of microsoft.workflow.compiler.exe, triggering a false positive.
-name: Suspicious microsoft workflow compiler rename
-references:
-- https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution
 search: '`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe)
   | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User,
   parent_process_name, process_name, OriginalFileName, process_path, CommandLine |
   rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
   `suspicious_microsoft_workflow_compiler_rename_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of microsoft.workflow.compiler.exe, triggering a false positive.
+references:
+- https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution
 tags:
-  analytics_story:
+  analytic_story:
   - Trusted Developer Utilities Proxy Execution
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -43,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml b/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml
index 041dfaf6ab..24cdd929a4 100644
--- a/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml
+++ b/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml
@@ -1,26 +1,30 @@
-author: Michael Haag, Splunk
+name: Suspicious microsoft workflow compiler usage
+id: 9bbc62e8-55d8-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-12'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies microsoft.workflow.compiler.exe usage.
   microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319
   and is rarely utilized. When investigating, identify the executed code on disk and
   review. It is not a commonly used process by many applications.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-id: 9bbc62e8-55d8-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, limited instances have been identified coming
-  from native Microsoft utilities similar to SCCM.
-name: Suspicious microsoft workflow compiler usage
-references:
-- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution
 search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
   as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe
   by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Although unlikely, limited instances have been identified coming
+  from native Microsoft utilities similar to SCCM.
+references:
+- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution
 tags:
-  analytics_story:
+  analytic_story:
   - Trusted Developer Utilities Proxy Execution
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/suspicious_msbuild_path.yml b/detections/endpoint/suspicious_msbuild_path.yml
index 5d2a1259b3..21360144b3 100644
--- a/detections/endpoint/suspicious_msbuild_path.yml
+++ b/detections/endpoint/suspicious_msbuild_path.yml
@@ -1,30 +1,34 @@
-author: Michael Haag, Splunk
+name: Suspicious msbuild path
+id: f5198224-551c-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-12'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies msbuild.exe executing from a non-standard
   path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319
   and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio
   will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however
   there are instances of build applications that will move or use a copy of MSBuild.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-id: f5198224-551c-11eb-ae93-0242ac130002
-known_false_positives: Some legitimate applications may use a moved copy of msbuild.exe,
-  triggering a false positive. Baselining of MSBuild.exe usage is recommended to better
-  understand it's path usage. Visual Studio runs an instance out of a path that will
-  need to be filtered on.
-name: Suspicious msbuild path
-references:
-- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
 search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
   as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe
   AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest
   Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)`| `suspicious_msbuild_path_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Some legitimate applications may use a moved copy of msbuild.exe,
+  triggering a false positive. Baselining of MSBuild.exe usage is recommended to better
+  understand it's path usage. Visual Studio runs an instance out of a path that will
+  need to be filtered on.
+references:
+- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
 tags:
-  analytics_story:
+  analytic_story:
   - Trusted Developer Utilities Proxy Execution MSBuild
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -45,5 +49,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/suspicious_msbuild_rename.yml b/detections/endpoint/suspicious_msbuild_rename.yml
index 9b51171451..d1144fcfc8 100644
--- a/detections/endpoint/suspicious_msbuild_rename.yml
+++ b/detections/endpoint/suspicious_msbuild_rename.yml
@@ -1,28 +1,31 @@
-author: Michael Haag, Splunk
+name: Suspicious MSBuild Rename
+id: 4006adac-5937-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-12'
+author: Michael Haag, Splunk
+type: batch
+datamodel: []
 description: The following analytic identifies renamed instances of msbuild.exe executing.
   Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and
   C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify
   the code executed and what is executing a renamed instance of MSBuild.
-how_to_implement: To successfully implement this search, you need to be ingesting
-  logs with the process name, parent process, and command-line executions from your
-  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
-  Sysmon TA.
-id: 4006adac-5937-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, some legitimate applications may use a moved
-  copy of msbuild, triggering a false positive.
-name: Suspicious MSBuild Rename
-references:
-- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
-- https://github.com/infosecn1nja/MaliciousMacroMSBuild/
 search: '`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe)
   | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User,
   parent_process_name, process_name, OriginalFileName, process_path, CommandLine |
   rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
   `suspicious_msbuild_rename_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of msbuild, triggering a false positive.
+references:
+- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
+- https://github.com/infosecn1nja/MaliciousMacroMSBuild/
 tags:
-  analytics_story:
+  analytic_story:
   - Trusted Developer Utilities Proxy Execution MSBuild
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -43,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/suspicious_msbuild_spawn.yml b/detections/endpoint/suspicious_msbuild_spawn.yml
index d6a76883f8..76fea9f03c 100644
--- a/detections/endpoint/suspicious_msbuild_spawn.yml
+++ b/detections/endpoint/suspicious_msbuild_spawn.yml
@@ -1,5 +1,11 @@
-author: Michael Haag, Splunk
+name: Suspicious MSBuild Spawn
+id: a115fba6-5514-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-12'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies wmiprvse.exe spawning msbuild.exe.
   This behavior is indicative of a COM object being utilized to spawn msbuild from
   wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using
@@ -7,24 +13,22 @@ description: The following analytic identifies wmiprvse.exe spawning msbuild.exe
   In a malicious instance, MSBuild.exe will spawn from non-standard processes and
   have no command line arguments. For example, MSBuild.exe spawning from explorer.exe,
   powershell.exe is far less common and should be investigated.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-id: a115fba6-5514-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, some legitimate applications may exhibit
-  this behavior, triggering a false positive.
-name: Suspicious MSBuild Spawn
-references:
-- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
 search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
   as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe
   AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process
   Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
+references:
+- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
 tags:
-  analytics_story:
+  analytic_story:
   - Trusted Developer Utilities Proxy Execution MSBuild
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -44,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/suspicious_mshta_child_process.yml b/detections/endpoint/suspicious_mshta_child_process.yml
index d378623fe0..5a9df018d5 100644
--- a/detections/endpoint/suspicious_mshta_child_process.yml
+++ b/detections/endpoint/suspicious_mshta_child_process.yml
@@ -1,20 +1,15 @@
-author: Michael Haag, Splunk
+name: Suspicious mshta child process
+id: 60023bb6-5500-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-12'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies child processes spawning from  "mshta.exe".
   The search will return the first time and last time these command-line arguments
   were used for these executions, as well as the target system, the user, parent process
   "mshta.exe" and its child process.
-how_to_implement: To successfully implement this search, you need to be ingesting
-  logs with the process name, parent process, and command-line executions from your
-  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
-  Sysmon TA.
-id: 60023bb6-5500-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, some legitimate applications may exhibit
-  this behavior, triggering a false positive.
-name: Suspicious mshta child process
-references:
-- https://github.com/redcanaryco/AtomicTestHarnesses
-- https://redcanary.com/blog/introducing-atomictestharnesses/
 search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
   as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe
@@ -25,8 +20,17 @@ search: '| tstats `security_content_summariesonly` count values(Processes.proces
   OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user
   | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `suspicious_mshta_child_process_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
+references:
+- https://github.com/redcanaryco/AtomicTestHarnesses
+- https://redcanary.com/blog/introducing-atomictestharnesses/
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious MSHTA Activity
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -46,5 +50,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/suspicious_mshta_spawn.yml b/detections/endpoint/suspicious_mshta_spawn.yml
index a7ac316d3b..1b379e204f 100644
--- a/detections/endpoint/suspicious_mshta_spawn.yml
+++ b/detections/endpoint/suspicious_mshta_spawn.yml
@@ -1,28 +1,32 @@
-author: Michael Haag, Splunk
+name: Suspicious mshta spawn
+id: 4d33a488-5b5f-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-20'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies wmiprvse.exe spawning mshta.exe. This
   behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe
   or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn
   mshta.exe from svchost.exe.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on process that include the name of the process responsible for the changes from
-  your endpoints into the `Endpoint` datamodel in the `Processes` node.
-id: 4d33a488-5b5f-11eb-ae93-0242ac130002
-known_false_positives: Although unlikely, some legitimate applications may exhibit
-  this behavior, triggering a false positive.
-name: Suspicious mshta spawn
-references:
-- https://codewhitesec.blogspot.com/2018/07/lethalhta.html
-- https://github.com/redcanaryco/AtomicTestHarnesses
-- https://redcanary.com/blog/introducing-atomictestharnesses/
 search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
   as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe
   OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe
   by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
+references:
+- https://codewhitesec.blogspot.com/2018/07/lethalhta.html
+- https://github.com/redcanaryco/AtomicTestHarnesses
+- https://redcanary.com/blog/introducing-atomictestharnesses/
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious MSHTA Activity
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -42,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/suspicious_reg_exe_process.yml b/detections/endpoint/suspicious_reg_exe_process.yml
index 01e43735b5..0ac5e11eae 100644
--- a/detections/endpoint/suspicious_reg_exe_process.yml
+++ b/detections/endpoint/suspicious_reg_exe_process.yml
@@ -1,20 +1,13 @@
-author: David Dorsey, Splunk
+name: Suspicious Reg exe Process
+id: a6b3ab4e-dd77-4213-95fa-fc94701995e0
+version: 4
 date: '2020-07-22'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for reg.exe being launched from a command prompt not
   started by the user. When a user launches cmd.exe, the parent process is usually
   explorer.exe. This search filters out those instances.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: a6b3ab4e-dd77-4213-95fa-fc94701995e0
-known_false_positives: It's possible for system administrators to write scripts that
-  exhibit this behavior. If this is the case, the search will need to be modified
-  to filter them out.
-name: Suspicious Reg exe Process
-references:
-- https://car.mitre.org/wiki/CAR-2013-03-001
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name
   != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name
@@ -25,8 +18,18 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup
   process_id| table process_id dest] | `suspicious_reg_exe_process_filter` '
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: It's possible for system administrators to write scripts that
+  exhibit this behavior. If this is the case, the search will need to be modified
+  to filter them out.
+references:
+- https://car.mitre.org/wiki/CAR-2013-03-001
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Defense Evasion Tactics
   - Disabling Security Tools
   - DHS Report TA18-074A
@@ -47,5 +50,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml b/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml
index 677419eeef..74371474eb 100644
--- a/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml
+++ b/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml
@@ -2,23 +2,14 @@ name: Suspicious Regsvr32 Register Suspicious Path
 id: 62732736-6250-11eb-ae93-0242ac130002
 version: 1
 date: '2021-01-28'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: Adversaries may abuse Regsvr32.exe to proxy execution of malicious code
   by using non-standard file extensions to load malciious DLLs. Upon investigating,
   look for network connections to remote destinations (internal or external). Review
   additional parrallel processes and child processes for additional activity.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints, to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model. Tune the query by filtering additional extensions
-  found to be used by  legitimate processes.
-type: ESCU
-references:
-- https://attack.mitre.org/techniques/T1218/010/
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
-- https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/
-- https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5
-- https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718
-author: Michael Haag, Splunk
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe
   (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*)
@@ -26,22 +17,37 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   Processes.user Processes.parent_process Processes.process_name Processes.process
   Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints, to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model. Tune the query by filtering additional extensions
+  found to be used by  legitimate processes.
 known_false_positives: Limited false positives with the query restricted to specified
   paths. Add more world writeable paths as tuning continues.
+references:
+- https://attack.mitre.org/techniques/T1218/010/
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
+- https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/
+- https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5
+- https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Regsvr32 Activity
-  mitre_attack_id:
-  - T1218.010
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
   - CIS 16
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1218.010
   nist:
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/suspicious_rundll32_dllregisterserver.yml b/detections/endpoint/suspicious_rundll32_dllregisterserver.yml
index 0067f2f862..69fadc40c4 100644
--- a/detections/endpoint/suspicious_rundll32_dllregisterserver.yml
+++ b/detections/endpoint/suspicious_rundll32_dllregisterserver.yml
@@ -2,6 +2,10 @@ name: Suspicious Rundll32 dllregisterserver
 id: 8c00a385-9b86-4ac0-8932-c9ec3713b159
 version: 1
 date: '2021-02-09'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies rundll32.exe using dllregisterserver
   on the command line to load a DLL. When a DLL is registered, the DllRegisterServer
   method entry point in the DLL is invoked. This is typically seen when a DLL is being
@@ -9,10 +13,18 @@ description: The following analytic identifies rundll32.exe using dllregisterser
   capture malicious use of it. During investigation, review the parent process and
   parrellel processes executing. Capture the DLL being loaded and inspect further.
   Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
+  Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_rundll32_dllregisterserver_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node.
-type: ESCU
+known_false_positives: This is likely to produce false positives and will require
+  some filtering. Tune the query by adding command line paths to known good DLLs,
+  or filtering based on parent process names.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -21,30 +33,24 @@ references:
 - https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt
 - https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/
 - https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx
-author: Michael Haag, Splunk
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
-  Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process
-  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
-  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `suspicious_rundll32_dllregisterserver_filter`'
-known_false_positives: This is likely to produce false positives and will require
-  some filtering. Tune the query by adding command line paths to known good DLLs,
-  or filtering based on parent process names.
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Rundll32 Activity
-  mitre_attack_id:
-  - T1218.011
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1218.011
   nist:
   - PR.PT
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/suspicious_rundll32_rename.yml b/detections/endpoint/suspicious_rundll32_rename.yml
index 7e7dddf7c9..2e8b52dbde 100644
--- a/detections/endpoint/suspicious_rundll32_rename.yml
+++ b/detections/endpoint/suspicious_rundll32_rename.yml
@@ -2,44 +2,49 @@ name: Suspicious Rundll32 Rename
 id: 7360137f-abad-473e-8189-acbdaa34d114
 version: 1
 date: '2021-02-04'
+author: Michael Haag, Splunk
+type: batch
+datamodel: []
 description: The following analytic identifies renamed instances of rundll32.exe executing.
   rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During
   investigation, validate it is the legitimate rundll32.exe executing and what script
   content it is loading. This query relies on the OriginalFileName from Sysmon, or
   internal name from the PE meta data. Expand the query as needed by looking for specific
   command line arguments outlined in other analytics.
-how_to_implement: To successfully implement this search, you need to be ingesting
-  logs with the process name, parent process, and command-line executions from your
-  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
-  Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
-type: ESCU
-references:
-- https://attack.mitre.org/techniques/T1218/011/
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
-- https://lolbas-project.github.io/lolbas/Binaries/Rundll32
-author: Michael Haag, Splunk
 search: '`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe
   | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User,
   parent_process_name, process_name, OriginalFileName, process_path, CommandLine |
   rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
   `suspicious_rundll32_rename_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
 known_false_positives: Although unlikely, some legitimate applications may use a moved
   copy of rundll32, triggering a false positive.
+references:
+- https://attack.mitre.org/techniques/T1218/011/
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
+- https://lolbas-project.github.io/lolbas/Binaries/Rundll32
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Rundll32 Activity
+  asset_type: Endpoint
+  automated_detection_testing: passed
+  cis20:
+  - CIS 8
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
   mitre_attack_id:
   - T1218.011
   - T1036.003
-  kill_chain_phases:
-  - Actions on Objectives
-  cis20:
-  - CIS 8
   nist:
   - PR.PT
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/suspicious_rundll32_startw.yml b/detections/endpoint/suspicious_rundll32_startw.yml
index da2b73b2cd..8c28c112ef 100644
--- a/detections/endpoint/suspicious_rundll32_startw.yml
+++ b/detections/endpoint/suspicious_rundll32_startw.yml
@@ -2,6 +2,10 @@ name: Suspicious Rundll32 StartW
 id: 9319dda5-73f2-4d43-a85a-67ce961bddb7
 version: 1
 date: '2021-02-04'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The following analytic identifies rundll32.exe executing a DLL function
   name, Start and StartW, on the command line that is commonly observed with CobaltStrike
   x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32
@@ -11,10 +15,17 @@ description: The following analytic identifies rundll32.exe executing a DLL func
   execution. Capture and triage the DLL in question. In the instance of CobaltStrike,
   rundll32.exe is the default process it opens and injects shellcode into. This default
   process can be changed, but typically is not.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
+  Processes.process=*start* by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_rundll32_startw_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node.
-type: ESCU
+known_false_positives: Although unlikely, some legitimate applications may use Start
+  as a function and call it via the command line. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -22,29 +33,24 @@ references:
 - https://lolbas-project.github.io/lolbas/Binaries/Rundll32
 - https://lolbas-project.github.io/lolbas/Libraries/Advpack/
 - https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
-author: Michael Haag, Splunk
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
-  Processes.process=*start* by Processes.dest Processes.user Processes.parent_process
-  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
-  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `suspicious_rundll32_startw_filter`'
-known_false_positives: Although unlikely, some legitimate applications may use Start
-  as a function and call it via the command line. Filter as needed.
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Rundll32 Activity
-  mitre_attack_id:
-  - T1218.011
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1218.011
   nist:
   - PR.PT
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/suspicious_rundll32_with_no_command_line_arguments.yml b/detections/endpoint/suspicious_rundll32_with_no_command_line_arguments.yml
index d9e5f073b7..ac70a88905 100644
--- a/detections/endpoint/suspicious_rundll32_with_no_command_line_arguments.yml
+++ b/detections/endpoint/suspicious_rundll32_with_no_command_line_arguments.yml
@@ -2,45 +2,50 @@ name: Suspicious Rundll32 no CommandLine Arguments
 id: e451bd16-e4c5-4109-8eb1-c4c6ecf048b4
 version: 1
 date: '2021-02-09'
+author: Michael Haag, Splunk
+type: batch
+datamodel: []
 description: The following analytic identifies rundll32.exe with no command line arguments.
   It is unusual for rundll32.exe to execute with no command line arguments present.
   This particular behavior is common with malicious software, including CobaltStrike.
   During investigation, identify any network connections and parallel processes. Identify
   any suspicious module loads related to credential dumping or file writes. Rundll32.exe
   is natively found in C:\Windows\system32 and C:\Windows\syswow64.
+search: '`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE)
+  | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime
+  max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name,
+  OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `suspicious_rundll32_no_commandline_arguments_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the process name, parent process, and command-line executions from your
   endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
   Sysmon TA.
-type: ESCU
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of rundll32, triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
 - https://lolbas-project.github.io/lolbas/Binaries/Rundll32
 - https://lolbas-project.github.io/lolbas/Libraries/Advpack/
 - https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
-author: Michael Haag, Splunk
-search: '`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE)
-  | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime
-  max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name,
-  OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `suspicious_rundll32_no_commandline_arguments_filter`'
-known_false_positives: Although unlikely, some legitimate applications may use a moved
-  copy of rundll32, triggering a false positive.
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Rundll32 Activity
-  mitre_attack_id:
-  - T1218.011
-  kill_chain_phases:
-  - Actions on Objectives
+  asset_type: Endpoint
+  automated_detection_testing: passed
   cis20:
   - CIS 8
+  dataset:
+  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  kill_chain_phases:
+  - Actions on Objectives
+  mitre_attack_id:
+  - T1218.011
   nist:
   - PR.PT
   - DE.CM
   security_domain: endpoint
-  asset_type: Endpoint
-  dataset:
-  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
-  automated_detection_testing: passed
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/detections/endpoint/suspicious_wevtutil_usage.yml b/detections/endpoint/suspicious_wevtutil_usage.yml
index fe583f0acb..d38a7b7e54 100644
--- a/detections/endpoint/suspicious_wevtutil_usage.yml
+++ b/detections/endpoint/suspicious_wevtutil_usage.yml
@@ -1,18 +1,14 @@
-author: David Dorsey, Splunk
+name: Suspicious wevtutil Usage
+id: 2827c0fd-e1be-4868-ae25-59d28e0f9d4f
+version: 3
 date: '2020-07-22'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The wevtutil.exe application is the windows event log utility. This searches
   for wevtutil.exe with parameters for clearing the application, security, setup,
   or system event logs.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: 2827c0fd-e1be-4868-ae25-59d28e0f9d4f
-known_false_positives: The wevtutil.exe application is a legitimate Windows event
-  log utility. Administrators may use it to manage Windows event logs.
-name: Suspicious wevtutil Usage
-references: []
 search: '| tstats `security_content_summariesonly` values(Processes.process) as process
   min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
   where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*"
@@ -20,8 +16,16 @@ search: '| tstats `security_content_summariesonly` values(Processes.process) as
   by Processes.process_name Processes.parent_process_name Processes.dest Processes.user|
   `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`
   | `suspicious_wevtutil_usage_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: The wevtutil.exe application is a legitimate Windows event
+  log utility. Administrators may use it to manage Windows event logs.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Log Manipulation
   - Ransomware
   asset_type: ''
@@ -48,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml b/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml
index 09364ccb02..6336a4a2c5 100644
--- a/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml
+++ b/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml
@@ -1,16 +1,12 @@
-author: Rico Valdez, Splunk
+name: Suspicious writes to windows Recycle Bin
+id: b5541828-8ffd-4070-9d95-b3da4de924cb
+version: 4
 date: '2020-07-22'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search detects writes to the recycle bin by a process other than
   explorer.exe.
-how_to_implement: To successfully implement this search you need to be ingesting information
-  on filesystem and process logs responsible for the changes from your endpoints into
-  the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes.
-id: b5541828-8ffd-4070-9d95-b3da4de924cb
-known_false_positives: Because the Recycle Bin is a hidden folder in modern versions
-  of Windows, it would be unusual for a process other than explorer.exe to write to
-  it. Incidents should be investigated as appropriate.
-name: Suspicious writes to windows Recycle Bin
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name)
   as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*"
@@ -20,8 +16,15 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name
   != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")`
   | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on filesystem and process logs responsible for the changes from your endpoints into
+  the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes.
+known_false_positives: Because the Recycle Bin is a hidden folder in modern versions
+  of Windows, it would be unusual for a process other than explorer.exe to write to
+  it. Incidents should be investigated as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Collection and Staging
   asset_type: Windows
   automated_detection_testing: passed
@@ -38,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/system_information_discovery_detection.yml b/detections/endpoint/system_information_discovery_detection.yml
index cbf641bb8f..7ccc22413b 100644
--- a/detections/endpoint/system_information_discovery_detection.yml
+++ b/detections/endpoint/system_information_discovery_detection.yml
@@ -1,12 +1,13 @@
-author: Patrick Bareiss, Splunk
+name: System Information Discovery Detection
+id: 8e99f89e-ae58-4ebc-bf52-ae0b1a277e72
+version: 1
 date: '2020-10-12'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: Detect system information discovery techniques used by attackers to understand
   configurations of the system to further exploit it.
-id: 8e99f89e-ae58-4ebc-bf52-ae0b1a277e72
-known_false_positives: Administrators debugging servers
-name: System Information Discovery Detection
-references:
-- https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*"
   OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user
@@ -15,8 +16,14 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   > 2 | stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime
   by user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `system_information_discovery_detection_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node.
+known_false_positives: Administrators debugging servers
+references:
+- https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation
 tags:
-  analytics_story:
+  analytic_story:
   - Discovery Techniques
   asset_type: Windows
   automated_detection_testing: passed
@@ -36,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/system_processes_run_from_unexpected_locations.yml b/detections/endpoint/system_processes_run_from_unexpected_locations.yml
index f2700e63e1..7d96db24b9 100644
--- a/detections/endpoint/system_processes_run_from_unexpected_locations.yml
+++ b/detections/endpoint/system_processes_run_from_unexpected_locations.yml
@@ -1,23 +1,26 @@
-author: David Dorsey, Splunk
+name: System Processes Run From Unexpected Locations
+id: a34aae96-ccf8-4aef-952c-3ea21444444d
+version: 5
 date: '2020-12-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for system processes that normally run out of C:\Windows\System32\
   or C:\Windows\SysWOW64 that are not run from that location.  This can indicate a
   malicious process that is trying to hide as a legitimate process.
-how_to_implement: To successfully implement this search you need to ingest details
-  about process execution from your hosts. Specifically, this search requires the
-  process name and the full path to the process executable.
-id: a34aae96-ccf8-4aef-952c-3ea21444444d
-known_false_positives: None identified
-name: System Processes Run From Unexpected Locations
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*"
   Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest
   Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name
   Processes.process_hash| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|
   `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter`'
+how_to_implement: To successfully implement this search you need to ingest details
+  about process execution from your hosts. Specifically, this search requires the
+  process name and the full path to the process executable.
+known_false_positives: None identified
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Command-Line Executions
   - Unusual Processes
   - Ransomware
@@ -39,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/unload_sysmon_filter_driver.yml b/detections/endpoint/unload_sysmon_filter_driver.yml
index ef8be4ae5f..051223669f 100644
--- a/detections/endpoint/unload_sysmon_filter_driver.yml
+++ b/detections/endpoint/unload_sysmon_filter_driver.yml
@@ -1,18 +1,14 @@
-author: Bhavin Patel, Splunk
+name: Unload Sysmon Filter Driver
+id: c77162d3-f93c-45cc-80c8-22f665664g9f
+version: 3
 date: '2020-07-22'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: Attackers often disable security tools to avoid detection. This search
   looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop
   sysmon from collecting the data.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro,
-  update this macro to filter out false positives.
-id: c77162d3-f93c-45cc-80c8-22f665664g9f
-known_false_positives: ''
-name: Unload Sysmon Filter Driver
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process)
   as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe
   AND Processes.process=*unload* AND Processes.process=*SysmonDrv*  by Processes.process_name
@@ -20,8 +16,16 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
   |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count
   process_name process_id parent_process_name process'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro,
+  update this macro to filter out false positives.
+known_false_positives: ''
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Disabling Security Tools
   asset_type: ''
   automated_detection_testing: passed
@@ -40,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/endpoint/unusually_long_command_line.yml b/detections/endpoint/unusually_long_command_line.yml
index 2aced7153b..f7d114d917 100644
--- a/detections/endpoint/unusually_long_command_line.yml
+++ b/detections/endpoint/unusually_long_command_line.yml
@@ -1,15 +1,12 @@
-author: David Dorsey, Splunk
+name: Unusually Long Command Line
+id: c77162d3-f93c-45cc-80c8-22f6a4264e7f
+version: 5
 date: '2020-12-08'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: Command lines that are extremely long may be indicative of malicious
   activity on your hosts.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships, from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the process
-  field in the Endpoint data model.
-id: c77162d3-f93c-45cc-80c8-22f6a4264e7f
-known_false_positives: Some legitimate applications start with long command lines.
-name: Unusually Long Command Line
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name
   Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|
@@ -18,8 +15,14 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process
   | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost)
   + avgperhost)'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships, from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the process
+  field in the Endpoint data model.
+known_false_positives: Some legitimate applications start with long command lines.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Command-Line Executions
   - Unusual Processes
   - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
@@ -40,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/endpoint/unusually_long_command_line___mltk.yml b/detections/endpoint/unusually_long_command_line___mltk.yml
index cf6cf89024..458754720a 100644
--- a/detections/endpoint/unusually_long_command_line___mltk.yml
+++ b/detections/endpoint/unusually_long_command_line___mltk.yml
@@ -1,8 +1,20 @@
-author: Rico Valdez, Splunk
+name: Unusually Long Command Line - MLTK
+id: 57edaefa-a73b-45e5-bbae-f39c1473f941
+version: 1
 date: '2019-05-08'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: Command lines that are extremely long may be indicative of malicious
   activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK)
   to help identify command lines with lengths that are unusual for a given user.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name
+  Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown
+  | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier
+  | search isOutlier > 0 | table firstTime lastTime user dest process_name process
+  processlen count | `unusually_long_command_line___mltk_filter`'
 how_to_implement: You must be ingesting endpoint data that monitors command lines
   and populates the Endpoint data model in the Processes node. The command-line arguments
   are mapped to the "process" field in the Endpoint data model. In addition, MLTK
@@ -13,7 +25,6 @@ how_to_implement: You must be ingesting endpoint data that monitors command line
   as the associated support search, so that the model created by the support search
   is available for use. You should periodically re-run the support search to rebuild
   the model with the latest data available in your environment.
-id: 57edaefa-a73b-45e5-bbae-f39c1473f941
 known_false_positives: Some legitimate applications use long command lines for installs
   or updates. You should review identified command lines for legitimacy. You may modify
   the first part of the search to omit legitimate command lines from consideration.
@@ -22,17 +33,9 @@ known_false_positives: Some legitimate applications use long command lines for i
   the support search to re-build the ML model on the latest data. You may get unexpected
   results if the user identified in the results is not present in the data used to
   build the associated model.
-name: Unusually Long Command Line - MLTK
 references: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name
-  Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
-  `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown
-  | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier
-  | search isOutlier > 0 | table firstTime lastTime user dest process_name process
-  processlen count | `unusually_long_command_line___mltk_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Command-Line Executions
   - Unusual Processes
   - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
@@ -50,5 +53,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/usn_journal_deletion.yml b/detections/endpoint/usn_journal_deletion.yml
index 3cd12aa0b1..b0a5d88e99 100644
--- a/detections/endpoint/usn_journal_deletion.yml
+++ b/detections/endpoint/usn_journal_deletion.yml
@@ -1,26 +1,30 @@
-author: David Dorsey, Splunk
+name: USN Journal Deletion
+id: b6e0ff70-b122-4227-9368-4cf322ab43c3
+version: 2
 date: '2018-12-03'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: The fsutil.exe application is a legitimate Windows utility used to perform
   tasks related to the file allocation table (FAT) and NTFS file systems. The update
   sequence number (USN) change journal provides a log of all changes made to the files
   on the disk. This search looks for fsutil.exe deleting the USN journal.
-how_to_implement: You must be ingesting data that records process activity from your
-  hosts to populate the Endpoint data model in the Processes node. You must also be
-  ingesting logs with both the process name and command line from your endpoints.
-  The command-line arguments are mapped to the "process" field in the Endpoint data
-  model.
-id: b6e0ff70-b122-4227-9368-4cf322ab43c3
-known_false_positives: None identified
-name: USN Journal Deletion
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process)
   as process values(Processes.parent_process) as parent_process min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe
   by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  |
   `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter`'
+how_to_implement: You must be ingesting data that records process activity from your
+  hosts to populate the Endpoint data model in the Processes node. You must also be
+  ingesting logs with both the process name and command line from your endpoints.
+  The command-line arguments are mapped to the "process" field in the Endpoint data
+  model.
+known_false_positives: None identified
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Log Manipulation
   - Ransomware
   asset_type: Endpoint
@@ -46,5 +50,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/endpoint/wbadmin_delete_system_backups.yml b/detections/endpoint/wbadmin_delete_system_backups.yml
index e62ea54246..5c47434d89 100644
--- a/detections/endpoint/wbadmin_delete_system_backups.yml
+++ b/detections/endpoint/wbadmin_delete_system_backups.yml
@@ -1,27 +1,31 @@
-author: Michael Haag, Splunk
+name: WBAdmin Delete System Backups
+id: cd5aed7e-5cea-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-22'
+author: Michael Haag, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for flags passed to wbadmin.exe (Windows Backup Administrator
   Tool) that delete backup files. This is typically used by ransomware to prevent
   recovery.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. Tune based on parent process names.
-id: cd5aed7e-5cea-11eb-ae93-0242ac130002
-known_false_positives: Administrators may modify the boot configuration.
-name: WBAdmin Delete System Backups
-references:
-- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md
-- https://thedfirreport.com/2020/10/08/ryuks-return/
-- https://attack.mitre.org/techniques/T1490/
-- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe
   Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*")
   by Processes.process_name Processes.process Processes.parent_process_name Processes.dest
   Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. Tune based on parent process names.
+known_false_positives: Administrators may modify the boot configuration.
+references:
+- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md
+- https://thedfirreport.com/2020/10/08/ryuks-return/
+- https://attack.mitre.org/techniques/T1490/
+- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin
 tags:
-  analytics_story:
+  analytic_story:
   - Ryuk Ransomware
   - Ransomware
   asset_type: Endpoint
@@ -41,5 +45,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/windows_adfind_exe.yml b/detections/endpoint/windows_adfind_exe.yml
index 78f278fcd4..d83a5319bc 100644
--- a/detections/endpoint/windows_adfind_exe.yml
+++ b/detections/endpoint/windows_adfind_exe.yml
@@ -1,21 +1,17 @@
-author: Jose Hernandez, Splunk
+name: Windows AdFind Exe
+id: bd3b0187-189b-46c0-be45-f52da2bae67f
+version: 1
 date: '2020-12-16'
+author: Jose Hernandez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: 'This search looks for the execution of `adfind.exe` with command-line
   arguments that it uses by default. Specifically the filter or search functions.
   It also considers the arguments necessary like objectcategory, see readme for more
   details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been
   seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST.
   AdFind.exe is usually used a recon tool to enumare a domain controller.'
-how_to_implement: To successfully implement this search, you need to be ingesting
-  logs with the process name, and command-line executions from your endpoints. If
-  you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-id: bd3b0187-189b-46c0-be45-f52da2bae67f
-known_false_positives: administrators rarely use adfind, usually not used for legitimate
-  reasons
-name: Windows AdFind Exe
-references:
-- https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/
-- https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html
 search: ' | tstats `security_content_summariesonly` count min(_time) as firstTime
   max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f*
   OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb*
@@ -23,8 +19,16 @@ search: ' | tstats `security_content_summariesonly` count min(_time) as firstTim
   Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id
   | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `windows_adfind_exe_filter` '
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, and command-line executions from your endpoints. If
+  you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: administrators rarely use adfind, usually not used for legitimate
+  reasons
+references:
+- https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/
+- https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html
 tags:
-  analytics_story:
+  analytic_story:
   - Sunburst Malware
   asset_type: Endpoint
   cis20:
@@ -43,5 +47,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/windows_event_log_cleared.yml b/detections/endpoint/windows_event_log_cleared.yml
index 4bfff3e8a9..4551e47785 100644
--- a/detections/endpoint/windows_event_log_cleared.yml
+++ b/detections/endpoint/windows_event_log_cleared.yml
@@ -1,20 +1,23 @@
-author: Rico Valdez, Splunk
+name: Windows Event Log Cleared
+id: ad517544-aff9-4c96-bd99-d6eb43bfbb6a
+version: 4
 date: '2020-07-06'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search looks for Windows events that indicate one of the Windows
   event logs has been purged.
-how_to_implement: To successfully implement this search, you need to be ingesting
-  Windows event logs from your hosts.
-id: ad517544-aff9-4c96-bd99-d6eb43bfbb6a
-known_false_positives: It is possible that these logs may be legitimately cleared
-  by Administrators.
-name: Windows Event Log Cleared
-references: []
 search: (`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system`
   EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode
   dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `windows_event_log_cleared_filter`
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows event logs from your hosts.
+known_false_positives: It is possible that these logs may be legitimately cleared
+  by Administrators.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Log Manipulation
   - Ransomware
   asset_type: Endpoint
@@ -41,5 +44,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/endpoint/windows_security_account_manager_stopped.yml b/detections/endpoint/windows_security_account_manager_stopped.yml
index cf3d07c4d0..b0e0e01abf 100644
--- a/detections/endpoint/windows_security_account_manager_stopped.yml
+++ b/detections/endpoint/windows_security_account_manager_stopped.yml
@@ -1,24 +1,27 @@
-author: Rod Soto, Jose Hernandez, Splunk
+name: Windows Security Account Manager Stopped
+id: 69c12d59-d951-431e-ab77-ec426b8d65e6
+version: 1
 date: '2020-11-06'
+author: Rod Soto, Jose Hernandez, Splunk
+type: batch
+datamodel: []
 description: The search looks for a Windows Security Account Manager (SAM) was stopped
   via command-line. This is consistent with Ryuk infections across a fleet of endpoints.
-how_to_implement: You must be ingesting data that records the process-system activity
-  from your hosts to populate the Endpoint Processes data-model object. If you are
-  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
-  you want to collect data.
-id: 69c12d59-d951-431e-ab77-ec426b8d65e6
-known_false_positives: SAM is a critical windows service, stopping it would cause
-  major issues on an endpoint this makes false positive rare. AlthoughNo false positives
-  have been identified.
-name: Windows Security Account Manager Stopped
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe"
   "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process"
   | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   | `windows_security_account_manager_stopped_filter`'
+how_to_implement: You must be ingesting data that records the process-system activity
+  from your hosts to populate the Endpoint Processes data-model object. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: SAM is a critical windows service, stopping it would cause
+  major issues on an endpoint this makes false positive rare. AlthoughNo false positives
+  have been identified.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Ryuk Ransomware
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -38,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml b/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml
index 9ccca9c836..bdf3982186 100644
--- a/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml
+++ b/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml
@@ -1,19 +1,22 @@
-author: Rico Valdez, Splunk
+name: WMI Permanent Event Subscription - Sysmon
+id: ad05aae6-3b2a-4f73-af97-57bd26cee3b9
+version: 2
 date: '2020-12-08'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
 description: This search looks for the creation of WMI permanent event subscriptions.
+search: '`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation,
+  EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter`'
 how_to_implement: To successfully implement this search, you must be collecting Sysmon
   data using Sysmon version 6.1 or greater and have Sysmon configured to generate
   alerts for WMI activity. In addition, you must have at least version 6.0.4 of the
   Sysmon TA installed to properly parse the fields.
-id: ad05aae6-3b2a-4f73-af97-57bd26cee3b9
 known_false_positives: Although unlikely, administrators may use event subscriptions
   for legitimate purposes.
-name: WMI Permanent Event Subscription - Sysmon
 references: []
-search: '`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation,
-  EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious WMI Use
   asset_type: Endpoint
   automated_detection_testing: passed
@@ -36,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 2
diff --git a/detections/experimental/application/detect_new_login_attempts_to_routers.yml b/detections/experimental/application/detect_new_login_attempts_to_routers.yml
index de15770e85..dcb8aa1b6b 100644
--- a/detections/experimental/application/detect_new_login_attempts_to_routers.yml
+++ b/detections/experimental/application/detect_new_login_attempts_to_routers.yml
@@ -1,23 +1,27 @@
-author: Bhavin Patel, Splunk
+name: Detect New Login Attempts to Routers
+id: 104658f4-afdc-499e-9719-17243rr826f1
+version: 1
 date: '2017-09-12'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Authentication
 description: The search queries the authentication logs for assets that are categorized
   as routers in the ES Assets and Identity Framework, to identify connections that
   have not been seen before in the last 30 days.
-how_to_implement: To successfully implement this search, you must ensure the network
-  router devices are categorized as "router" in the Assets and identity table. You
-  must also populate the Authentication data model with logs related to users authenticating
-  to routing infrastructure.
-id: 104658f4-afdc-499e-9719-17243rr826f1
-known_false_positives: Legitimate router connections may appear as new connections
-name: Detect New Login Attempts to Routers
-references: []
 search: '| tstats `security_content_summariesonly` count earliest(_time) as earliest
   latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router
   by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(),
   "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)`
   | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter`'
+how_to_implement: To successfully implement this search, you must ensure the network
+  router devices are categorized as "router" in the Assets and identity table. You
+  must also populate the Authentication data model with logs related to users authenticating
+  to routing infrastructure.
+known_false_positives: Legitimate router connections may appear as new connections
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Router and Infrastructure Security
   asset_type: Endpoint
   cis20:
@@ -33,5 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/experimental/application/detect_phishing_content___ssa.yml b/detections/experimental/application/detect_phishing_content___ssa.yml
index 1283f7a703..9fc137905b 100644
--- a/detections/experimental/application/detect_phishing_content___ssa.yml
+++ b/detections/experimental/application/detect_phishing_content___ssa.yml
@@ -1,5 +1,10 @@
-author: Xiao Lin, Splunk
+name: Phishing Email Detection by Machine Learning Method - SSA
+id: 4b237388-dfa1-41a6-91d4-4de2d598376f
+version: 1
 date: '2020-08-25'
+author: Xiao Lin, Splunk
+type: streaming
+datamodel: []
 description: Malicious mails can conduct phishing that induces readers to open attachment,
   click links or trigger third party service. This detect uses Natural Language Processing
   (NLP) approach to analyze an email message's content (Sender, Subject and Body)
@@ -13,14 +18,6 @@ description: Malicious mails can conduct phishing that induces readers to open a
   which returns the probability of a phishing email. Current implementation assumes
   the email is fed to DSP in JSON format contains at least email's sender, subject
   and its message body, including reply content, if any.
-how_to_implement: Events are fed to DSP contains at least email's sender, subject
-  and its message body.
-id: 4b237388-dfa1-41a6-91d4-4de2d598376f
-known_false_positives: Because of imbalance of anomaly data in training, the model
-  will less likely report false positive. Instead, the model is more prone to false
-  negative. Current best recall score is ~85%
-name: Phishing Email Detection by Machine Learning Method - SSA
-references: []
 search: '| from read_ssa_enriched_events() | eval eventLine=concat(ucast(map_get(input_event,
   "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", "
   "), " ", ucast(map_get(input_event, "Content"), "string", " "), "                                                                                                                                "),
@@ -41,6 +38,12 @@ search: '| from read_ssa_enriched_events() | eval eventLine=concat(ucast(map_get
   path="s3://smle-experiments/models/phishing_email" | eval probability = mvindex(ml_out,
   0) | where probability > 0.5 | eval start_time=_time, end_time=_time, entities="TBD",
   body="TBD" | select probability, body, entities, start_time, end_time | into write_ssa_detected_events();'
+how_to_implement: Events are fed to DSP contains at least email's sender, subject
+  and its message body.
+known_false_positives: Because of imbalance of anomaly data in training, the model
+  will less likely report false positive. Instead, the model is more prone to false
+  negative. Current best recall score is ~85%
+references: []
 tags:
   cis20:
   - CIS 8
@@ -55,5 +58,3 @@ tags:
   - UEBA for Security Cloud
   risk_severity: low
   security_domain: mail server
-type: SSA
-version: 1
diff --git a/detections/experimental/application/email_attachments_with_lots_of_spaces.yml b/detections/experimental/application/email_attachments_with_lots_of_spaces.yml
index da2006139d..b13ccb80ee 100644
--- a/detections/experimental/application/email_attachments_with_lots_of_spaces.yml
+++ b/detections/experimental/application/email_attachments_with_lots_of_spaces.yml
@@ -1,8 +1,20 @@
-author: David Dorsey, Splunk
+name: Email Attachments With Lots Of Spaces
+id: 56e877a6-1455-4479-ada6-0550dc1e22f8
+version: 2
 date: '2017-09-19'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Email
 description: Attackers often use spaces as a means to obfuscate an attachment's file
   extension. This search looks for messages with email attachments that have many
   spaces within the file names.
+search: '| tstats `security_content_summariesonly` count values(All_Email.recipient)
+  as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email
+  where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")`
+  | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio
+  >= 0.1 |  rex field=recipient_address "(?<recipient_user>.*)@" | `email_attachments_with_lots_of_spaces_filter`'
 how_to_implement: "You need to ingest data from emails. Specifically, the sender's\
   \ address and the file names of any attachments must be mapped to the Email data\
   \ model. The threshold ratio is set to 10%, but this value can be configured to\
@@ -16,17 +28,10 @@ how_to_implement: "You need to ingest data from emails. Specifically, the sender
   \ will gather further information about the file attachment and its network behaviors.\
   \ If Phantom finds malicious behavior and an analyst approves of the results, the\
   \ email will be deleted from the user's inbox."
-id: 56e877a6-1455-4479-ada6-0550dc1e22f8
 known_false_positives: None at this time
-name: Email Attachments With Lots Of Spaces
-search: '| tstats `security_content_summariesonly` count values(All_Email.recipient)
-  as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email
-  where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")`
-  | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio
-  >= 0.1 |  rex field=recipient_address "(?<recipient_user>.*)@" | `email_attachments_with_lots_of_spaces_filter`'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Suspicious Emails
   asset_type: Endpoint
@@ -41,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/application/email_servers_sending_high_volume_traffic_to_hosts.yml b/detections/experimental/application/email_servers_sending_high_volume_traffic_to_hosts.yml
index e2a7e1fc61..e829ed6c99 100644
--- a/detections/experimental/application/email_servers_sending_high_volume_traffic_to_hosts.yml
+++ b/detections/experimental/application/email_servers_sending_high_volume_traffic_to_hosts.yml
@@ -1,21 +1,14 @@
-author: Bhavin Patel, Splunk
+name: Email servers sending high volume traffic to hosts
+id: 7f5fb3e1-4209-4914-90db-0ec21b556378
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for an increase of data transfers from your email server
   to your clients. This could be indicative of a malicious actor collecting data using
   your email server.
-how_to_implement: This search requires you to be ingesting your network traffic and
-  populating the Network_Traffic data model.  Your email servers must be categorized
-  as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold
-  and minimum_data_samples values based on the network traffic in your environment.
-  The "deviation_threshold" field is a multiplying factor to control how much variation
-  you're willing to tolerate. The "minimum_data_samples" field is the minimum number
-  of connections of data samples required for the statistic to be valid.
-id: 7f5fb3e1-4209-4914-90db-0ec21b556378
-known_false_positives: The false-positive rate will vary based on how you set the
-  deviation_threshold and data_samples values. Our recommendation is to adjust these
-  values based on your network traffic to and from your email servers.
-name: Email servers sending high volume traffic to hosts
-references: []
 search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out
   from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip
   _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out)
@@ -30,8 +23,19 @@ search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as
   = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out,
   2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average,
   num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter`'
+how_to_implement: This search requires you to be ingesting your network traffic and
+  populating the Network_Traffic data model.  Your email servers must be categorized
+  as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold
+  and minimum_data_samples values based on the network traffic in your environment.
+  The "deviation_threshold" field is a multiplying factor to control how much variation
+  you're willing to tolerate. The "minimum_data_samples" field is the minimum number
+  of connections of data samples required for the statistic to be valid.
+known_false_positives: The false-positive rate will vary based on how you set the
+  deviation_threshold and data_samples values. Our recommendation is to adjust these
+  values based on your network traffic to and from your email servers.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Collection and Staging
   asset_type: Endpoint
   cis20:
@@ -49,5 +53,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/application/monitor_email_for_brand_abuse.yml b/detections/experimental/application/monitor_email_for_brand_abuse.yml
index 30c2a56add..b90a8c2d05 100644
--- a/detections/experimental/application/monitor_email_for_brand_abuse.yml
+++ b/detections/experimental/application/monitor_email_for_brand_abuse.yml
@@ -1,15 +1,13 @@
-author: David Dorsey, Splunk
+name: Monitor Email For Brand Abuse
+id: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8
+version: 2
 date: '2018-01-05'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Email
 description: This search looks for emails claiming to be sent from a domain similar
   to one that you want to have monitored for abuse.
-how_to_implement: You need to ingest email header data. Specifically the sender's
-  address (src_user) must be populated.  You also need to have run the search "ESCU
-  - DNSTwist Domain Names", which creates the permutations of the domain that will
-  be checked for.
-id: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8
-known_false_positives: None at this time
-name: Monitor Email For Brand Abuse
-references: []
 search: '| tstats `security_content_summariesonly` values(All_Email.recipient) as
   recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email
   by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")`
@@ -17,8 +15,14 @@ search: '| tstats `security_content_summariesonly` values(All_Email.recipient) a
   temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true
   brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true
   | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter`'
+how_to_implement: You need to ingest email header data. Specifically the sender's
+  address (src_user) must be populated.  You also need to have run the search "ESCU
+  - DNSTwist Domain Names", which creates the permutations of the domain that will
+  be checked for.
+known_false_positives: None at this time
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Brand Monitoring
   - Suspicious Emails
   asset_type: Endpoint
@@ -33,5 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/application/no_windows_updates_in_a_time_frame.yml b/detections/experimental/application/no_windows_updates_in_a_time_frame.yml
index d567238c47..260866e37a 100644
--- a/detections/experimental/application/no_windows_updates_in_a_time_frame.yml
+++ b/detections/experimental/application/no_windows_updates_in_a_time_frame.yml
@@ -1,20 +1,16 @@
-author: Bhavin Patel, Splunk
+name: No Windows Updates in a time frame
+id: 1a77c08c-2f56-409c-a2d3-7d64617edd4f
+version: 1
 date: '2017-09-15'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Updates
 description: This search looks for Windows endpoints that have not generated an event
   indicating a successful Windows update in the last 60 days. Windows updates are
   typically released monthly and applied shortly thereafter. An endpoint that has
   not successfully applied an update in this time frame indicates the endpoint is
   not regularly being patched for some reason.
-how_to_implement: To successfully implement this search, it requires that the 'Update'
-  data model is being populated. This can be accomplished by ingesting Windows events
-  or the Windows Update log via a universal forwarder on the Windows endpoints you
-  wish to monitor. The Windows add-on should be also be installed and configured to
-  properly parse Windows events in Splunk. There may be other data sources which can
-  populate this data model, including vulnerability management systems.
-id: 1a77c08c-2f56-409c-a2d3-7d64617edd4f
-known_false_positives: None identified
-name: No Windows Updates in a time frame
-references: []
 search: '| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates
   where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest
   Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status
@@ -22,8 +18,16 @@ search: '| tstats `security_content_summariesonly` max(_time) as lastTime from d
   <= relative_time(now(), "-60d@d"), 1, 0)  | `security_content_ctime(lastTime)`  |
   search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update
   Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter`'
+how_to_implement: To successfully implement this search, it requires that the 'Update'
+  data model is being populated. This can be accomplished by ingesting Windows events
+  or the Windows Update log via a universal forwarder on the Windows endpoints you
+  wish to monitor. The Windows add-on should be also be installed and configured to
+  properly parse Windows events in Splunk. There may be other data sources which can
+  populate this data model, including vulnerability management systems.
+known_false_positives: None identified
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Monitor for Updates
   asset_type: Endpoint
   cis20:
@@ -36,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/experimental/application/spectre_and_meltdown_vulnerable_systems.yml b/detections/experimental/application/spectre_and_meltdown_vulnerable_systems.yml
index bd36ed2199..e68543b314 100644
--- a/detections/experimental/application/spectre_and_meltdown_vulnerable_systems.yml
+++ b/detections/experimental/application/spectre_and_meltdown_vulnerable_systems.yml
@@ -1,21 +1,25 @@
-author: David Dorsey, Splunk
+name: Spectre and Meltdown Vulnerable Systems
+id: 354be8e0-32cd-4da0-8c47-796de13b60ea
+version: 1
 date: '2017-01-07'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Vulnerabilities
 description: The search is used to detect systems that are still vulnerable to the
   Spectre and Meltdown vulnerabilities.
-how_to_implement: The search requires that you are ingesting your vulnerability-scanner
-  data and that it reports the CVE of the vulnerability identified.
-id: 354be8e0-32cd-4da0-8c47-796de13b60ea
-known_false_positives: It is possible that your vulnerability scanner is not detecting
-  that the patches have been applied.
-name: Spectre and Meltdown Vulnerable Systems
-references: []
 search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
   as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753"
   OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754"
   by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter`'
+how_to_implement: The search requires that you are ingesting your vulnerability-scanner
+  data and that it reports the CVE of the vulnerability identified.
+known_false_positives: It is possible that your vulnerability scanner is not detecting
+  that the patches have been applied.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Spectre And Meltdown Vulnerabilities
   asset_type: Endpoint
   cis20:
@@ -30,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/experimental/application/suspicious_email___uba_anomaly.yml b/detections/experimental/application/suspicious_email___uba_anomaly.yml
index 485b09994a..55ce9bdb67 100644
--- a/detections/experimental/application/suspicious_email___uba_anomaly.yml
+++ b/detections/experimental/application/suspicious_email___uba_anomaly.yml
@@ -1,17 +1,14 @@
-author: Bhavin Patel, Splunk
+name: Suspicious Email - UBA Anomaly
+id: 56e877a6-1455-4479-ad16-0550dc1e33f8
+version: 3
 date: '2020-07-22'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- UEBA
 description: This detection looks for emails that are suspicious because of their
   sender, domain rareness, or behavior differences. This is an anomaly generated by
   Splunk User Behavior Analytics (UBA).
-how_to_implement: You must be ingesting data from email logs and have Splunk integrated
-  with UBA. This anomaly is raised by a UBA detection model called  "SuspiciousEmailDetectionModel."
-  Ensure that this model is enabled on your UBA instance.
-id: 56e877a6-1455-4479-ad16-0550dc1e33f8
-known_false_positives: This detection model will alert on any sender domain that is
-  seen for the first time. This could be a potential false positive. The next step
-  is to investigate and add the URL to an allow list if you determine that it is a
-  legitimate sender.
-name: Suspicious Email - UBA Anomaly
 search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where
   nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model
@@ -20,8 +17,16 @@ search: '|tstats `security_content_summariesonly` count min(_time) as firstTime
   All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)`
   | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `suspicious_email___uba_anomaly_filter`'
+how_to_implement: You must be ingesting data from email logs and have Splunk integrated
+  with UBA. This anomaly is raised by a UBA detection model called  "SuspiciousEmailDetectionModel."
+  Ensure that this model is enabled on your UBA instance.
+known_false_positives: This detection model will alert on any sender domain that is
+  seen for the first time. This could be a potential false positive. The next step
+  is to investigate and add the URL to an allow list if you determine that it is a
+  legitimate sender.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Emails
   asset_type: Endpoint
   cis20:
@@ -37,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 3
diff --git a/detections/experimental/application/suspicious_email_attachment_extensions.yml b/detections/experimental/application/suspicious_email_attachment_extensions.yml
index fa34dd4d08..a5ddc04ec4 100644
--- a/detections/experimental/application/suspicious_email_attachment_extensions.yml
+++ b/detections/experimental/application/suspicious_email_attachment_extensions.yml
@@ -1,7 +1,18 @@
-author: David Dorsey, Splunk
+name: Suspicious Email Attachment Extensions
+id: 473bd65f-06ca-4dfe-a2b8-ba04ab4a0084
+version: 3
 date: '2020-07-22'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Email
 description: This search looks for emails that have attachments with suspicious file
   extensions.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user,
+  All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments`
+  | `suspicious_email_attachment_extensions_filter` '
 how_to_implement: "You need to ingest data from emails. Specifically, the sender's\
   \ address and the file names of any attachments must be mapped to the Email data\
   \ model. \\\n **Splunk Phantom Playbook Integration**\\\nIf Splunk Phantom is also\
@@ -14,16 +25,10 @@ how_to_implement: "You need to ingest data from emails. Specifically, the sender
   \ gather further information about the file attachment and its network behaviors.\
   \ If Phantom finds malicious behavior and an analyst approves of the results, the\
   \ email will be deleted from the user's inbox."
-id: 473bd65f-06ca-4dfe-a2b8-ba04ab4a0084
 known_false_positives: None identified
-name: Suspicious Email Attachment Extensions
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user,
-  All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` |
-  `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments`
-  | `suspicious_email_attachment_extensions_filter` '
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Suspicious Emails
   asset_type: Endpoint
@@ -43,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/experimental/application/suspicious_java_classes.yml b/detections/experimental/application/suspicious_java_classes.yml
index 9df6bf67f0..20b9f4c9a4 100644
--- a/detections/experimental/application/suspicious_java_classes.yml
+++ b/detections/experimental/application/suspicious_java_classes.yml
@@ -1,22 +1,25 @@
-author: Jose Hernandez, Splunk
+name: Suspicious Java Classes
+id: if1fea6da-3c86-4c1d-b255-fc3b2781a491
+version: 1
 date: '2018-12-06'
+author: Jose Hernandez, Splunk
+type: batch
+datamodel: []
 description: This search looks for suspicious Java classes that are often used to
   exploit remote command execution in common Java frameworks, such as Apache Struts.
-how_to_implement: In order to properly run this search, Splunk needs to ingest data
-  from your web-traffic appliances that serve or sit in the path of your Struts application
-  servers. This can be accomplished by indexing data from a web proxy, or by using
-  network traffic-analysis tools, such as Splunk Stream or Bro.
-id: if1fea6da-3c86-4c1d-b255-fc3b2781a491
-known_false_positives: There are no known false positives.
-name: Suspicious Java Classes
-references: []
 search: '`stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)"
   | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time)
   as lastTime, values(url) as uri, values(status) as status, values(http_user_agent)
   as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `suspicious_java_classes_filter`'
+how_to_implement: In order to properly run this search, Splunk needs to ingest data
+  from your web-traffic appliances that serve or sit in the path of your Struts application
+  servers. This can be accomplished by indexing data from a web proxy, or by using
+  network traffic-analysis tools, such as Splunk Stream or Bro.
+known_false_positives: There are no known false positives.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Apache Struts Vulnerability
   asset_type: Endpoint
   cis20:
@@ -31,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml b/detections/experimental/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml
index b691324ee6..17fc6b562d 100644
--- a/detections/experimental/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml
+++ b/detections/experimental/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml
@@ -1,23 +1,26 @@
-author: Rod Soto, Splunk
+name: Amazon EKS Kubernetes cluster scan detection
+id: 294c4686-63dd-4fe6-93a2-ca807626704a
+version: 1
 date: '2020-04-15'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information of unauthenticated requests via user
   agent, and authentication data against Kubernetes cluster in AWS
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch
-  EKS Logs inputs.
-id: 294c4686-63dd-4fe6-93a2-ca807626704a
-known_false_positives: Not all unauthenticated requests are malicious, but frequency,
-  UA and source IPs will provide context.
-name: Amazon EKS Kubernetes cluster scan detection
-references: []
 search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS
   Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime
   max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name
   values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI)
   by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
   |`amazon_eks_kubernetes_cluster_scan_detection_filter` '
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch
+  EKS Logs inputs.
+known_false_positives: Not all unauthenticated requests are malicious, but frequency,
+  UA and source IPs will provide context.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Scanning Activity
   asset_type: Amazon EKS Kubernetes cluster
   kill_chain_phases:
@@ -29,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/amazon_eks_kubernetes_pod_scan_detection.yml b/detections/experimental/cloud/amazon_eks_kubernetes_pod_scan_detection.yml
index ec473ccf2d..7ec567a60c 100644
--- a/detections/experimental/cloud/amazon_eks_kubernetes_pod_scan_detection.yml
+++ b/detections/experimental/cloud/amazon_eks_kubernetes_pod_scan_detection.yml
@@ -1,24 +1,27 @@
-author: Rod Soto, Splunk
+name: Amazon EKS Kubernetes Pod scan detection
+id: dbfca1dd-b8e5-4ba4-be0e-e565e5d62002
+version: 1
 date: '2020-04-15'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection information on unauthenticated requests
   against Kubernetes' Pods API
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
-  and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch
-  EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection`
-  macro to filter out the false positives.
-id: dbfca1dd-b8e5-4ba4-be0e-e565e5d62002
-known_false_positives: Not all unauthenticated requests are malicious, but frequency,
-  UA and source IPs and direct request to API provide context.
-name: Amazon EKS Kubernetes Pod scan detection
-references: []
 search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods
   requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip
   | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason)
   values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by
   src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)`
   | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` '
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch
+  EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection`
+  macro to filter out the false positives.
+known_false_positives: Not all unauthenticated requests are malicious, but frequency,
+  UA and source IPs and direct request to API provide context.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Scanning Activity
   asset_type: Amazon EKS Kubernetes cluster Pod
   kill_chain_phases:
@@ -30,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/aws_detect_attach_to_role_policy.yml b/detections/experimental/cloud/aws_detect_attach_to_role_policy.yml
index 7d8795816e..ec33f24fd2 100644
--- a/detections/experimental/cloud/aws_detect_attach_to_role_policy.yml
+++ b/detections/experimental/cloud/aws_detect_attach_to_role_policy.yml
@@ -1,22 +1,25 @@
-author: Rod Soto, Splunk
+name: aws detect attach to role policy
+id: 88fc31dd-f331-448c-9856-d3d51dd5d3a1
+version: 1
 date: '2020-07-27'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of an user attaching itself to a different
   role trust policy. This can be used for lateral movement and escalation of privileges.
-how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This
-  search works with cloudwatch logs
-id: 88fc31dd-f331-448c-9856-d3d51dd5d3a1
-known_false_positives: Attach to policy can create a lot of noise. This search can
-  be adjusted to provide specific values to identify cases of abuse (i.e status=failure).
-  The search can provide context for common users attaching themselves to higher privilege
-  policies or even newly created policies.
-name: aws detect attach to role policy
-references: []
 search: '`aws_cloudwatchlogs_eks` attach policy| spath requestParameters.policyArn
   | table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn
   eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated
   userIdentity.sessionContext.attributes.creationDate  | `aws_detect_attach_to_role_policy_filter`'
+how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: Attach to policy can create a lot of noise. This search can
+  be adjusted to provide specific values to identify cases of abuse (i.e status=failure).
+  The search can provide context for common users attaching themselves to higher privilege
+  policies or even newly created policies.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
   asset_type: AWS Account
   kill_chain_phases:
@@ -28,5 +31,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/aws_detect_permanent_key_creation.yml b/detections/experimental/cloud/aws_detect_permanent_key_creation.yml
index e61bed234a..80b079210e 100644
--- a/detections/experimental/cloud/aws_detect_permanent_key_creation.yml
+++ b/detections/experimental/cloud/aws_detect_permanent_key_creation.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: aws detect permanent key creation
+id: 12d6d713-3cb4-4ffc-a064-1dca3d1cca01
+version: 1
 date: '2020-07-27'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of accounts creating permanent keys. Permanent
   keys are not created by default and they are only needed for programmatic calls.
   Creation of Permanent key is an important event to monitor.
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudwatch logs
-id: 12d6d713-3cb4-4ffc-a064-1dca3d1cca01
-known_false_positives: Not all permanent key creations are malicious. If there is
-  a policy of rotating keys this search can be adjusted to provide better context.
-name: aws detect permanent key creation
-references: []
 search: '`aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey
   "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent
   action status responseElements.accessKey.createDate responseElements.accessKey.status
   responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: Not all permanent key creations are malicious. If there is
+  a policy of rotating keys this search can be adjusted to provide better context.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
   asset_type: AWS Account
   kill_chain_phases:
@@ -27,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/aws_detect_role_creation.yml b/detections/experimental/cloud/aws_detect_role_creation.yml
index 5f75009e14..1841b3f6e5 100644
--- a/detections/experimental/cloud/aws_detect_role_creation.yml
+++ b/detections/experimental/cloud/aws_detect_role_creation.yml
@@ -1,24 +1,27 @@
-author: Rod Soto, Splunk
+name: aws detect role creation
+id: 5f04081e-ddee-4353-afe4-504f288de9ad
+version: 1
 date: '2020-07-27'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of role creation by IAM users. Role creation
   is an event by itself if user is creating a new role with trust policies different
   than the available in AWS and it can be used for lateral movement and escalation
   of privileges.
-how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This
-  search works with cloudwatch logs
-id: 5f04081e-ddee-4353-afe4-504f288de9ad
-known_false_positives: CreateRole is not very common in common users. This search
-  can be adjusted to provide specific values to identify cases of abuse. In general
-  AWS provides plenty of trust policies that fit most use cases.
-name: aws detect role creation
-references: []
 search: '`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole
   requestParameters.description=Allows* | table sourceIPAddress userIdentity.principalId
   userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName
   requestParameters.description responseElements.role.arn responseElements.role.createDate
   | `aws_detect_role_creation_filter`'
+how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: CreateRole is not very common in common users. This search
+  can be adjusted to provide specific values to identify cases of abuse. In general
+  AWS provides plenty of trust policies that fit most use cases.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
   asset_type: AWS Account
   kill_chain_phases:
@@ -30,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/aws_detect_sts_assume_role_abuse.yml b/detections/experimental/cloud/aws_detect_sts_assume_role_abuse.yml
index f52b8e0e25..99799e92b2 100644
--- a/detections/experimental/cloud/aws_detect_sts_assume_role_abuse.yml
+++ b/detections/experimental/cloud/aws_detect_sts_assume_role_abuse.yml
@@ -1,22 +1,25 @@
-author: Rod Soto, Splunk
+name: aws detect sts assume role abuse
+id: 8e565314-b6a2-46d8-9f05-1a34a176a662
+version: 1
 date: '2020-07-27'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of suspicious use of sts:AssumeRole. These
   tokens can be created on the go and used by attackers to move laterally and escalate
   privileges.
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudtrail logs
-id: 8e565314-b6a2-46d8-9f05-1a34a176a662
-known_false_positives: Sts:AssumeRole can be very noisy as it is a standard mechanism
-  to provide cross account and cross resources access. This search can be adjusted
-  to provide specific values to identify cases of abuse.
-name: aws detect sts assume role abuse
-references: []
 search: '`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role
   | table sourceIPAddress userIdentity.arn user_agent user_access_key status action
   requestParameters.roleName responseElements.role.roleName responseElements.role.createDate
   | `aws_detect_sts_assume_role_abuse_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudtrail logs
+known_false_positives: Sts:AssumeRole can be very noisy as it is a standard mechanism
+  to provide cross account and cross resources access. This search can be adjusted
+  to provide specific values to identify cases of abuse.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
   asset_type: AWS Account
   kill_chain_phases:
@@ -28,5 +31,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/aws_detect_sts_get_session_token_abuse.yml b/detections/experimental/cloud/aws_detect_sts_get_session_token_abuse.yml
index 3f3f58c9d3..a3c5eb90ee 100644
--- a/detections/experimental/cloud/aws_detect_sts_get_session_token_abuse.yml
+++ b/detections/experimental/cloud/aws_detect_sts_get_session_token_abuse.yml
@@ -1,22 +1,25 @@
-author: Rod Soto, Splunk
+name: aws detect sts get session token abuse
+id: 85d7b35f-b8b5-4b01-916f-29b81e7a0551
+version: 1
 date: '2020-07-27'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of suspicious use of sts:GetSessionToken.
   These tokens can be created on the go and used by attackers to move laterally and
   escalate privileges.
+search: '`aws_cloudwatchlogs_eks` ASIA  userIdentity.type=IAMUser| spath eventName
+  | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn
+  userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter`'
 how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This
   search works with cloudwatch logs
-id: 85d7b35f-b8b5-4b01-916f-29b81e7a0551
 known_false_positives: Sts:GetSessionToken can be very noisy as in certain environments
   numerous calls of this type can be executed. This search can be adjusted to provide
   specific values to identify cases of abuse. In specific environments the use of
   field requestParameters.serialNumber will need to be used.
-name: aws detect sts get session token abuse
 references: []
-search: '`aws_cloudwatchlogs_eks` ASIA  userIdentity.type=IAMUser| spath eventName
-  | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn
-  userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
   asset_type: AWS Account
   kill_chain_phases:
@@ -28,5 +31,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/gcp_detect_accounts_with_high_risk_roles_by_project.yml b/detections/experimental/cloud/gcp_detect_accounts_with_high_risk_roles_by_project.yml
index afa850e45e..417e2f1a2c 100644
--- a/detections/experimental/cloud/gcp_detect_accounts_with_high_risk_roles_by_project.yml
+++ b/detections/experimental/cloud/gcp_detect_accounts_with_high_risk_roles_by_project.yml
@@ -1,19 +1,13 @@
-author: Rod Soto, Splunk
+name: GCP Detect accounts with high risk roles by project
+id: 27af8c15-38b0-4408-b339-920170724adb
+version: 1
 date: '2020-10-09'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of accounts with high risk roles by projects.
   Compromised accounts with high risk roles can move laterally or even scalate privileges
   at different projects depending on organization schema.
-how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
-  logs
-id: 27af8c15-38b0-4408-b339-920170724adb
-known_false_positives: Accounts with high risk roles should be reduced to the minimum
-  number needed, however specific tasks and setups may be simply expected behavior
-  within organization
-name: GCP Detect accounts with high risk roles by project
-references:
-- https://github.com/dxa4481/gcploit
-- https://www.youtube.com/watch?v=Ml09R38jpok
-- https://cloud.google.com/iam/docs/understanding-roles
 search: '`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner
   OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin
   OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin
@@ -21,8 +15,17 @@ search: '`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}
   data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission
   data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role
   data.protoPayload.response.bindings{}.members{} | `gcp_detect_accounts_with_high_risk_roles_by_project_filter`'
+how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
+  logs
+known_false_positives: Accounts with high risk roles should be reduced to the minimum
+  number needed, however specific tasks and setups may be simply expected behavior
+  within organization
+references:
+- https://github.com/dxa4481/gcploit
+- https://www.youtube.com/watch?v=Ml09R38jpok
+- https://cloud.google.com/iam/docs/understanding-roles
 tags:
-  analytics_story:
+  analytic_story:
   - GCP Cross Account Activity
   asset_type: GCP Account
   kill_chain_phases:
@@ -34,5 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/gcp_detect_gcploit_framework.yml b/detections/experimental/cloud/gcp_detect_gcploit_framework.yml
index c7679fb673..7d112319e1 100644
--- a/detections/experimental/cloud/gcp_detect_gcploit_framework.yml
+++ b/detections/experimental/cloud/gcp_detect_gcploit_framework.yml
@@ -1,24 +1,27 @@
-author: Rod Soto, Splunk
+name: GCP Detect gcploit framework
+id: a1c5a85e-a162-410c-a5d9-99ff639e5a52
+version: 1
 date: '2020-10-08'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of GCPloit exploitation framework. This
   framework can be used to escalate privileges and move laterally from compromised
   high privilege accounts.
-how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
-  logs
-id: a1c5a85e-a162-410c-a5d9-99ff639e5a52
-known_false_positives: Payload.request.function.timeout value can possibly be match
-  with other functions or requests however the source user and target request account
-  may indicate an attempt to move laterally accross acounts or projects
-name: GCP Detect gcploit framework
-references:
-- https://github.com/dxa4481/gcploit
-- https://www.youtube.com/watch?v=Ml09R38jpok
 search: '`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s
   | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail
   data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location
   http_user_agent | `gcp_detect_gcploit_framework_filter`'
+how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
+  logs
+known_false_positives: Payload.request.function.timeout value can possibly be match
+  with other functions or requests however the source user and target request account
+  may indicate an attempt to move laterally accross acounts or projects
+references:
+- https://github.com/dxa4481/gcploit
+- https://www.youtube.com/watch?v=Ml09R38jpok
 tags:
-  analytics_story:
+  analytic_story:
   - GCP Cross Account Activity
   asset_type: GCP Account
   kill_chain_phases:
@@ -30,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/gcp_detect_high_risk_permissions_by_resource_and_account.yml b/detections/experimental/cloud/gcp_detect_high_risk_permissions_by_resource_and_account.yml
index 8147447b1c..e6f2d562b9 100644
--- a/detections/experimental/cloud/gcp_detect_high_risk_permissions_by_resource_and_account.yml
+++ b/detections/experimental/cloud/gcp_detect_high_risk_permissions_by_resource_and_account.yml
@@ -1,27 +1,30 @@
-author: Rod Soto, Splunk
+name: GCP Detect high risk permissions by resource and account
+id: 2e70ef35-2187-431f-aedc-4503dc9b06ba
+version: 1
 date: '2020-10-09'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of high risk permissions by resource and
   accounts. These are permissions that can allow attackers with compromised accounts
   to move laterally and escalate privileges.
-how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
-  logs
-id: 2e70ef35-2187-431f-aedc-4503dc9b06ba
-known_false_positives: High risk permissions are part of any GCP environment, however
-  it is important to track resource and accounts usage, this search may produce false
-  positives.
-name: GCP Detect high risk permissions by resource and account
-references:
-- https://github.com/dxa4481/gcploit
-- https://www.youtube.com/watch?v=Ml09R38jpok
-- https://cloud.google.com/iam/docs/permissions-reference
 search: '`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken
   OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create
   OR composer.environments.create OR dataproc.clusters.create |table data.protoPayload.requestMetadata.callerIp
   data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission
   data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id
   | `gcp_detect_high_risk_permissions_by_resource_and_account_filter`'
+how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
+  logs
+known_false_positives: High risk permissions are part of any GCP environment, however
+  it is important to track resource and accounts usage, this search may produce false
+  positives.
+references:
+- https://github.com/dxa4481/gcploit
+- https://www.youtube.com/watch?v=Ml09R38jpok
+- https://cloud.google.com/iam/docs/permissions-reference
 tags:
-  analytics_story:
+  analytic_story:
   - GCP Cross Account Activity
   asset_type: GCP Account
   kill_chain_phases:
@@ -33,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/gcp_detect_oauth_token_abuse.yml b/detections/experimental/cloud/gcp_detect_oauth_token_abuse.yml
index 33957f6d3a..78040c1056 100644
--- a/detections/experimental/cloud/gcp_detect_oauth_token_abuse.yml
+++ b/detections/experimental/cloud/gcp_detect_oauth_token_abuse.yml
@@ -1,23 +1,26 @@
-author: Rod Soto, Splunk
+name: gcp detect oauth token abuse
+id: a7e9f7bb-8901-4ad0-8d88-0a4ab07b1972
+version: 1
 date: '2020-09-01'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides detection of possible GCP Oauth token abuse. GCP
   Oauth token without time limit can be exfiltrated and reused for keeping access
   sessions alive without further control of authentication, allowing attackers to
   access and move laterally.
-how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
-  logs
-id: a7e9f7bb-8901-4ad0-8d88-0a4ab07b1972
-known_false_positives: GCP Oauth token abuse detection will only work if there are
-  access policies in place along with audit logs.
-name: gcp detect oauth token abuse
-references:
-- https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1
-- https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2
 search: '`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog
   |table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp
   protoPayload.status.details{}.violations{}.type protoPayload.status.message  | `gcp_detect_oauth_token_abuse_filter`'
+how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
+  logs
+known_false_positives: GCP Oauth token abuse detection will only work if there are
+  access policies in place along with audit logs.
+references:
+- https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1
+- https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2
 tags:
-  analytics_story:
+  analytic_story:
   - GCP Cross Account Activity
   asset_type: GCP Account
   kill_chain_phases:
@@ -29,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml b/detections/experimental/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml
index e11a9c6671..09d4e4b55d 100644
--- a/detections/experimental/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml
+++ b/detections/experimental/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml
@@ -1,19 +1,22 @@
-author: Rod Soto, Splunk
+name: GCP Kubernetes cluster pod scan detection
+id: 19b53215-4a16-405b-8087-9e6acf619842
+version: 1
 date: '2020-07-17'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information of unauthenticated requests via user
   agent, and authentication data against Kubernetes cluster's pods
-how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later),
-  then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
-id: 19b53215-4a16-405b-8087-9e6acf619842
-known_false_positives: Not all unauthenticated requests are malicious, but frequency,
-  User Agent, source IPs and pods  will provide context.
-name: GCP Kubernetes cluster pod scan detection
-references: []
 search: '`google_gcp_pubsub_message` category=kube-audit |spath input=properties.log
   |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason
   properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter`'
+how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later),
+  then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
+known_false_positives: Not all unauthenticated requests are malicious, but frequency,
+  User Agent, source IPs and pods  will provide context.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Scanning Activity
   asset_type: GCP Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/gcp_kubernetes_cluster_scan_detection.yml b/detections/experimental/cloud/gcp_kubernetes_cluster_scan_detection.yml
index 2d50e21f20..87ac299a8f 100644
--- a/detections/experimental/cloud/gcp_kubernetes_cluster_scan_detection.yml
+++ b/detections/experimental/cloud/gcp_kubernetes_cluster_scan_detection.yml
@@ -1,16 +1,12 @@
-author: Rod Soto, Splunk
+name: GCP Kubernetes cluster scan detection
+id: db5957ec-0144-4c56-b512-9dccbe7a2d26
+version: 1
 date: '2020-04-15'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information of unauthenticated requests via user
   agent, and authentication data against Kubernetes cluster
-how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later),
-  then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
-  You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection
-  to filter out FPs.
-id: db5957ec-0144-4c56-b512-9dccbe7a2d26
-known_false_positives: Not all unauthenticated requests are malicious, but frequency,
-  User Agent and source IPs will provide context.
-name: GCP Kubernetes cluster scan detection
-references: []
 search: '`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1
   data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid
   "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous"
@@ -20,8 +16,15 @@ search: '`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!
   as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name
   as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`  |
   `gcp_kubernetes_cluster_scan_detection_filter` '
+how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later),
+  then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
+  You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection
+  to filter out FPs.
+known_false_positives: Not all unauthenticated requests are malicious, but frequency,
+  User Agent and source IPs will provide context.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Scanning Activity
   asset_type: GCP Kubernetes cluster
   kill_chain_phases:
@@ -33,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml b/detections/experimental/cloud/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml
index 41495c12fa..6db594f667 100644
--- a/detections/experimental/cloud/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml
+++ b/detections/experimental/cloud/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml
@@ -1,19 +1,22 @@
-author: Rod Soto, Splunk
+name: Kubernetes AWS detect most active service accounts by pod
+id: 5b30b25d-7d32-42d8-95ca-64dfcd9076e6
+version: 1
 date: '2020-06-23'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes service accounts,accessing
   pods by IP address, verb and decision
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudwatch logs
-id: 5b30b25d-7d32-42d8-95ca-64dfcd9076e6
-known_false_positives: Not all service accounts interactions are malicious. Analyst
-  must consider IP, verb and decision context when trying to detect maliciousness.
-name: Kubernetes AWS detect most active service accounts by pod
-references: []
 search: '`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts  objectRef.resource=pods
   | table  sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision  |
   top  sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: Not all service accounts interactions are malicious. Analyst
+  must consider IP, verb and decision context when trying to detect maliciousness.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: AWS EKS Kubernetes cluster
   kill_chain_phases:
@@ -23,5 +26,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_aws_detect_rbac_authorizations_by_account.yml b/detections/experimental/cloud/kubernetes_aws_detect_rbac_authorizations_by_account.yml
index 4924538b7f..eb823b8f5b 100644
--- a/detections/experimental/cloud/kubernetes_aws_detect_rbac_authorizations_by_account.yml
+++ b/detections/experimental/cloud/kubernetes_aws_detect_rbac_authorizations_by_account.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: Kubernetes AWS detect RBAC authorization by account
+id: de7264ed-3ed9-4fef-bb01-6eefc87cefe8
+version: 1
 date: '2020-06-23'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes RBAC authorizations by
   accounts, this search can be modified by adding top to see both extremes of RBAC
   by accounts occurrences
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudwatch logs
-id: de7264ed-3ed9-4fef-bb01-6eefc87cefe8
-known_false_positives: Not all RBAC Authorications are malicious. RBAC authorizations
-  can uncover malicious activity specially if sensitive Roles have been granted.
-name: Kubernetes AWS detect RBAC authorization by account
-references: []
 search: '`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* | table
   sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason | stats
   count by user.username annotations.authorization.k8s.io/reason | rare user.username
   annotations.authorization.k8s.io/reason |`kubernetes_aws_detect_rbac_authorization_by_account_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: Not all RBAC Authorications are malicious. RBAC authorizations
+  can uncover malicious activity specially if sensitive Roles have been granted.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: AWS EKS Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_aws_detect_sensitive_object_access.yml b/detections/experimental/cloud/kubernetes_aws_detect_sensitive_object_access.yml
index f6c76c92b6..53741c5860 100644
--- a/detections/experimental/cloud/kubernetes_aws_detect_sensitive_object_access.yml
+++ b/detections/experimental/cloud/kubernetes_aws_detect_sensitive_object_access.yml
@@ -1,20 +1,23 @@
-author: Rod Soto, Splunk
+name: AWS EKS Kubernetes cluster sensitive object access
+id: 7f227943-2196-4d4d-8d6a-ac8cb308e61c
+version: 1
 date: '2020-06-23'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes accounts accessing sensitve
   objects such as configmaps or secrets
-how_to_implement: You must install Splunk Add-on for Amazon Web Services and Splunk
-  App for AWS. This search works with cloudwatch logs.
-id: 7f227943-2196-4d4d-8d6a-ac8cb308e61c
-known_false_positives: Sensitive object access is not necessarily malicious but user
-  and object context can provide guidance for detection.
-name: AWS EKS Kubernetes cluster sensitive object access
-references: []
 search: '`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1
   sourceIPs{}!=127.0.0.1  |table sourceIPs{} user.username user.groups{} objectRef.resource
   objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup
   user.username user.groups{} |`aws_eks_kubernetes_cluster_sensitive_object_access_filter`'
+how_to_implement: You must install Splunk Add-on for Amazon Web Services and Splunk
+  App for AWS. This search works with cloudwatch logs.
+known_false_positives: Sensitive object access is not necessarily malicious but user
+  and object context can provide guidance for detection.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: AWS EKS Kubernetes cluster
   kill_chain_phases:
@@ -24,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_aws_detect_sensitive_role_access.yml b/detections/experimental/cloud/kubernetes_aws_detect_sensitive_role_access.yml
index 875a1f9b23..26deca7fd1 100644
--- a/detections/experimental/cloud/kubernetes_aws_detect_sensitive_role_access.yml
+++ b/detections/experimental/cloud/kubernetes_aws_detect_sensitive_role_access.yml
@@ -1,20 +1,23 @@
-author: Rod Soto, Splunk
+name: Kubernetes AWS detect sensitive role access
+id: b6013a7b-85e0-4a45-b051-10b252d69569
+version: 1
 date: '2020-06-23'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes accounts accessing sensitve
   objects such as configmpas or secrets
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudwatch logs.
-id: b6013a7b-85e0-4a45-b051-10b252d69569
-known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
-  however source IP, namespace and user group may indicate possible malicious use. '
-name: Kubernetes AWS detect sensitive role access
-references: []
 search: '`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings
   sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1  | table sourceIPs{} user.username user.groups{}
   objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username
   user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudwatch logs.
+known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
+  however source IP, namespace and user group may indicate possible malicious use. '
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: AWS EKS Kubernetes cluster
   kill_chain_phases:
@@ -24,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml b/detections/experimental/cloud/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml
index 4d766a53c1..36a83160fb 100644
--- a/detections/experimental/cloud/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml
+++ b/detections/experimental/cloud/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: Kubernetes AWS detect service accounts forbidden failure access
+id: a6959c57-fa8f-4277-bb86-7c32fba579d5
+version: 1
 date: '2020-06-23'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes service accounts with
   failure or forbidden access status, this search can be extended by using top or
   rare operators to find trends or rarities in failure status, user agents, source
   IPs and request URI
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudwatch logs.
-id: a6959c57-fa8f-4277-bb86-7c32fba579d5
-known_false_positives: This search can give false positives as there might be inherent
-  issues with authentications and permissions at cluster.
-name: Kubernetes AWS detect service accounts forbidden failure access
-references: []
 search: '`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status
   = Failure | table sourceIPs{} user.username userAgent verb responseStatus.status
   requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudwatch logs.
+known_false_positives: This search can give false positives as there might be inherent
+  issues with authentications and permissions at cluster.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: AWS EKS Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml b/detections/experimental/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml
index 8cf3a9d30d..ff8cba3f9e 100644
--- a/detections/experimental/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml
+++ b/detections/experimental/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml
@@ -1,20 +1,23 @@
-author: Rod Soto, Splunk
+name: Kubernetes AWS detect suspicious kubectl calls
+id: 042a3d32-8318-4763-9679-09db2644a8f2
+version: 1
 date: '2020-06-23'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on anonymous Kubectl calls with IP,
   verb namespace and object access context
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
-  search works with cloudwatch logs.
-id: 042a3d32-8318-4763-9679-09db2644a8f2
-known_false_positives: Kubectl calls are not malicious by nature. However source IP,
-  verb and Object can reveal potential malicious activity, specially anonymous suspicious
-  IPs and sensitive objects such as configmaps or secrets
-name: Kubernetes AWS detect suspicious kubectl calls
-references: []
 search: '`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1
   src_user=system:anonymous  | table  src_ip src_user verb userAgent requestURI  |
   stats  count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudwatch logs.
+known_false_positives: Kubectl calls are not malicious by nature. However source IP,
+  verb and Object can reveal potential malicious activity, specially anonymous suspicious
+  IPs and sensitive objects such as configmaps or secrets
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: AWS EKS Kubernetes cluster
   kill_chain_phases:
@@ -24,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml b/detections/experimental/cloud/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml
index 76a0d548db..df72e536e1 100644
--- a/detections/experimental/cloud/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml
+++ b/detections/experimental/cloud/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: Kubernetes Azure detect most active service accounts by pod namespace
+id: 55a2264a-b7f0-45e5-addd-1e5ab3415c72
+version: 1
 date: '2020-05-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes service accounts,accessing
   pods and namespaces by IP address and verb
-how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
-  Kube-Audit data diagnostics
-id: 55a2264a-b7f0-45e5-addd-1e5ab3415c72
-known_false_positives: Not all service accounts interactions are malicious. Analyst
-  must consider IP and verb context when trying to detect maliciousness.
-name: Kubernetes Azure detect most active service accounts by pod namespace
-references: []
 search: '`kubernetes_azure` category=kube-audit | spath input=properties.log | search
   user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow  |
   table  sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status
   properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status
   properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter`'
+how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
+  Kube-Audit data diagnostics
+known_false_positives: Not all service accounts interactions are malicious. Analyst
+  must consider IP and verb context when trying to detect maliciousness.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: Azure AKS Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_azure_detect_rbac_authorization_by_account.yml b/detections/experimental/cloud/kubernetes_azure_detect_rbac_authorization_by_account.yml
index d4d9923682..be00530427 100644
--- a/detections/experimental/cloud/kubernetes_azure_detect_rbac_authorization_by_account.yml
+++ b/detections/experimental/cloud/kubernetes_azure_detect_rbac_authorization_by_account.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: Kubernetes Azure detect RBAC authorization by account
+id: 47af7d20-0607-4079-97d7-7a29af58b54e
+version: 1
 date: '2020-05-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes RBAC authorizations by
   accounts, this search can be modified by adding rare or top to see both extremes
   of RBAC by accounts occurrences
-how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
-  Kube-Audit data diagnostics
-id: 47af7d20-0607-4079-97d7-7a29af58b54e
-known_false_positives: Not all RBAC Authorications are malicious. RBAC authorizations
-  can uncover malicious activity specially if sensitive Roles have been granted.
-name: Kubernetes Azure detect RBAC authorization by account
-references: []
 search: '`kubernetes_azure` category=kube-audit | spath input=properties.log | search
   annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent
   annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason
   | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter`'
+how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
+  Kube-Audit data diagnostics
+known_false_positives: Not all RBAC Authorications are malicious. RBAC authorizations
+  can uncover malicious activity specially if sensitive Roles have been granted.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: Azure AKS Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_azure_detect_sensitive_object_access.yml b/detections/experimental/cloud/kubernetes_azure_detect_sensitive_object_access.yml
index 01ff5ccc58..de93ee2c4f 100644
--- a/detections/experimental/cloud/kubernetes_azure_detect_sensitive_object_access.yml
+++ b/detections/experimental/cloud/kubernetes_azure_detect_sensitive_object_access.yml
@@ -1,20 +1,23 @@
-author: Rod Soto, Splunk
+name: Kubernetes Azure detect sensitive object access
+id: 1bba382b-07fd-4ffa-b390-8002739b76e8
+version: 1
 date: '2020-05-20'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes accounts accessing sensitve
   objects such as configmpas or secrets
-how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
-  Kube-Audit data diagnostics
-id: 1bba382b-07fd-4ffa-b390-8002739b76e8
-known_false_positives: Sensitive object access is not necessarily malicious but user
-  and object context can provide guidance for detection.
-name: Kubernetes Azure detect sensitive object access
-references: []
 search: '`kubernetes_azure` category=kube-audit | spath input=properties.log| search
   objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow  |table
   user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name
   annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter`'
+how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
+  Kube-Audit data diagnostics
+known_false_positives: Sensitive object access is not necessarily malicious but user
+  and object context can provide guidance for detection.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: Azure AKS Kubernetes cluster
   kill_chain_phases:
@@ -24,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_azure_detect_sensitive_role_access.yml b/detections/experimental/cloud/kubernetes_azure_detect_sensitive_role_access.yml
index 2c2f2a46a6..fe1a6e62b9 100644
--- a/detections/experimental/cloud/kubernetes_azure_detect_sensitive_role_access.yml
+++ b/detections/experimental/cloud/kubernetes_azure_detect_sensitive_role_access.yml
@@ -1,20 +1,23 @@
-author: Rod Soto, Splunk
+name: Kubernetes Azure detect sensitive role access
+id: f27349e5-1641-4f6a-9e68-30402be0ad4c
+version: 1
 date: '2020-05-20'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes accounts accessing sensitve
   objects such as configmpas or secrets
-how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
-  Kube-Audit data diagnostics
-id: f27349e5-1641-4f6a-9e68-30402be0ad4c
-known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
-  however source IP, namespace and user group may indicate possible malicious use. '
-name: Kubernetes Azure detect sensitive role access
-references: []
 search: '`kubernetes_azure` category=kube-audit | spath input=properties.log| search
   objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username
   user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason
   | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter`'
+how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
+  Kube-Audit data diagnostics
+known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
+  however source IP, namespace and user group may indicate possible malicious use. '
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: Azure AKS Kubernetes cluster
   kill_chain_phases:
@@ -24,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml b/detections/experimental/cloud/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml
index fefc8b4139..c46ac27650 100644
--- a/detections/experimental/cloud/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml
+++ b/detections/experimental/cloud/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml
@@ -1,20 +1,23 @@
-author: Rod Soto, Splunk
+name: Kubernetes Azure detect service accounts forbidden failure access
+id: 019690d7-420f-4da0-b320-f27b09961514
+version: 1
 date: '2020-05-20'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes service accounts with
   failure or forbidden access status
-how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
-  Kube-Audit data diagnostics
-id: 019690d7-420f-4da0-b320-f27b09961514
-known_false_positives: This search can give false positives as there might be inherent
-  issues with authentications and permissions at cluster.
-name: Kubernetes Azure detect service accounts forbidden failure access
-references: []
 search: '`kubernetes_azure` category=kube-audit | spath input=properties.log | search
   user.groups{}=system:serviceaccounts*  responseStatus.reason=Forbidden | table  sourceIPs{}
   user.username userAgent verb responseStatus.reason responseStatus.status properties.pod
   objectRef.namespace  |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter`'
+how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
+  Kube-Audit data diagnostics
+known_false_positives: This search can give false positives as there might be inherent
+  issues with authentications and permissions at cluster.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: Azure AKS Kubernetes cluster
   kill_chain_phases:
@@ -24,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_azure_detect_suspicious_kubectl_calls.yml b/detections/experimental/cloud/kubernetes_azure_detect_suspicious_kubectl_calls.yml
index 1f7874b6c8..2fa956e69c 100644
--- a/detections/experimental/cloud/kubernetes_azure_detect_suspicious_kubectl_calls.yml
+++ b/detections/experimental/cloud/kubernetes_azure_detect_suspicious_kubectl_calls.yml
@@ -1,23 +1,26 @@
-author: Rod Soto, Splunk
+name: Kubernetes Azure detect suspicious kubectl calls
+id: 4b6d1ba8-0000-4cec-87e6-6cbbd71651b5
+version: 1
 date: '2020-05-26'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on rare Kubectl calls with IP, verb
   namespace and object access context
-how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
-  Kube-Audit data diagnostics
-id: 4b6d1ba8-0000-4cec-87e6-6cbbd71651b5
-known_false_positives: Kubectl calls are not malicious by nature. However source IP,
-  verb and Object can reveal potential malicious activity, specially suspicious IPs
-  and sensitive objects such as configmaps or secrets
-name: Kubernetes Azure detect suspicious kubectl calls
-references: []
 search: '`kubernetes_azure` category=kube-audit | spath input=properties.log | spath
   input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration
   | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{}
   verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI |
   rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace
   requestURI |`kubernetes_azure_detect_suspicious_kubectl_calls_filter`'
+how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
+  Kube-Audit data diagnostics
+known_false_positives: Kubectl calls are not malicious by nature. However source IP,
+  verb and Object can reveal potential malicious activity, specially suspicious IPs
+  and sensitive objects such as configmaps or secrets
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: Azure AKS Kubernetes cluster
   kill_chain_phases:
@@ -27,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_azure_pod_scan_fingerprint.yml b/detections/experimental/cloud/kubernetes_azure_pod_scan_fingerprint.yml
index 20e0d7728f..3c337e5e26 100644
--- a/detections/experimental/cloud/kubernetes_azure_pod_scan_fingerprint.yml
+++ b/detections/experimental/cloud/kubernetes_azure_pod_scan_fingerprint.yml
@@ -1,20 +1,23 @@
-author: Rod Soto, Splunk
+name: Kubernetes Azure pod scan fingerprint
+id: 86aad3e0-732f-4f66-bbbc-70df448e461d
+version: 1
 date: '2020-05-20'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information of unauthenticated requests via source
   IP user agent, request URI and response status data against Kubernetes cluster pod
   in Azure
-how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
-  Kube-Audit data diagnostics
-id: 86aad3e0-732f-4f66-bbbc-70df448e461d
-known_false_positives: Not all unauthenticated requests are malicious, but source
-  IPs, userAgent, verb, request URI and response status will provide context.
-name: Kubernetes Azure pod scan fingerprint
-references: []
 search: '`kubernetes_azure` category=kube-audit | spath input=properties.log | search
   responseStatus.code=401 | table  sourceIPs{} userAgent verb requestURI responseStatus.reason
   properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter`'
+how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
+  Kube-Audit data diagnostics
+known_false_positives: Not all unauthenticated requests are malicious, but source
+  IPs, userAgent, verb, request URI and response status will provide context.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Scanning Activity
   asset_type: Azure AKS Kubernetes cluster
   kill_chain_phases:
@@ -24,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_azure_scan_fingerprint.yml b/detections/experimental/cloud/kubernetes_azure_scan_fingerprint.yml
index 20cab4f98b..287d0e96a1 100644
--- a/detections/experimental/cloud/kubernetes_azure_scan_fingerprint.yml
+++ b/detections/experimental/cloud/kubernetes_azure_scan_fingerprint.yml
@@ -1,20 +1,23 @@
-author: Rod Soto, Splunk
+name: Kubernetes Azure scan fingerprint
+id: c5e5bd5c-1013-4841-8b23-e7b3253c840a
+version: 1
 date: '2020-05-19'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information of unauthenticated requests via source
   IP user agent, request URI and response status data against Kubernetes cluster in
   Azure
-how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
-  Kube-Audit data diagnostics
-id: c5e5bd5c-1013-4841-8b23-e7b3253c840a
-known_false_positives: Not all unauthenticated requests are malicious, but source
-  IPs, userAgent, verb, request URI and response status will provide context.
-name: Kubernetes Azure scan fingerprint
-references: []
 search: '`kubernetes_azure` category=kube-audit | spath input=properties.log | search
   responseStatus.code=401 | table  sourceIPs{} userAgent verb requestURI responseStatus.reason
   |`kubernetes_azure_scan_fingerprint_filter`'
+how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
+  Kube-Audit data diagnostics
+known_false_positives: Not all unauthenticated requests are malicious, but source
+  IPs, userAgent, verb, request URI and response status will provide context.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Scanning Activity
   asset_type: Azure AKS Kubernetes cluster
   kill_chain_phases:
@@ -26,5 +29,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_gcp_detect_RBAC_authorizations_by_account.yml b/detections/experimental/cloud/kubernetes_gcp_detect_RBAC_authorizations_by_account.yml
index 747d112a8b..acfbad5842 100644
--- a/detections/experimental/cloud/kubernetes_gcp_detect_RBAC_authorizations_by_account.yml
+++ b/detections/experimental/cloud/kubernetes_gcp_detect_RBAC_authorizations_by_account.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: Kubernetes GCP detect RBAC authorizations by account
+id: 99487de3-7192-4b41-939d-fbe9acfb1340
+version: 1
 date: '2020-07-11'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes RBAC authorizations by
   accounts, this search can be modified by adding top to see both extremes of RBAC
   by accounts occurrences
-how_to_implement: You must install splunk AWS add on for GCP. This search works with
-  pubsub messaging service logs
-id: 99487de3-7192-4b41-939d-fbe9acfb1340
-known_false_positives: Not all RBAC Authorications are malicious. RBAC authorizations
-  can uncover malicious activity specially if sensitive Roles have been granted.
-name: Kubernetes GCP detect RBAC authorizations by account
-references: []
 search: '`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding
   OR Clusterrole  | table src_ip src_user data.labels.authorization.k8s.io/decision
   data.labels.authorization.k8s.io/reason | rare src_user data.labels.authorization.k8s.io/reason
   |`kubernetes_gcp_detect_rbac_authorizations_by_account_filter`'
+how_to_implement: You must install splunk AWS add on for GCP. This search works with
+  pubsub messaging service logs
+known_false_positives: Not all RBAC Authorications are malicious. RBAC authorizations
+  can uncover malicious activity specially if sensitive Roles have been granted.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: GCP GKE Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_gcp_detect_most_active_service_accounts_by_pod.yml b/detections/experimental/cloud/kubernetes_gcp_detect_most_active_service_accounts_by_pod.yml
index 458bd4e19d..df859c23a8 100644
--- a/detections/experimental/cloud/kubernetes_gcp_detect_most_active_service_accounts_by_pod.yml
+++ b/detections/experimental/cloud/kubernetes_gcp_detect_most_active_service_accounts_by_pod.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: Kubernetes GCP detect most active service accounts by pod
+id: 7f5c2779-88a0-4824-9caa-0f606c8f260f
+version: 1
 date: '2020-07-10'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes service accounts,accessing
   pods by IP address, verb and decision
-how_to_implement: You must install splunk GCP add on. This search works with pubsub
-  messaging service logs
-id: 7f5c2779-88a0-4824-9caa-0f606c8f260f
-known_false_positives: Not all service accounts interactions are malicious. Analyst
-  must consider IP, verb and decision context when trying to detect maliciousness.
-name: Kubernetes GCP detect most active service accounts by pod
-references: []
 search: '`google_gcp_pubsub_message`  data.protoPayload.request.spec.group{}=system:serviceaccounts
   | table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb
   data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource
   | top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision
   data.protoPayload.response.spec.resourceAttributes.resource |`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter`'
+how_to_implement: You must install splunk GCP add on. This search works with pubsub
+  messaging service logs
+known_false_positives: Not all service accounts interactions are malicious. Analyst
+  must consider IP, verb and decision context when trying to detect maliciousness.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: GCP GKE Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_gcp_detect_sensitive_object_access.yml b/detections/experimental/cloud/kubernetes_gcp_detect_sensitive_object_access.yml
index 6f1afae57b..4c37d551b2 100644
--- a/detections/experimental/cloud/kubernetes_gcp_detect_sensitive_object_access.yml
+++ b/detections/experimental/cloud/kubernetes_gcp_detect_sensitive_object_access.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: Kubernetes GCP detect sensitive object access
+id: bdb6d596-86a0-4aba-8369-418ae8b9963a
+version: 1
 date: '2020-07-11'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes accounts accessing sensitve
   objects such as configmaps or secrets
-how_to_implement: You must install splunk add on for GCP . This search works with
-  pubsub messaging service logs.
-id: bdb6d596-86a0-4aba-8369-418ae8b9963a
-known_false_positives: Sensitive object access is not necessarily malicious but user
-  and object context can provide guidance for detection.
-name: Kubernetes GCP detect sensitive object access
-references: []
 search: '`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps
   OR secrets  | table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name
   data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision
   | dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name
   |`kubernetes_gcp_detect_sensitive_object_access_filter`'
+how_to_implement: You must install splunk add on for GCP . This search works with
+  pubsub messaging service logs.
+known_false_positives: Sensitive object access is not necessarily malicious but user
+  and object context can provide guidance for detection.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: GCP GKE Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_gcp_detect_sensitive_role_access.yml b/detections/experimental/cloud/kubernetes_gcp_detect_sensitive_role_access.yml
index 41668b0e1c..aa3b1e3cac 100644
--- a/detections/experimental/cloud/kubernetes_gcp_detect_sensitive_role_access.yml
+++ b/detections/experimental/cloud/kubernetes_gcp_detect_sensitive_role_access.yml
@@ -1,21 +1,24 @@
-author: Rod Soto, Splunk
+name: Kubernetes GCP detect sensitive role access
+id: a46923f6-36b9-4806-a681-31f314907c30
+version: 1
 date: '2020-07-11'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes accounts accessing sensitve
   objects such as configmpas or secrets
-how_to_implement: You must install splunk add on for GCP. This search works with pubsub
-  messaging servicelogs.
-id: a46923f6-36b9-4806-a681-31f314907c30
-known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
-  however source IP, user agent, decision and reason may indicate possible malicious
-  use. '
-name: Kubernetes GCP detect sensitive role access
-references: []
 search: '`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding
   OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1  | table src_ip
   src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason
   | dedup src_ip src_user |`kubernetes_gcp_detect_sensitive_role_access_filter`'
+how_to_implement: You must install splunk add on for GCP. This search works with pubsub
+  messaging servicelogs.
+known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
+  however source IP, user agent, decision and reason may indicate possible malicious
+  use. '
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Role Activity
   asset_type: GCP GKE EKS Kubernetes cluster
   kill_chain_phases:
@@ -25,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_gcp_detect_service_accounts_forbidden_failure_access.yml b/detections/experimental/cloud/kubernetes_gcp_detect_service_accounts_forbidden_failure_access.yml
index eae4377948..a05269a924 100644
--- a/detections/experimental/cloud/kubernetes_gcp_detect_service_accounts_forbidden_failure_access.yml
+++ b/detections/experimental/cloud/kubernetes_gcp_detect_service_accounts_forbidden_failure_access.yml
@@ -1,23 +1,26 @@
-author: Rod Soto, Splunk
+name: Kubernetes GCP detect service accounts forbidden failure access
+id: 7094808d-432a-48e7-bb3c-77e96c894f3b
+version: 1
 date: '2020-06-23'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on Kubernetes service accounts with
   failure or forbidden access status, this search can be extended by using top or
   rare operators to find trends or rarities in failure status, user agents, source
   IPs and request URI
-how_to_implement: You must install splunk add on for GCP. This search works with pubsub
-  messaging service logs.
-id: 7094808d-432a-48e7-bb3c-77e96c894f3b
-known_false_positives: This search can give false positives as there might be inherent
-  issues with authentications and permissions at cluster.
-name: Kubernetes GCP detect service accounts forbidden failure access
-references: []
 search: '`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=*
   | table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace
   data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb  data.protoPayload.request.status.allowed
   data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision
   | dedup src_ip src_user | `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter`'
+how_to_implement: You must install splunk add on for GCP. This search works with pubsub
+  messaging service logs.
+known_false_positives: This search can give false positives as there might be inherent
+  issues with authentications and permissions at cluster.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: GCP GKE Kubernetes cluster
   kill_chain_phases:
@@ -27,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/cloud/kubernetes_gcp_detect_suspicious_kubectl_calls.yml b/detections/experimental/cloud/kubernetes_gcp_detect_suspicious_kubectl_calls.yml
index b09ee1e24e..7123237ecf 100644
--- a/detections/experimental/cloud/kubernetes_gcp_detect_suspicious_kubectl_calls.yml
+++ b/detections/experimental/cloud/kubernetes_gcp_detect_suspicious_kubectl_calls.yml
@@ -1,22 +1,25 @@
-author: Rod Soto, Splunk
+name: Kubernetes GCP detect suspicious kubectl calls
+id: a5bed417-070a-41f2-a1e4-82b6aa281557
+version: 1
 date: '2020-07-11'
+author: Rod Soto, Splunk
+type: batch
+datamodel: []
 description: This search provides information on anonymous Kubectl calls with IP,
   verb namespace and object access context
-how_to_implement: You must install splunk add on for GCP. This search works with pubsub
-  messaging logs.
-id: a5bed417-070a-41f2-a1e4-82b6aa281557
-known_false_positives: Kubectl calls are not malicious by nature. However source IP,
-  source user, user agent, object path, and authorization context can reveal potential
-  malicious activity, specially anonymous suspicious IPs and sensitive objects such
-  as configmaps or secrets
-name: Kubernetes GCP detect suspicious kubectl calls
-references: []
 search: '`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl*
   src_user=system:unsecured OR src_user=system:anonymous | table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent
   data.protoPayload.authorizationInfo{}.granted object_path |dedup src_ip src_user
   |`kubernetes_gcp_detect_suspicious_kubectl_calls_filter`'
+how_to_implement: You must install splunk add on for GCP. This search works with pubsub
+  messaging logs.
+known_false_positives: Kubectl calls are not malicious by nature. However source IP,
+  source user, user agent, object path, and authorization context can reveal potential
+  malicious activity, specially anonymous suspicious IPs and sensitive objects such
+  as configmaps or secrets
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Sensitive Object Access Activity
   asset_type: GCP GKE Kubernetes cluster
   kill_chain_phases:
@@ -26,5 +29,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/endpoint/child_processes_of_spoolsv_exe.yml b/detections/experimental/endpoint/child_processes_of_spoolsv_exe.yml
index 59cd0ed0c5..21247f7933 100644
--- a/detections/experimental/endpoint/child_processes_of_spoolsv_exe.yml
+++ b/detections/experimental/endpoint/child_processes_of_spoolsv_exe.yml
@@ -1,28 +1,32 @@
-author: Rico Valdez, Splunk
+name: Child Processes of Spoolsv exe
+id: aa0c4aeb-5b18-41c4-8c07-f1442d7599df
+version: 3
 date: '2020-03-16'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for child processes of spoolsv.exe. This activity is
   associated with a POC privilege-escalation exploit associated with CVE-2018-8440.
   Spoolsv.exe is the process associated with the Print Spooler service in Windows
   and typically runs as SYSTEM.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro
-  to filter out legitimate child processes spawned by spoolsv.exe.
-id: aa0c4aeb-5b18-41c4-8c07-f1442d7599df
-known_false_positives: Some legitimate printer-related processes may show up as children
-  of spoolsv.exe. You should confirm that any activity as legitimate and may be added
-  as exclusions in the search.
-name: Child Processes of Spoolsv exe
-references: []
 search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
   as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
   as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe
   AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process
   Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter` '
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro
+  to filter out legitimate child processes spawned by spoolsv.exe.
+known_false_positives: Some legitimate printer-related processes may show up as children
+  of spoolsv.exe. You should confirm that any activity as legitimate and may be added
+  as exclusions in the search.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Privilege Escalation
   asset_type: Endpoint
   cis20:
@@ -41,5 +45,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156.yml b/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156.yml
index 74276648f8..42553d4a4b 100644
--- a/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156.yml
+++ b/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156.yml
@@ -1,18 +1,21 @@
-author: Shannon Davis, Splunk
+name: Detect Baron Samedit CVE-2021-3156
+id: 93fbec4e-0375-440c-8db3-4508eca470c4
+version: 1
 date: '2021-01-27'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
 description: This search detects the heap-based buffer overflow of sudoedit
+search: '`linux_hosts` | search "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter`'
 how_to_implement: Splunk Universal Forwarder running on Linux systems, capturing logs
   from the /var/log directory. The vulnerability is exposed when a non privledged
   user tries passing in a single \ character at the end of the command while using
   the shell and edit flags.
-id: 93fbec4e-0375-440c-8db3-4508eca470c4
 known_false_positives: unknown
-name: Detect Baron Samedit CVE-2021-3156
 references:
 - https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit
-search: '`linux_hosts` | search "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Baron Samedit CVE-2021-3156
   asset_type: Endpoint
   cis20:
@@ -30,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml b/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml
index d398bfdbad..523180408b 100644
--- a/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml
+++ b/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml
@@ -1,21 +1,24 @@
-author: Shannon Davis, Splunk
+name: Detect Baron Samedit CVE-2021-3156 Segfault
+id: 10f2bae0-bbe6-4984-808c-37dc1c67980d
+version: 1
 date: '2021-01-29'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
 description: This search detects the heap-based buffer overflow of sudoedit
+search: '`linux_hosts` | search sudoedit segfault | stats count min(_time) as firstTime
+  max(_time) as lastTime by host | search count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter`'
 how_to_implement: Splunk Universal Forwarder running on Linux systems (tested on Centos
   and Ubuntu), where segfaults are being logged.  This also captures instances where
   the exploit has been compiled into a binary. The detection looks for greater than
   5 instances of sudoedit combined with segfault over your search time period on a
   single host
-id: 10f2bae0-bbe6-4984-808c-37dc1c67980d
 known_false_positives: If sudoedit is throwing segfaults for other reasons this will
   pick those up too.
-name: Detect Baron Samedit CVE-2021-3156 Segfault
 references:
 - https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit
-search: '`linux_hosts` | search sudoedit segfault | stats count min(_time) as firstTime
-  max(_time) as lastTime by host | search count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Baron Samedit CVE-2021-3156
   asset_type: Endpoint
   cis20:
@@ -33,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml b/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml
index 096706179a..bdaa20f495 100644
--- a/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml
+++ b/detections/experimental/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml
@@ -1,18 +1,21 @@
-author: Shannon Davis, Splunk
+name: Detect Baron Samedit CVE-2021-3156 via OSQuery
+id: 1de31d5d-8fa6-4ee0-af89-17069134118a
+version: 1
 date: '2021-01-28'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
 description: This search detects the heap-based buffer overflow of sudoedit
+search: '`osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter`'
 how_to_implement: OSQuery installed and configured to pick up process events (info
   at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402.
   The vulnerability is exposed when a non privledged user tries passing in a single
   \ character at the end of the command while using the shell and edit flags.
-id: 1de31d5d-8fa6-4ee0-af89-17069134118a
 known_false_positives: unknown
-name: Detect Baron Samedit CVE-2021-3156 via OSQuery
 references:
 - https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit
-search: '`osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Baron Samedit CVE-2021-3156
   asset_type: Endpoint
   cis20:
@@ -30,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/experimental/endpoint/detect_oulook_exe_writing_a__zip_file.yml b/detections/experimental/endpoint/detect_oulook_exe_writing_a__zip_file.yml
index 050331cf90..2ae04051cd 100644
--- a/detections/experimental/endpoint/detect_oulook_exe_writing_a__zip_file.yml
+++ b/detections/experimental/endpoint/detect_oulook_exe_writing_a__zip_file.yml
@@ -1,15 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Detect Oulook exe writing a  zip file
+id: a51bfe1a-94f0-4822-b1e4-16ae10145893
+version: 3
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for execution of process `outlook.exe` where the process
   is writing a `.zip` file to the disk.
-how_to_implement: You must be ingesting data that records filesystem and process activity
-  from your hosts to populate the Endpoint data model. This is typically populated
-  via endpoint detection-and-response products, such as Carbon Black, or endpoint
-  data sources, such as Sysmon.
-id: a51bfe1a-94f0-4822-b1e4-16ae10145893
-known_false_positives: It is not uncommon for outlook to write legitimate zip files
-  to the disk.
-name: Detect Oulook exe writing a  zip file
 search: '| tstats `security_content_summariesonly`  min(_time) as firstTime max(_time)
   as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe
   OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id
@@ -26,8 +23,15 @@ search: '| tstats `security_content_summariesonly`  min(_time) as firstTime max(
   file_name file_hash count file_id] | table firstTime lastTime user malicious_id
   outlook_id process_name parent_process_name file_name  file_path | where file_name
   != "" | `detect_oulook_exe_writing_a__zip_file_filter` '
+how_to_implement: You must be ingesting data that records filesystem and process activity
+  from your hosts to populate the Endpoint data model. This is typically populated
+  via endpoint detection-and-response product, such as Carbon Black, or endpoint
+  data sources, such as Sysmon.
+known_false_positives: It is not uncommon for outlook to write legitimate zip files
+  to the disk.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Phishing Payloads
   asset_type: Endpoint
   cis20:
@@ -46,5 +50,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/experimental/endpoint/detection_of_tools_built_by_nirsoft.yml b/detections/experimental/endpoint/detection_of_tools_built_by_nirsoft.yml
index 2556bc305b..739c1d5904 100644
--- a/detections/experimental/endpoint/detection_of_tools_built_by_nirsoft.yml
+++ b/detections/experimental/endpoint/detection_of_tools_built_by_nirsoft.yml
@@ -1,24 +1,28 @@
-author: Bhavin Patel, Splunk
+name: Detection of tools built by NirSoft
+id: 1297fb80-f42a-4q4a-9c8b-78c061417cf6
+version: 3
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for specific command-line arguments that may indicate
   the execution of tools made by Nirsoft, which are legitimate, but may be abused
   by attackers.
-how_to_implement: You must be ingesting endpoint data that tracks process activity,
-  including parent-child relationships from your endpoints to populate the Endpoint
-  data model in the Processes node. The command-line arguments are mapped to the "process"
-  field in the Endpoint data model.
-id: 1297fb80-f42a-4q4a-9c8b-78c061417cf6
-known_false_positives: While legitimate, these NirSoft tools are prone to abuse. You
-  should verfiy that the tool was used for a legitimate purpose.
-name: Detection of tools built by NirSoft
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process)
   as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*
   /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name
   Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter`'
+how_to_implement: You must be ingesting endpoint data that tracks process activity,
+  including parent-child relationships from your endpoints to populate the Endpoint
+  data model in the Processes node. The command-line arguments are mapped to the "process"
+  field in the Endpoint data model.
+known_false_positives: While legitimate, these NirSoft tools are prone to abuse. You
+  should verfiy that the tool was used for a legitimate purpose.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   asset_type: Endpoint
   cis20:
@@ -35,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/experimental/endpoint/first_time_seen_running_windows_service.yml b/detections/experimental/endpoint/first_time_seen_running_windows_service.yml
index 187dab7152..4e96c879de 100644
--- a/detections/experimental/endpoint/first_time_seen_running_windows_service.yml
+++ b/detections/experimental/endpoint/first_time_seen_running_windows_service.yml
@@ -1,7 +1,17 @@
-author: David Dorsey, Splunk
+name: First Time Seen Running Windows Service
+id: 823136f2-d755-4b6d-ae04-372b486a5808
+version: 4
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: This search looks for the first and last time a Windows service is seen
   running in your environment. This table is then cached.
+search: '`wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+)
+  service entered the (?<state>\w+) state" | where state="running" | lookup previously_seen_running_windows_services
+  service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen
+  > relative_time(now(), `previously_seen_windows_services_window`) | table _time
+  dest service | `first_time_seen_running_windows_service_filter`'
 how_to_implement: While this search does not require you to adhere to Splunk CIM,
   you must be ingesting your Windows system event logs in order for this search to
   execute successfully. You should run the baseline search `Previously Seen Running
@@ -11,18 +21,11 @@ how_to_implement: While this search does not require you to adhere to Splunk CIM
   keep this table up to date and to age out old Windows Services. Please update the
   `previously_seen_windows_services_window` macro to adjust the time window. Please
   ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
-id: 823136f2-d755-4b6d-ae04-372b486a5808
 known_false_positives: A previously unseen service is not necessarily malicious. Verify
   that the service is legitimate and that was installed by a legitimate process.
-name: First Time Seen Running Windows Service
 references: []
-search: '`wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+)
-  service entered the (?<state>\w+) state" | where state="running" | lookup previously_seen_running_windows_services
-  service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen
-  > relative_time(now(), `previously_seen_windows_services_window`) | table _time
-  dest service | `first_time_seen_running_windows_service_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Windows Service Abuse
   - Orangeworm Attack Group
   - Sunburst Malware
@@ -45,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 4
diff --git a/detections/experimental/endpoint/macos___re_opened_applications.yml b/detections/experimental/endpoint/macos___re_opened_applications.yml
index 08c044f4eb..20dfa8b493 100644
--- a/detections/experimental/endpoint/macos___re_opened_applications.yml
+++ b/detections/experimental/endpoint/macos___re_opened_applications.yml
@@ -1,13 +1,24 @@
-author: Jamie Windley, Splunk
+name: MacOS - Re-opened Applications
+id: 40bb64f9-f619-4e3d-8732-328d40377c4b
+version: 1
 date: '2020-02-07'
+author: Jamie Windley, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for processes referencing the plist files that determine
   which applications are re-opened when a user reboots their machine.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*"
+  by Processes.user Processes.process_name Processes.parent_process_name Processes.dest
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `macos___re_opened_applications_filter`'
 how_to_implement: In order to properly run this search, Splunk needs to ingest process
   data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf)
   pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be
   deployed across your indexers and universal forwarders in order to have the data
   populate the Endpoint data model.
-id: 40bb64f9-f619-4e3d-8732-328d40377c4b
 known_false_positives: At this stage, there are no known false positives. During testing,
   no process events refering the com.apple.loginwindow.plist files were observed during
   normal operation of re-opening applications on reboot. Therefore, it can be asumed
@@ -15,14 +26,7 @@ known_false_positives: At this stage, there are no known false positives. During
   In the event that the legitimate modification by the system of these files is in
   fact logged to the process log, then the process_name of that process can be added
   to an allow list.
-name: MacOS - Re-opened Applications
 references: []
-search: '| tstats `security_content_summariesonly` count values(Processes.process)
-  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
-  max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*"
-  by Processes.user Processes.process_name Processes.parent_process_name Processes.dest
-  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `macos___re_opened_applications_filter`'
 tags:
   asset_type: Endpoint
   cis20:
@@ -38,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/endpoint/processes_tapping_keyboard_events.yml b/detections/experimental/endpoint/processes_tapping_keyboard_events.yml
index 80583693df..d94c514f67 100644
--- a/detections/experimental/endpoint/processes_tapping_keyboard_events.yml
+++ b/detections/experimental/endpoint/processes_tapping_keyboard_events.yml
@@ -1,26 +1,29 @@
-author: Jose Hernandez, Splunk
+name: Processes Tapping Keyboard Events
+id: 2a371608-331d-4034-ae2c-21dda8f1d0ec
+version: 1
 date: '2019-01-25'
+author: Jose Hernandez, Splunk
+type: batch
+datamodel: []
 description: This search looks for processes in an MacOS system that is tapping keyboard
   events in MacOS, and essentially monitoring all keystrokes made by a user. This
   is a common technique used by RATs to log keystrokes from a victim, although it
   can also be used by legitimate processes like Siri to react on human input
+search: '| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps
+  | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id|
+  dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter`'
 how_to_implement: In order to properly run this search, Splunk needs to ingest data
   from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599)
   pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must
   be deployed across your indexers and universal forwarders in order to have the osquery
   data populate the Alerts data model.
-id: 2a371608-331d-4034-ae2c-21dda8f1d0ec
 known_false_positives: There might be some false positives as keyboard event taps
   are used by processes like Siri and Zoom video chat, for some good examples of processes
   to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161)
   comment.
-name: Processes Tapping Keyboard Events
 references: []
-search: '| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps
-  | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id|
-  dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - ColdRoot MacOS RAT
   asset_type: Endpoint
   cis20:
@@ -35,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/experimental/endpoint/remote_desktop_process_running_on_system.yml b/detections/experimental/endpoint/remote_desktop_process_running_on_system.yml
index f65bb3e968..c00b9a83c9 100644
--- a/detections/experimental/endpoint/remote_desktop_process_running_on_system.yml
+++ b/detections/experimental/endpoint/remote_desktop_process_running_on_system.yml
@@ -1,9 +1,20 @@
-author: David Dorsey, Splunk
+name: Remote Desktop Process Running On System
+id: f5939373-8054-40ad-8c64-cec478a22a4a
+version: 5
 date: '2020-07-21'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Endpoint
 description: This search looks for the remote desktop process mstsc.exe running on
   systems upon which it doesn't typically run. This is accomplished by filtering out
   all systems that are noted in the `common_rdp_source category` in the Assets and
   Identity framework.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe
+  AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user
+  Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` '
 how_to_implement: To successfully implement this search, you must be ingesting data
   that records process activity from your hosts to populate the endpoint data model
   in the processes node. The search requires you to identify systems that do not commonly
@@ -12,17 +23,10 @@ how_to_implement: To successfully implement this search, you must be ingesting d
   to add the "common_rdp_source" category to that system using the Enterprise Security
   Assets and Identities framework. This can be done by adding an entry in the assets.csv
   file located in `SA-IdentityManagement/lookups`.
-id: f5939373-8054-40ad-8c64-cec478a22a4a
 known_false_positives: Remote Desktop may be used legitimately by users on the network.
-name: Remote Desktop Process Running On System
 references: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe
-  AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user
-  Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - Hidden Cobra Malware
   - Lateral Movement
   asset_type: Endpoint
@@ -43,5 +47,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 5
diff --git a/detections/experimental/endpoint/spike_in_file_writes.yml b/detections/experimental/endpoint/spike_in_file_writes.yml
index 1aa9748a46..5a61580b9d 100644
--- a/detections/experimental/endpoint/spike_in_file_writes.yml
+++ b/detections/experimental/endpoint/spike_in_file_writes.yml
@@ -1,18 +1,12 @@
-author: David Dorsey, Splunk
+name: Spike in File Writes
+id: fdb0f805-74e4-4539-8c00-618927333aae
+version: 3
 date: '2020-03-16'
+author: David Dorsey, Splunk
+type: batch
+datamodel: []
 description: The search looks for a sharp increase in the number of files written
   to a particular host
-how_to_implement: In order to implement this search, you must populate the Endpoint
-  file-system data model node. This is typically populated via endpoint detection
-  and response products, such as Carbon Black or endpoint data sources such as Sysmon.
-  The data used for this search is typically generated via logs that report reads
-  and writes to the file system.
-id: fdb0f805-74e4-4539-8c00-618927333aae
-known_false_positives: It is important to understand that if you happen to install
-  any new applications on your hosts or are copying a large number of files, you can
-  expect to see a large increase of file modifications.
-name: Spike in File Writes
-references: []
 search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem
   where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)`
   | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time
@@ -20,8 +14,17 @@ search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint
   "-1d@d"), count,null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-1d@d"),
   count, null))) as stdev by "dest" | eval upperBound=(avg+stdev*4), isOutlier=if((count
   > upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` '
+how_to_implement: In order to implement this search, you must populate the Endpoint
+  file-system data model node. This is typically populated via endpoint detection
+  and response product, such as Carbon Black or endpoint data sources such as Sysmon.
+  The data used for this search is typically generated via logs that report reads
+  and writes to the file system.
+known_false_positives: It is important to understand that if you happen to install
+  any new applications on your hosts or are copying a large number of files, you can
+  expect to see a large increase of file modifications.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   - Ryuk Ransomware
   - Ransomware
@@ -37,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 3
diff --git a/detections/experimental/endpoint/sunburst_correlation_dll_and_network_event.yml b/detections/experimental/endpoint/sunburst_correlation_dll_and_network_event.yml
index af8f59b9f9..fac0c15d6f 100644
--- a/detections/experimental/endpoint/sunburst_correlation_dll_and_network_event.yml
+++ b/detections/experimental/endpoint/sunburst_correlation_dll_and_network_event.yml
@@ -1,25 +1,28 @@
-author: Patrick Bareiss, Splunk
+name: Sunburst Correlation DLL and Network Event
+id: 701a8740-e8db-40df-9190-5516d3819787
+version: 1
 date: '2020-12-14'
+author: Patrick Bareiss, Splunk
+type: batch
+datamodel: []
 description: The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe.
   After a period of 12-14 days, the malware will attempt to resolve a subdomain of
   avsvmcloud.com. This detections will correlate both events.
-how_to_implement: This detection relies on sysmon logs with the Event ID 7, Driver
-  loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll
-  is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query.
-  We suggest to run this detection at least once a day over the last 14 days.
-id: 701a8740-e8db-40df-9190-5516d3819787
-known_false_positives: unknown
-name: Sunburst Correlation DLL and Network Event
-references:
-- https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html
 search: '(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll)
   OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode)
   AS dc_events | where dc_events=2 | stats min(_time) as firstTime max(_time) as lastTime
   values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host | rename
   host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | `sunburst_correlation_dll_and_network_event_filter` '
+how_to_implement: This detection relies on sysmon logs with the Event ID 7, Driver
+  loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll
+  is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query.
+  We suggest to run this detection at least once a day over the last 14 days.
+known_false_positives: unknown
+references:
+- https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html
 tags:
-  analytics_story:
+  analytic_story:
   - Sunburst Malware
   asset_type: Windows
   cis20:
@@ -36,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/experimental/endpoint/wmi_permanent_event_subscription.yml b/detections/experimental/endpoint/wmi_permanent_event_subscription.yml
index 81560747a6..a35567f023 100644
--- a/detections/experimental/endpoint/wmi_permanent_event_subscription.yml
+++ b/detections/experimental/endpoint/wmi_permanent_event_subscription.yml
@@ -1,21 +1,24 @@
-author: Rico Valdez, Splunk
-date: '2018-10-23'
-description: This search looks for the creation of WMI permanent event subscriptions.
-how_to_implement: To successfully implement this search, you must be ingesting the
-  Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on
-  the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
-id: 71bfdb13-f200-4c6c-b2c9-a2e07adf437d
-known_false_positives: Although unlikely, administrators may use event subscriptions
-  for legitimate purposes.
 name: WMI Permanent Event Subscription
-references: []
+id: 71bfdb13-f200-4c6c-b2c9-a2e07adf437d
+version: 1
+date: '2018-10-23'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
+description: This search looks for the creation of WMI permanent event subscriptions.
 search: '`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?<consumer>[^;|^$]+)"
   | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats
   count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer,
   Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
   | rename ComputerName as dest | `wmi_permanent_event_subscription_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting the
+  Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on
+  the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
+known_false_positives: Although unlikely, administrators may use event subscriptions
+  for legitimate purposes.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious WMI Use
   asset_type: Endpoint
   cis20:
@@ -35,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/experimental/endpoint/wmi_temporary_event_subscription.yml b/detections/experimental/endpoint/wmi_temporary_event_subscription.yml
index 74ad250522..6ba11f0709 100644
--- a/detections/experimental/endpoint/wmi_temporary_event_subscription.yml
+++ b/detections/experimental/endpoint/wmi_temporary_event_subscription.yml
@@ -1,24 +1,27 @@
-author: Rico Valdez, Splunk
-date: '2018-10-23'
-description: This search looks for the creation of WMI temporary event subscriptions.
-how_to_implement: To successfully implement this search, you must be ingesting the
-  Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on
-  the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
-id: 38cbd42c-1098-41bb-99cf-9d6d2b296d83
-known_false_positives: Some software may create WMI temporary event subscriptions
-  for various purposes. The included search contains an exception for two of these
-  that occur by default on Windows 10 systems. You may need to modify the search to
-  create exceptions for other legitimate events.
 name: WMI Temporary Event Subscription
-references: []
+id: 38cbd42c-1098-41bb-99cf-9d6d2b296d83
+version: 1
+date: '2018-10-23'
+author: Rico Valdez, Splunk
+type: batch
+datamodel: []
+description: This search looks for the creation of WMI temporary event subscriptions.
 search: '`wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?<query>[^;|^$]+)"
   | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = ''wsmprovhost.exe''"
   AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA ''AntiVirusProduct''
   OR TargetInstance ISA ''FirewallProduct'' OR TargetInstance ISA ''AntiSpywareProduct''"
   | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query  |
   `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting the
+  Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on
+  the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
+known_false_positives: Some software may create WMI temporary event subscriptions
+  for various purposes. The included search contains an exception for two of these
+  that occur by default on Windows 10 systems. You may need to modify the search to
+  create exceptions for other legitimate events.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious WMI Use
   asset_type: Endpoint
   cis20:
@@ -38,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/experimental/network/detect_arp_poisoning.yml b/detections/experimental/network/detect_arp_poisoning.yml
index ba27d6d7a0..0be208ae0e 100644
--- a/detections/experimental/network/detect_arp_poisoning.yml
+++ b/detections/experimental/network/detect_arp_poisoning.yml
@@ -1,26 +1,29 @@
-author: Mikael Bjerkeland, Splunk
+name: Detect ARP Poisoning
+id: b44bebd6-bd39-467b-9321-73971bcd7aac
+version: 1
 date: '2020-08-11'
+author: Mikael Bjerkeland, Splunk
+type: batch
+datamodel: []
 description: By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the
   organization's network devices, we will be able to detect ARP Poisoning attacks
   in the Infrastructure.
+search: '`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection"
+  | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime
+  max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`|
+  `detect_arp_poisoning_filter`'
 how_to_implement: This search uses a standard SPL query on logs from Cisco Network
   devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html)
   and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html)
   and log with a severity level of minimum "5 - notification". The search also requires
   that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
   is used to parse the logs from the Cisco network devices.
-id: b44bebd6-bd39-467b-9321-73971bcd7aac
 known_false_positives: This search might be prone to high false positives if DHCP
   Snooping or ARP inspection has been incorrectly configured, or if a device normally
   sends many ARP packets (unlikely).
-name: Detect ARP Poisoning
 references: []
-search: '`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection"
-  | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime
-  max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`|
-  `detect_arp_poisoning_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Router and Infrastructure Security
   asset_type: Infrastructure
   cis20:
@@ -43,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/experimental/network/dns_record_changed.yml b/detections/experimental/network/dns_record_changed.yml
index f337f79a77..4815f3310d 100644
--- a/detections/experimental/network/dns_record_changed.yml
+++ b/detections/experimental/network/dns_record_changed.yml
@@ -1,8 +1,22 @@
-author: Jose Hernandez, Splunk
+name: DNS record changed
+id: 44d3a43e-dcd5-49f7-8356-5209bb369065
+version: 3
 date: '2020-07-21'
+author: Jose Hernandez, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: The search takes the DNS records and their answers results of the discovered_dns_records
   lookup and finds if any records have changed by searching DNS response from the
   Network_Resolution datamodel across the last day.
+search: '| inputlookup discovered_dns_records | rename answer as discovered_answer
+  | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type)
+  as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution
+  where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query
+  | rename DNS.query as query | where query!="unknown" | rex field=query "(?<domain>\w+\.\w+?)(?:$|/)"]
+  | makemv delim=" " answer |  makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer
+  | makemv current_answer  | mvexpand current_answer | makemv discovered_answer |
+  eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter`'
 how_to_implement: "To successfully implement this search you will need to ensure that\
   \ DNS data is populating the `Network_Resolution` data model. It also requires that\
   \ the `discover_dns_record` lookup table be populated by the included support search\
@@ -16,21 +30,12 @@ how_to_implement: "To successfully implement this search you will need to ensure
   \ when configuring this detection search, and set the corresponding Playbook to\
   \ active. \\\n(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\\\
   \n"
-id: 44d3a43e-dcd5-49f7-8356-5209bb369065
 known_false_positives: Legitimate DNS changes can be detected in this search. Investigate,
   verify and update the list of provided current answers for the domains in question
   as appropriate.
-name: DNS record changed
-search: '| inputlookup discovered_dns_records | rename answer as discovered_answer
-  | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type)
-  as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution
-  where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query
-  | rename DNS.query as query | where query!="unknown" | rex field=query "(?<domain>\w+\.\w+?)(?:$|/)"]
-  | makemv delim=" " answer |  makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer
-  | makemv current_answer  | mvexpand current_answer | makemv discovered_answer |
-  eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter`'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DNS Hijacking
   asset_type: Endpoint
   cis20:
@@ -53,5 +58,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/experimental/network/excessive_dns_failures.yml b/detections/experimental/network/excessive_dns_failures.yml
index 3c66362868..b000a4f436 100644
--- a/detections/experimental/network/excessive_dns_failures.yml
+++ b/detections/experimental/network/excessive_dns_failures.yml
@@ -1,15 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Excessive DNS Failures
+id: 104658f4-afdc-499e-9719-17243f9826f1
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search identifies DNS query failures by counting the number of DNS
   responses that do not indicate success, and trigger on more than 50 occurrences.
-how_to_implement: To successfully implement this search you must ensure that DNS data
-  is populating the Network_Resolution data model.
-id: 104658f4-afdc-499e-9719-17243f9826f1
-known_false_positives: It is possible legitimate traffic can trigger this rule. Please
-  investigate as appropriate. The threshold for generating an event can also be customized
-  to better suit your environment.
-name: Excessive DNS Failures
-references: []
 search: '| tstats `security_content_summariesonly` count values("DNS.query") as queries
   from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error"
   "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*"
@@ -17,8 +15,14 @@ search: '| tstats `security_content_summariesonly` count values("DNS.query") as
   domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str
   domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries)
   as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter`'
+how_to_implement: To successfully implement this search you must ensure that DNS data
+  is populating the Network_Resolution data model.
+known_false_positives: It is possible legitimate traffic can trigger this rule. Please
+  investigate as appropriate. The threshold for generating an event can also be customized
+  to better suit your environment.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious DNS Traffic
   - Command and Control
   asset_type: Endpoint
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml b/detections/experimental/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml
index 20a8d610bc..3f274315f2 100644
--- a/detections/experimental/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml
+++ b/detections/experimental/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml
@@ -1,21 +1,14 @@
-author: Bhavin Patel, Splunk
+name: Hosts receiving high volume of network traffic from email server
+id: 7f5fb3e1-4209-4914-90db-0ec21b556368
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for an increase of data transfers from your email server
   to your clients. This could be indicative of a malicious actor collecting data using
   your email server.
-how_to_implement: This search requires you to be ingesting your network traffic and
-  populating the Network_Traffic data model.  Your email servers must be categorized
-  as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold
-  and minimum_data_samples values based on the network traffic in your environment.
-  The "deviation_threshold" field is a multiplying factor to control how much variation
-  you're willing to tolerate. The "minimum_data_samples" field is the minimum number
-  of connections of data samples required for the statistic to be valid.
-id: 7f5fb3e1-4209-4914-90db-0ec21b556368
-known_false_positives: The false-positive rate will vary based on how you set the
-  deviation_threshold and data_samples values. Our recommendation is to adjust these
-  values based on your network traffic to and from your email servers.
-name: Hosts receiving high volume of network traffic from email server
-references: []
 search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in
   from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip
   _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in)
@@ -30,8 +23,19 @@ search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as
   = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2)
   | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average,
   num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`'
+how_to_implement: This search requires you to be ingesting your network traffic and
+  populating the Network_Traffic data model.  Your email servers must be categorized
+  as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold
+  and minimum_data_samples values based on the network traffic in your environment.
+  The "deviation_threshold" field is a multiplying factor to control how much variation
+  you're willing to tolerate. The "minimum_data_samples" field is the minimum number
+  of connections of data samples required for the statistic to be valid.
+known_false_positives: The false-positive rate will vary based on how you set the
+  deviation_threshold and data_samples values. Our recommendation is to adjust these
+  values based on your network traffic to and from your email servers.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Collection and Staging
   asset_type: Endpoint
   cis20:
@@ -49,5 +53,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/network/large_volume_of_dns_any_queries.yml b/detections/experimental/network/large_volume_of_dns_any_queries.yml
index 48b49585df..2cd258a8fe 100644
--- a/detections/experimental/network/large_volume_of_dns_any_queries.yml
+++ b/detections/experimental/network/large_volume_of_dns_any_queries.yml
@@ -1,20 +1,24 @@
-author: Bhavin Patel, Splunk
+name: Large Volume of DNS ANY Queries
+id: 8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb
+version: 1
 date: '2017-09-20'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: The search is used to identify attempts to use your DNS Infrastructure
   for DDoS purposes via a DNS amplification attack leveraging ANY queries.
-how_to_implement: To successfully implement this search you must ensure that DNS data
-  is populating the Network_Resolution data model.
-id: 8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb
-known_false_positives: Legitimate ANY requests may trigger this search, however it
-  is unusual to see a large volume of them under typical circumstances. You may modify
-  the threshold in the search to better suit your environment.
-name: Large Volume of DNS ANY Queries
-references: []
 search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
   where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest"
   | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter`'
+how_to_implement: To successfully implement this search you must ensure that DNS data
+  is populating the Network_Resolution data model.
+known_false_positives: Legitimate ANY requests may trigger this search, however it
+  is unusual to see a large volume of them under typical circumstances. You may modify
+  the threshold in the search to better suit your environment.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - DNS Amplification Attacks
   asset_type: DNS Servers
   cis20:
@@ -33,5 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/experimental/network/prohibited_network_traffic_allowed.yml b/detections/experimental/network/prohibited_network_traffic_allowed.yml
index b8da99abd4..cae48f8216 100644
--- a/detections/experimental/network/prohibited_network_traffic_allowed.yml
+++ b/detections/experimental/network/prohibited_network_traffic_allowed.yml
@@ -1,26 +1,30 @@
-author: Rico Valdez, Splunk
+name: Prohibited Network Traffic Allowed
+id: ce5a0962-849f-4720-a678-753fe6674479
+version: 2
 date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for network traffic defined by port and transport layer
   protocol in the Enterprise Security lookup table "lookup_interesting_ports", that
   is marked as prohibited, and has an associated 'allow' action in the Network_Traffic
   data model. This could be indicative of a misconfigured network device.
-how_to_implement: In order to properly run this search, Splunk needs to ingest data
-  from firewalls or other network control devices that mediate the traffic allowed
-  into an environment. This is necessary so that the search can identify an 'action'
-  taken on the traffic of interest. The search requires the Network_Traffic data model
-  be populated.
-id: ce5a0962-849f-4720-a678-753fe6674479
-known_false_positives: None identified
-name: Prohibited Network Traffic Allowed
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by
   All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action
   | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port
   OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter`'
+how_to_implement: In order to properly run this search, Splunk needs to ingest data
+  from firewalls or other network control devices that mediate the traffic allowed
+  into an environment. This is necessary so that the search can identify an 'action'
+  taken on the traffic of interest. The search requires the Network_Traffic data model
+  be populated.
+known_false_positives: None identified
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Prohibited Traffic Allowed or Protocol Mismatch
   - Ransomware
   - Command and Control
@@ -41,5 +45,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/network/protocol_or_port_mismatch.yml b/detections/experimental/network/protocol_or_port_mismatch.yml
index 99933ce2ca..2b8ef78736 100644
--- a/detections/experimental/network/protocol_or_port_mismatch.yml
+++ b/detections/experimental/network/protocol_or_port_mismatch.yml
@@ -1,19 +1,17 @@
-author: Rico Valdez, Splunk
+name: Protocol or Port Mismatch
+id: 54dc1265-2f74-4b6d-b30d-49eb506a31b3
+version: 2
 date: '2020-07-21'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for network traffic on common ports where a higher
   layer protocol does not match the port that is being used. For example, this search
   should identify cases where protocols other than HTTP are running on TCP port 80.
   This can be used by attackers to circumvent firewall restrictions, or as an attempt
   to hide malicious communications over ports and protocols that are typically allowed
   and not well inspected.
-how_to_implement: Running this search properly requires a technology that can inspect
-  network traffic and identify common protocols. Technologies such as Bro and Palo
-  Alto Networks firewalls are two examples that will identify protocols via inspection,
-  and not just assume a specific protocol based on the transport protocol and ports.
-id: 54dc1265-2f74-4b6d-b30d-49eb506a31b3
-known_false_positives: None identified
-name: Protocol or Port Mismatch
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53)
   OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80
@@ -22,8 +20,14 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app,
   All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter`'
+how_to_implement: Running this search properly requires a technology that can inspect
+  network traffic and identify common protocols. Technologies such as Bro and Palo
+  Alto Networks firewalls are two examples that will identify protocols via inspection,
+  and not just assume a specific protocol based on the transport protocol and ports.
+known_false_positives: None identified
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Prohibited Traffic Allowed or Protocol Mismatch
   - Command and Control
   asset_type: Endpoint
@@ -42,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/network/protocols_passing_authentication_in_cleartext.yml b/detections/experimental/network/protocols_passing_authentication_in_cleartext.yml
index 7d6990b200..5c54766ef7 100644
--- a/detections/experimental/network/protocols_passing_authentication_in_cleartext.yml
+++ b/detections/experimental/network/protocols_passing_authentication_in_cleartext.yml
@@ -1,24 +1,28 @@
-author: Rico Valdez, Splunk
+name: Protocols passing authentication in cleartext
+id: 6923cd64-17a0-453c-b945-81ac2d8c6db9
+version: 2
 date: '2020-11-04'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for cleartext protocols at risk of leaking credentials.
   Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous
   FTP sessions. While some of these protocols can be used over SSL, they typically
   run on different assigned ports in those cases.
-how_to_implement: This search requires you to be ingesting your network traffic, and
-  populating the Network_Traffic data model.
-id: 6923cd64-17a0-453c-b945-81ac2d8c6db9
-known_false_positives: Some networks may use kerberized FTP or telnet servers, however,
-  this is rare.
-name: Protocols passing authentication in cleartext
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND
   (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110"
   OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user
   All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter`'
+how_to_implement: This search requires you to be ingesting your network traffic, and
+  populating the Network_Traffic data model.
+known_false_positives: Some networks may use kerberized FTP or telnet servers, however,
+  this is rare.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Use of Cleartext Protocols
   asset_type: Endpoint
   cis20:
@@ -37,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml b/detections/experimental/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml
index 61bd92bd97..3c334a6a2e 100644
--- a/detections/experimental/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml
+++ b/detections/experimental/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml
@@ -1,22 +1,26 @@
-author: Bhavin Patel, Splunk
+name: Detect attackers scanning for vulnerable JBoss servers
+id: 104658f4-afdc-499e-9719-17243f982681
+version: 1
 date: '2017-09-23'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Web
 description: This search looks for specific GET or HEAD requests to web servers that
   are indicative of reconnaissance attempts to identify vulnerable JBoss servers.
   JexBoss is described as the exploit tool of choice for this malicious activity.
-how_to_implement: You must be ingesting data from the web server or network traffic
-  that contains web specific information, and populating the Web data model.
-id: 104658f4-afdc-499e-9719-17243f982681
-known_false_positives: It's possible for legitimate HTTP requests to be made to URLs
-  containing the suspicious paths.
-name: Detect attackers scanning for vulnerable JBoss servers
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD")
   AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*"
   OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")`
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter`'
+how_to_implement: You must be ingesting data from the web server or network traffic
+  that contains web specific information, and populating the Web data model.
+known_false_positives: It's possible for legitimate HTTP requests to be made to URLs
+  containing the suspicious paths.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - JBoss Vulnerability
   - SamSam Ransomware
   asset_type: Web Server
@@ -29,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/experimental/web/detect_f5_tmui_rct_cve_2020_5902.yml b/detections/experimental/web/detect_f5_tmui_rct_cve_2020_5902.yml
index e1de4ebc6d..f93c6322cc 100644
--- a/detections/experimental/web/detect_f5_tmui_rct_cve_2020_5902.yml
+++ b/detections/experimental/web/detect_f5_tmui_rct_cve_2020_5902.yml
@@ -1,7 +1,13 @@
-author: Shannon Davis, Splunk
+name: Detect F5 TMUI RCE CVE-2020-5902
+id: 810e4dbc-d46e-11ea-87d0-0242ac130003
+version: 1
 date: '2020-08-02'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
 description: This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ,
   and Traffix SDC devices
+search: '`f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter`'
 how_to_implement: To consistently detect exploit attempts on F5 devices using the
   vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs
   via syslog.  As many BIG-IP devices will have SSL enabled on their management interfaces,
@@ -9,16 +15,13 @@ how_to_implement: To consistently detect exploit attempts on F5 devices using th
   traffic in order to inspect it.  I am using a regex string from a Cloudflare mitigation
   technique to try and always catch the offending string (..;), along with the other
   exploit of using (hsqldb;).
-id: 810e4dbc-d46e-11ea-87d0-0242ac130003
 known_false_positives: unknown
-name: Detect F5 TMUI RCE CVE-2020-5902
 references:
 - https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/
 - https://support.f5.com/csp/article/K52145254
 - https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/
-search: '`f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - F5 TMUI RCE CVE-2020-5902
   asset_type: Network
   cis20:
@@ -35,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/experimental/web/detect_malicious_requests_to_exploit_jboss_servers.yml b/detections/experimental/web/detect_malicious_requests_to_exploit_jboss_servers.yml
index 788e7450ed..71bd1b6b3c 100644
--- a/detections/experimental/web/detect_malicious_requests_to_exploit_jboss_servers.yml
+++ b/detections/experimental/web/detect_malicious_requests_to_exploit_jboss_servers.yml
@@ -1,23 +1,27 @@
-author: Bhavin Patel, Splunk
+name: Detect malicious requests to exploit JBoss servers
+id: c8bff7a4-11ea-4416-a27d-c5bca472913d
+version: 1
 date: '2017-09-23'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Web
 description: This search is used to detect malicious HTTP requests crafted to exploit
   jmx-console in JBoss servers. The malicious requests have a long URL length, as
   the payload is embedded in the URL.
-how_to_implement: You must ingest data from the web server or capture network data
-  that contains web specific information with solutions such as Bro or Splunk Stream,
-  and populating the Web data model
-id: c8bff7a4-11ea-4416-a27d-c5bca472913d
-known_false_positives: No known false positives for this detection.
-name: Detect malicious requests to exploit JBoss servers
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD")
   by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*"
   AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime,
   lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter`'
+how_to_implement: You must ingest data from the web server or capture network data
+  that contains web specific information with solutions such as Bro or Splunk Stream,
+  and populating the Web data model
+known_false_positives: No known false positives for this detection.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - JBoss Vulnerability
   - SamSam Ransomware
   asset_type: Web Server
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/experimental/web/monitor_web_traffic_for_brand_abuse.yml b/detections/experimental/web/monitor_web_traffic_for_brand_abuse.yml
index d0d79d2d22..d00edff771 100644
--- a/detections/experimental/web/monitor_web_traffic_for_brand_abuse.yml
+++ b/detections/experimental/web/monitor_web_traffic_for_brand_abuse.yml
@@ -1,20 +1,24 @@
-author: David Dorsey, Splunk
+name: Monitor Web Traffic For Brand Abuse
+id: 134da869-e264-4a8f-8d7e-fcd0ec88f301
+version: 1
 date: '2017-09-23'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Web
 description: This search looks for Web requests to faux domains similar to the one
   that you want to have monitored for abuse.
+search: '| tstats `security_content_summariesonly` values(Web.url) as urls min(_time)
+  as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter`'
 how_to_implement: You need to ingest data from your web traffic. This can be accomplished
   by indexing data from a web proxy, or using a network traffic analysis tool, such
   as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain
   Names", which creates the permutations of the domain that will be checked for.
-id: 134da869-e264-4a8f-8d7e-fcd0ec88f301
 known_false_positives: None at this time
-name: Monitor Web Traffic For Brand Abuse
 references: []
-search: '| tstats `security_content_summariesonly` values(Web.url) as urls min(_time)
-  as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
-  | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Brand Monitoring
   asset_type: Endpoint
   cis20:
@@ -28,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/experimental/web/sql_injection_with_long_urls.yml b/detections/experimental/web/sql_injection_with_long_urls.yml
index 9cd24d64bb..7007b3a0a9 100644
--- a/detections/experimental/web/sql_injection_with_long_urls.yml
+++ b/detections/experimental/web/sql_injection_with_long_urls.yml
@@ -1,17 +1,13 @@
-author: Bhavin Patel, Splunk
+name: SQL Injection with Long URLs
+id: e0aad4cf-0790-423b-8328-7564d0d938f9
+version: 2
 date: '2020-07-21'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Web
 description: This search looks for long URLs that have several SQL commands visible
   within them.
-how_to_implement: To successfully implement this search, you need to be monitoring
-  network communications to your web servers or ingesting your HTTP logs and populating
-  the Web data model. You must also identify your web servers in the Enterprise Security
-  assets table.
-id: e0aad4cf-0790-423b-8328-7564d0d938f9
-known_false_positives: It's possible that legitimate traffic will have long URLs or
-  long user agent strings and that common SQL commands may be found within the URL.
-  Please investigate as appropriate.
-name: SQL Injection with Long URLs
-references: []
 search: '| tstats `security_content_summariesonly` count from datamodel=Web where
   Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length
   > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")`
@@ -26,8 +22,16 @@ search: '| tstats `security_content_summariesonly` count from datamodel=Web wher
   "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct"))
   + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url,
   "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter`'
+how_to_implement: To successfully implement this search, you need to be monitoring
+  network communications to your web servers or ingesting your HTTP logs and populating
+  the Web data model. You must also identify your web servers in the Enterprise Security
+  assets table.
+known_false_positives: It's possible that legitimate traffic will have long URLs or
+  long user agent strings and that common SQL commands may be found within the URL.
+  Please investigate as appropriate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - SQL Injection
   asset_type: Database Server
   cis20:
@@ -49,5 +53,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/experimental/web/supernova_webshell.yml b/detections/experimental/web/supernova_webshell.yml
index 34e844a759..fc8f34a6dd 100644
--- a/detections/experimental/web/supernova_webshell.yml
+++ b/detections/experimental/web/supernova_webshell.yml
@@ -1,24 +1,28 @@
-author: John Stoner, Splunk
+name: Supernova Webshell
+id: 2ec08a09-9ff1-4dac-b59f-1efd57972ec1
+version: 1
 date: '2021-01-06'
+author: John Stoner, Splunk
+type: batch
+datamodel:
+- Web
 description: This search aims to detect the Supernova webshell used in the SUNBURST
   attack.
-how_to_implement: To successfully implement this search, you need to be monitoring
-  web traffic to your Solarwinds Orion. The logs should be ingested into splunk and
-  populating/mapped to the Web data model.
-id: 2ec08a09-9ff1-4dac-b59f-1efd57972ec1
-known_false_positives: There might be false positives associted with this detection
-  since items like args as a web argument is pretty generic.
-name: Supernova Webshell
-references:
-- https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html
-- https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/
 search: '| tstats `security_content_summariesonly` count from datamodel=Web.Web where
   web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR
   Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by
   Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s
   | `supernova_webshell_filter`'
+how_to_implement: To successfully implement this search, you need to be monitoring
+  web traffic to your Solarwinds Orion. The logs should be ingested into splunk and
+  populating/mapped to the Web data model.
+known_false_positives: There might be false positives associted with this detection
+  since items like args as a web argument is pretty generic.
+references:
+- https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html
+- https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/
 tags:
-  analytics_story:
+  analytic_story:
   - Sunburst Malware
   cis20:
   - CIS 4
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml b/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml
index ecd10391b8..324507d7ff 100644
--- a/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml
+++ b/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml
@@ -1,11 +1,20 @@
-author: Bhavin Patel, Splunk
+name: Detect hosts connecting to dynamic domain providers
+id: c77162d3-f93c-45cc-80c8-22f6v5464g9f
+version: 3
 date: '2021-01-14'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: Malicious actors often abuse legitimate Dynamic DNS services to host
   malicious payloads or interactive command and control nodes. Attackers will automate
   domain resolution changes by routing dynamic domains to countless IP addresses to
   circumvent firewall blocks, block lists as well as frustrate a network defenders
   analytic and investigative processes. This search will look for DNS queries made
   from within your infrastructure to suspicious dynamic domains.
+search: '| tstats `security_content_summariesonly` count values(DNS.answer) as answer
+  min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")`
+  | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter`'
 how_to_implement: 'First, you''ll need to ingest data from your DNS operations. This
   can be done by ingesting logs from your server or data, collected passively by Splunk
   Stream or a similar solution. Specifically, data that contains the domain that is
@@ -31,16 +40,12 @@ how_to_implement: 'First, you''ll need to ingest data from your DNS operations.
 
   Detailed documentation on how to create a new field within Incident Review may be
   found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-id: c77162d3-f93c-45cc-80c8-22f6v5464g9f
 known_false_positives: Some users and applications may leverage Dynamic DNS to reach
   out to some domains on the Internet since dynamic DNS by itself is not malicious,
   however this activity must be verified.
-name: Detect hosts connecting to dynamic domain providers
-search: '| tstats `security_content_summariesonly` count values(DNS.answer) as answer
-  min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")`
-  | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter`'
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Data Protection
   - Prohibited Traffic Allowed or Protocol Mismatch
   - DNS Hijacking
@@ -70,5 +75,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/network/detect_ipv6_network_infrastructure_threats.yml b/detections/network/detect_ipv6_network_infrastructure_threats.yml
index 6365a452d6..39240a442e 100644
--- a/detections/network/detect_ipv6_network_infrastructure_threats.yml
+++ b/detections/network/detect_ipv6_network_infrastructure_threats.yml
@@ -1,26 +1,13 @@
-author: Mikael Bjerkeland, Splunk
+name: Detect IPv6 Network Infrastructure Threats
+id: c3be767e-7959-44c5-8976-0e9c12a91ad2
+version: 1
 date: '2020-10-28'
+author: Mikael Bjerkeland, Splunk
+type: batch
+datamodel: []
 description: By enabling IPv6 First Hop Security as a Layer 2 Security measure on
   the organization's network devices, we will be able to detect various attacks such
   as packet forging in the Infrastructure.
-how_to_implement: This search uses a standard SPL query on logs from Cisco Network
-  devices. The network devices must be configured with one or more First Hop Security
-  measures such as RA Guard, DHCP Guard and/or device tracking. See References for
-  more information. The search also requires that the Cisco Networks Add-on for Splunk
-  (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco
-  network devices.
-id: c3be767e-7959-44c5-8976-0e9c12a91ad2
-known_false_positives: None currently known
-name: Detect IPv6 Network Infrastructure Threats
-references:
-- https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf
-- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html
-- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html
-- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html
-- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html
-- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html
-- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html
-- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html
 search: '`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP")
   | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix
   | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac
@@ -30,8 +17,24 @@ search: '`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","M
   src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation
   action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`
   | `detect_ipv6_network_infrastructure_threats_filter`'
+how_to_implement: This search uses a standard SPL query on logs from Cisco Network
+  devices. The network devices must be configured with one or more First Hop Security
+  measures such as RA Guard, DHCP Guard and/or device tracking. See References for
+  more information. The search also requires that the Cisco Networks Add-on for Splunk
+  (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco
+  network devices.
+known_false_positives: None currently known
+references:
+- https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf
+- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html
+- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html
+- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html
+- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html
+- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html
+- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html
+- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html
 tags:
-  analytics_story:
+  analytic_story:
   - Router and Infrastructure Security
   asset_type: Infrastructure
   cis20:
@@ -54,5 +57,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_large_outbound_icmp_packets.yml b/detections/network/detect_large_outbound_icmp_packets.yml
index e8863b85db..5d1824035c 100644
--- a/detections/network/detect_large_outbound_icmp_packets.yml
+++ b/detections/network/detect_large_outbound_icmp_packets.yml
@@ -1,9 +1,22 @@
-author: Rico Valdez, Splunk
+name: Detect Large Outbound ICMP Packets
+id: e9c102de-4d43-42a7-b1c8-8062ea297419
+version: 2
 date: '2018-06-01'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for outbound ICMP packets with a packet size larger
   than 1,000 bytes. Various threat actors have been known to use ICMP as a command
   and control channel for their attack infrastructure. Large ICMP packets from an
   endpoint to a remote host may be indicative of this activity.
+search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime
+  latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from
+  datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category
+  !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes
+  > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")`
+  | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)
+  | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter`'
 how_to_implement: 'In order to run this search effectively, we highly recommend that
   you leverage the Assets and Identity framework. It is important that you have a
   good understanding of how your network segments are designed and that you are able
@@ -13,7 +26,6 @@ how_to_implement: 'In order to run this search effectively, we highly recommend
   More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata.
   This search also requires you to be ingesting your network traffic and populating
   the Network_Traffic data model'
-id: e9c102de-4d43-42a7-b1c8-8062ea297419
 known_false_positives: ICMP packets are used in a variety of ways to help troubleshoot
   networking issues and ensure the proper flow of traffic. As such, it is possible
   that a large ICMP packet could be perfectly legitimate. If large ICMP packets are
@@ -21,17 +33,9 @@ known_false_positives: ICMP packets are used in a variety of ways to help troubl
   of these packets observed over time. If the search is providing a large number of
   false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter`
   to adjust the byte threshold or add specific IP addresses to an allow list.
-name: Detect Large Outbound ICMP Packets
 references: []
-search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime
-  latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from
-  datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category
-  !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes
-  > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")`
-  | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)
-  | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Command and Control
   asset_type: Endpoint
   cis20:
@@ -48,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/network/detect_outbound_smb_traffic.yml b/detections/network/detect_outbound_smb_traffic.yml
index bd71087b74..f501f5e6ab 100644
--- a/detections/network/detect_outbound_smb_traffic.yml
+++ b/detections/network/detect_outbound_smb_traffic.yml
@@ -1,29 +1,16 @@
-author: Bhavin Patel, Stuart Hopkins from Splunk
+name: Detect Outbound SMB Traffic
+id: 7f5fb3e1-4209-414-90db-0ec21b936378
+version: 3
 date: '2020-07-21'
+author: Bhavin Patel, Stuart Hopkins from Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for outbound SMB connections made by hosts within your
   network to the Internet. SMB traffic is used for Windows file-sharing activity.
   One of the techniques often used by attackers involves retrieving the credential
   hash using an SMB request made to a compromised server controlled by the threat
   actor.
-how_to_implement: 'In order to run this search effectively, we highly recommend that
-  you leverage the Assets and Identity framework. It is important that you have good
-  understanding of how your network segments are designed, and be able to distinguish
-  internal from external address space. Add a category named `internal` to the CIDRs
-  that host the companys assets in `assets_by_cidr.csv` lookup file, which is located
-  in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating
-  this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata.
-  This search also requires you to be ingesting your network traffic and populating
-  the Network_Traffic data model'
-id: 7f5fb3e1-4209-414-90db-0ec21b936378
-known_false_positives: It is likely that the outbound Server Message Block (SMB) traffic
-  is legitimate, if the company's internal networks are not well-defined in the Assets
-  and Identity Framework. Categorize the internal CIDR blocks as `internal` in the
-  lookup file to avoid creating notable events for traffic destined to those CIDR
-  blocks. Any other network connection that is going out to the Internet should be
-  investigated and blocked. Best practices suggest preventing external communications
-  of all SMB versions and related protocols at the network boundary.
-name: Detect Outbound SMB Traffic
-references: []
 search: '| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time)
   as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app
   values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port
@@ -33,8 +20,25 @@ search: '| tstats `security_content_summariesonly` earliest(_time) as start_time
   OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by
   All_Traffic.src_ip | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(start_time)`
   | `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter`'
+how_to_implement: 'In order to run this search effectively, we highly recommend that
+  you leverage the Assets and Identity framework. It is important that you have good
+  understanding of how your network segments are designed, and be able to distinguish
+  internal from external address space. Add a category named `internal` to the CIDRs
+  that host the companys assets in `assets_by_cidr.csv` lookup file, which is located
+  in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating
+  this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata.
+  This search also requires you to be ingesting your network traffic and populating
+  the Network_Traffic data model'
+known_false_positives: It is likely that the outbound Server Message Block (SMB) traffic
+  is legitimate, if the company's internal networks are not well-defined in the Assets
+  and Identity Framework. Categorize the internal CIDR blocks as `internal` in the
+  lookup file to avoid creating notable events for traffic destined to those CIDR
+  blocks. Any other network connection that is going out to the Internet should be
+  investigated and blocked. Best practices suggest preventing external communications
+  of all SMB versions and related protocols at the network boundary.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Hidden Cobra Malware
   - DHS Report TA18-074A
   - Sunburst Malware
@@ -53,5 +57,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/network/detect_port_security_violation.yml b/detections/network/detect_port_security_violation.yml
index 07a9225f6f..7d38351e33 100644
--- a/detections/network/detect_port_security_violation.yml
+++ b/detections/network/detect_port_security_violation.yml
@@ -1,5 +1,10 @@
-author: Mikael Bjerkeland, Splunk
+name: Detect Port Security Violation
+id: 2de3d5b8-a4fa-45c5-8540-6d071c194d24
+version: 1
 date: '2020-10-28'
+author: Mikael Bjerkeland, Splunk
+type: batch
+datamodel: []
 description: By enabling Port Security on a Cisco switch you can restrict input to
   an interface by limiting and identifying MAC addresses of the workstations that
   are allowed to access the port. When you assign secure MAC addresses to a secure
@@ -10,26 +15,24 @@ description: By enabling Port Security on a Cisco switch you can restrict input
   maximum number of secure MAC addresses is reached, when the MAC address of a workstation
   attempting to access the port is different from any of the identified secure MAC
   addresses, a security violation occurs.
-how_to_implement: This search uses a standard SPL query on logs from Cisco Network
-  devices. The network devices must be configured with Port Security and Error Disable
-  for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html)
-  and log with a severity level of minimum "5 - notification". The search also requires
-  that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
-  is used to parse the logs from the Cisco network devices.
-id: 2de3d5b8-a4fa-45c5-8540-6d071c194d24
-known_false_positives: This search might be prone to high false positives if you have
-  malfunctioning devices connected to your ethernet ports or if end users periodically
-  connect physical devices to the network.
-name: Detect Port Security Violation
-references: []
 search: '`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation")
   OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN")
   | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime
   max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS
   src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter`'
+how_to_implement: This search uses a standard SPL query on logs from Cisco Network
+  devices. The network devices must be configured with Port Security and Error Disable
+  for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html)
+  and log with a severity level of minimum "5 - notification". The search also requires
+  that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
+  is used to parse the logs from the Cisco network devices.
+known_false_positives: This search might be prone to high false positives if you have
+  malfunctioning devices connected to your ethernet ports or if end users periodically
+  connect physical devices to the network.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Router and Infrastructure Security
   asset_type: Infrastructure
   cis20:
@@ -53,5 +56,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_rogue_dhcp_server.yml b/detections/network/detect_rogue_dhcp_server.yml
index 0e315b14fd..c3acdc47d9 100644
--- a/detections/network/detect_rogue_dhcp_server.yml
+++ b/detections/network/detect_rogue_dhcp_server.yml
@@ -1,26 +1,29 @@
-author: Mikael Bjerkeland, Splunk
+name: Detect Rogue DHCP Server
+id: 6e1ada88-7a0d-4ac1-92c6-03d354686079
+version: 1
 date: '2020-08-11'
+author: Mikael Bjerkeland, Splunk
+type: batch
+datamodel: []
 description: By enabling DHCP Snooping as a Layer 2 Security measure on the organization's
   network devices, we will be able to detect unauthorized DHCP servers handing out
   DHCP leases to devices on the network (Man in the Middle attack).
+search: '`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT"
+  | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type)
+  AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`|
+  `detect_rogue_dhcp_server_filter`'
 how_to_implement: This search uses a standard SPL query on logs from Cisco Network
   devices. The network devices must be configured with DHCP Snooping enabled (see
   https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html)
   and log with a severity level of minimum "5 - notification". The search also requires
   that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
   is used to parse the logs from the Cisco network devices.
-id: 6e1ada88-7a0d-4ac1-92c6-03d354686079
 known_false_positives: This search might be prone to high false positives if DHCP
   Snooping has been incorrectly configured or in the unlikely event that the DHCP
   server has been moved to another network interface.
-name: Detect Rogue DHCP Server
 references: []
-search: '`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT"
-  | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type)
-  AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`|
-  `detect_rogue_dhcp_server_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Router and Infrastructure Security
   asset_type: Infrastructure
   cis20:
@@ -43,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_snicat_sni_exfiltration.yml b/detections/network/detect_snicat_sni_exfiltration.yml
index f65d87930f..862cb13a89 100644
--- a/detections/network/detect_snicat_sni_exfiltration.yml
+++ b/detections/network/detect_snicat_sni_exfiltration.yml
@@ -1,25 +1,28 @@
-author: Shannon Davis, Splunk
+name: Detect SNICat SNI Exfiltration
+id: 82d06410-134c-11eb-adc1-0242ac120002
+version: 1
 date: '2020-10-21'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
 description: This search looks for commands that the SNICat tool uses in the TLS SNI
   field.
+search: '`zeek_ssl` | rex field=server_name "(?<snicat>(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)"
+  | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip
+  dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter`'
 how_to_implement: You must be ingesting Zeek SSL data into Splunk. Zeek data should
   also be getting ingested in JSON format.  We are detecting when any of the predefined
   SNICat commands are found within the server_name (SNI) field. These commands are
   LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito.  You can go further
   once this has been detected, and run other searches to decode the SNI data to prove
   or disprove if any data exfiltration has taken place.
-id: 82d06410-134c-11eb-adc1-0242ac120002
 known_false_positives: Unknown
-name: Detect SNICat SNI Exfiltration
 references:
 - https://www.mnemonic.no/blog/introducing-snicat/
 - https://github.com/mnemonic-no/SNIcat
 - https://attack.mitre.org/techniques/T1041/
-search: '`zeek_ssl` | rex field=server_name "(?<snicat>(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)"
-  | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip
-  dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Data Exfiltration
   asset_type: Network
   cis20:
@@ -37,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_software_download_to_network_device.yml b/detections/network/detect_software_download_to_network_device.yml
index 619837fe0c..33db59428f 100644
--- a/detections/network/detect_software_download_to_network_device.yml
+++ b/detections/network/detect_software_download_to_network_device.yml
@@ -1,22 +1,16 @@
-author: Mikael Bjerkeland, Splunk
+name: Detect Software Download To Network Device
+id: cc590c66-f65f-48f2-986a-4797244762f8
+version: 1
 date: '2020-10-28'
+author: Mikael Bjerkeland, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: Adversaries may abuse netbooting to load an unauthorized network device
   operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot
   (netbooting) is commonly used by network administrators to load configuration-controlled
   network device images from a centralized management server. Netbooting is one option
   in the boot sequence and can be used to centralize, manage, and control device images.
-how_to_implement: This search looks for Network Traffic events to TFTP, FTP or SSH/SCP
-  ports from network devices. Make sure to tag any network devices as network, router
-  or switch in order for this detection to work. If the TFTP traffic doesn't traverse
-  a firewall nor packet inspection, these events will not be logged. This is typically
-  an issue if the TFTP server is on the same subnet as the network device. There is
-  also a chance of the network device loading software using a DHCP assigned IP address
-  (netboot) which is not in the Asset inventory.
-id: cc590c66-f65f-48f2-986a-4797244762f8
-known_false_positives: This search will also report any legitimate attempts of software
-  downloads to network devices as well as outbound SSH sessions from network devices.
-name: Detect Software Download To Network Device
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND
   All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21)
@@ -24,8 +18,18 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch
   by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")`
   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter`'
+how_to_implement: This search looks for Network Traffic events to TFTP, FTP or SSH/SCP
+  ports from network devices. Make sure to tag any network devices as network, router
+  or switch in order for this detection to work. If the TFTP traffic doesn't traverse
+  a firewall nor packet inspection, these events will not be logged. This is typically
+  an issue if the TFTP server is on the same subnet as the network device. There is
+  also a chance of the network device loading software using a DHCP assigned IP address
+  (netboot) which is not in the Asset inventory.
+known_false_positives: This search will also report any legitimate attempts of software
+  downloads to network devices as well as outbound SSH sessions from network devices.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Router and Infrastructure Security
   asset_type: Infrastructure
   cis20:
@@ -44,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_traffic_mirroring.yml b/detections/network/detect_traffic_mirroring.yml
index 0c04541021..f270c9a423 100644
--- a/detections/network/detect_traffic_mirroring.yml
+++ b/detections/network/detect_traffic_mirroring.yml
@@ -1,10 +1,20 @@
-author: Mikael Bjerkeland, Splunk
+name: Detect Traffic Mirroring
+id: 42b3b753-5925-49c5-9742-36fa40a73990
+version: 1
 date: '2020-10-28'
+author: Mikael Bjerkeland, Splunk
+type: batch
+datamodel: []
 description: Adversaries may leverage traffic mirroring in order to automate data
   exfiltration over compromised network infrastructure. Traffic mirroring is a native
   feature for some network devices and used for network analysis and may be configured
   to duplicate traffic and forward to one or more destinations for analysis by a network
   analyzer or other monitoring device.
+search: '`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN"
+  mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD"
+  command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime
+  count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
+  | `detect_traffic_mirroring_filter`'
 how_to_implement: This search uses a standard SPL query on logs from Cisco Network
   devices. The network devices must log with a severity level of minimum "5 - notification".
   The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
@@ -12,18 +22,11 @@ how_to_implement: This search uses a standard SPL query on logs from Cisco Netwo
   been configured according to the documentation of the Cisco Networks Add-on. Also
   note that an attacker may disable logging from the device prior to enabling traffic
   mirroring.
-id: 42b3b753-5925-49c5-9742-36fa40a73990
 known_false_positives: This search will return false positives for any legitimate
   traffic captures by network administrators.
-name: Detect Traffic Mirroring
 references: []
-search: '`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN"
-  mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD"
-  command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime
-  count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
-  | `detect_traffic_mirroring_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Router and Infrastructure Security
   asset_type: Infrastructure
   cis20:
@@ -45,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_unauthorized_assets_by_mac_address.yml b/detections/network/detect_unauthorized_assets_by_mac_address.yml
index 99b1d56891..b71232d79f 100644
--- a/detections/network/detect_unauthorized_assets_by_mac_address.yml
+++ b/detections/network/detect_unauthorized_assets_by_mac_address.yml
@@ -1,31 +1,35 @@
-author: Bhavin Patel, Splunk
+name: Detect Unauthorized Assets by MAC address
+id: dcfd6b40-42f9-469d-a433-2e53f7489ff4
+version: 1
 date: '2017-09-13'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Sessions
 description: By populating the organization's assets within the assets_by_str.csv,
   we will be able to detect unauthorized devices that are trying to connect with the
   organization's network by inspecting DHCP request packets, which are issued by devices
   when they attempt to obtain an IP address from the DHCP server. The MAC address
   associated with the source of the DHCP request is checked against the list of known
   devices, and reports on those that are not found.
-how_to_implement: This search uses the Network_Sessions data model shipped with Enterprise
-  Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv
-  file located in SA-IdentityManagement, which will contain a list of known authorized
-  organizational assets including their MAC addresses. Ensure that all inventoried
-  systems have their MAC address populated.
-id: dcfd6b40-42f9-469d-a433-2e53f7489ff4
-known_false_positives: This search might be prone to high false positives. Please
-  consider this when conducting analysis or investigations. Authorized devices may
-  be detected as unauthorized. If this is the case, verify the MAC address of the
-  system responsible for the false positive and add it to the Assets and Identity
-  framework with the proper information.
-name: Detect Unauthorized Assets by MAC address
-references: []
 search: '| tstats `security_content_summariesonly` count from datamodel=Network_Sessions
   where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip
   All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")`
   | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields
   + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter`'
+how_to_implement: This search uses the Network_Sessions data model shipped with Enterprise
+  Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv
+  file located in SA-IdentityManagement, which will contain a list of known authorized
+  organizational assets including their MAC addresses. Ensure that all inventoried
+  systems have their MAC address populated.
+known_false_positives: This search might be prone to high false positives. Please
+  consider this when conducting analysis or investigations. Authorized devices may
+  be detected as unauthorized. If this is the case, verify the MAC address of the
+  system responsible for the false positive and add it to the Assets and Identity
+  framework with the proper information.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Asset Tracking
   asset_type: Infrastructure
   cis20:
@@ -42,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml b/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml
index b7d8230144..0d9fa88013 100644
--- a/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml
+++ b/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml
@@ -1,21 +1,24 @@
-author: Shannon Davis, Splunk
-date: '2020-07-28'
-description: This search detects SIGRed via Splunk Stream.
-how_to_implement: You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We
-  are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size
-  via stream:tcp.  Replace the macro definitions ('stream:dns' and 'stream:tcp') with
-  configurations for your Splunk environment.
-id: babd8d10-d073-11ea-87d0-0242ac130003
-known_false_positives: unknown
 name: Detect Windows DNS SIGRed via Splunk Stream
-references:
-- https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/
+id: babd8d10-d073-11ea-87d0-0242ac130003
+version: 1
+date: '2020-07-28'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
+description: This search detects SIGRed via Splunk Stream.
 search: '`stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY)
   | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp`
   bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats
   count by flow_id | where count>1 | fields - count'
+how_to_implement: You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We
+  are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size
+  via stream:tcp.  Replace the macro definitions ('stream:dns' and 'stream:tcp') with
+  configurations for your Splunk environment.
+known_false_positives: unknown
+references:
+- https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/
 tags:
-  analytics_story:
+  analytic_story:
   - Windows DNS SIGRed CVE-2020-1350
   asset_type: Endpoint
   cis20:
@@ -32,5 +35,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/detect_windows_dns_sigred_via_zeek.yml b/detections/network/detect_windows_dns_sigred_via_zeek.yml
index 8ff6dd3a8e..7a7e2410f9 100644
--- a/detections/network/detect_windows_dns_sigred_via_zeek.yml
+++ b/detections/network/detect_windows_dns_sigred_via_zeek.yml
@@ -1,23 +1,27 @@
-author: Shannon Davis, Splunk
-date: '2020-07-28'
-description: This search detects SIGRed via Zeek DNS and Zeek Conn data.
-how_to_implement: You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek
-  data should also be getting ingested in JSON format.  We are detecting SIG and KEY
-  records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json.  The
-  Network Resolution and Network Traffic datamodels are in use for this search.
-id: c5c622e4-d073-11ea-87d0-0242ac130003
-known_false_positives: unknown
 name: Detect Windows DNS SIGRed via Zeek
-references:
-- https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/
+id: c5c622e4-d073-11ea-87d0-0242ac130003
+version: 1
+date: '2020-07-28'
+author: Shannon Davis, Splunk
+type: batch
+datamodel:
+- Network_Resolution
+description: This search detects SIGRed via Zeek DNS and Zeek Conn data.
 search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
   where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id
   | append [| tstats  `security_content_summariesonly` count from datamodel=Network_Traffic
   where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id
   as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id
   | where count>1 | fields - count '
+how_to_implement: You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek
+  data should also be getting ingested in JSON format.  We are detecting SIG and KEY
+  records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json.  The
+  Network Resolution and Network Traffic datamodels are in use for this search.
+known_false_positives: unknown
+references:
+- https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/
 tags:
-  analytics_story:
+  analytic_story:
   - Windows DNS SIGRed CVE-2020-1350
   asset_type: Endpoint
   cis20:
@@ -34,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: endpoint
-type: ESCU
-version: 1
diff --git a/detections/network/detect_zerologon_via_zeek.yml b/detections/network/detect_zerologon_via_zeek.yml
index 45778fbe05..4a09aa0491 100644
--- a/detections/network/detect_zerologon_via_zeek.yml
+++ b/detections/network/detect_zerologon_via_zeek.yml
@@ -1,25 +1,28 @@
-author: Shannon Davis, Splunk
+name: Detect Zerologon via Zeek
+id: bf7a06ec-f703-11ea-adc1-0242ac120002
+version: 1
 date: '2020-09-15'
+author: Shannon Davis, Splunk
+type: batch
+datamodel: []
 description: This search detects attempts to run exploits for the Zerologon CVE-2020-1472
   vulnerability via Zeek RPC
-how_to_implement: You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should
-  also be getting ingested in JSON format.  We are detecting when all three RPC operations
-  (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app
-  via bro:rpc:json.  These three operations are then correlated on the Zeek UID field.
-id: bf7a06ec-f703-11ea-adc1-0242ac120002
-known_false_positives: unknown
-name: Detect Zerologon via Zeek
-references:
-- https://www.secura.com/blog/zero-logon
-- https://github.com/SecuraBV/CVE-2020-1472
-- https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472
 search: '`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3)
   | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge"))
   as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2"))
   as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4
   passcount>0 | search `detect_zerologon_via_zeek_filter`'
+how_to_implement: You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should
+  also be getting ingested in JSON format.  We are detecting when all three RPC operations
+  (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app
+  via bro:rpc:json.  These three operations are then correlated on the Zeek UID field.
+known_false_positives: unknown
+references:
+- https://www.secura.com/blog/zero-logon
+- https://github.com/SecuraBV/CVE-2020-1472
+- https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472
 tags:
-  analytics_story:
+  analytic_story:
   - Detect Zerologon Attack
   asset_type: Network
   cis20:
@@ -36,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/network/dns_query_length_outliers___mltk.yml b/detections/network/dns_query_length_outliers___mltk.yml
index 7337653a73..ec6b240ebd 100644
--- a/detections/network/dns_query_length_outliers___mltk.yml
+++ b/detections/network/dns_query_length_outliers___mltk.yml
@@ -1,7 +1,20 @@
-author: Rico Valdez, Splunk
+name: DNS Query Length Outliers - MLTK
+id: 85fbcfe8-9718-4911-adf6-7000d077a3a9
+version: 2
 date: '2020-01-22'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search allows you to identify DNS requests that are unusually large
   for the record type being requested in your environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as start_time
+  max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution
+  by DNS.query DNS.record_type | search DNS.record_type=* |  `drop_dm_object_name(DNS)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval
+  query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)"
+  as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time
+  query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` '
 how_to_implement: 'To successfully implement this search, you will need to ensure
   that DNS data is populating the Network_Resolution data model. In addition, the
   Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your
@@ -30,21 +43,12 @@ how_to_implement: 'To successfully implement this search, you will need to ensur
 
   Detailed documentation on how to create a new field within Incident Review may be
   found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-id: 85fbcfe8-9718-4911-adf6-7000d077a3a9
 known_false_positives: If you are seeing more results than desired, you may consider
   reducing the value for threshold in the search. You should also periodically re-run
   the support search to re-build the ML model on the latest data.
-name: DNS Query Length Outliers - MLTK
 references: []
-search: '| tstats `security_content_summariesonly` count min(_time) as start_time
-  max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution
-  by DNS.query DNS.record_type | search DNS.record_type=* |  `drop_dm_object_name(DNS)`
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval
-  query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)"
-  as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time
-  query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - Hidden Cobra Malware
   - Suspicious DNS Traffic
   - Command and Control
@@ -65,5 +69,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/network/dns_query_length_with_high_standard_deviation.yml b/detections/network/dns_query_length_with_high_standard_deviation.yml
index 8b86a07f91..932d7bbc4c 100644
--- a/detections/network/dns_query_length_with_high_standard_deviation.yml
+++ b/detections/network/dns_query_length_with_high_standard_deviation.yml
@@ -1,21 +1,25 @@
-author: Bhavin Patel, Splunk
+name: DNS Query Length With High Standard Deviation
+id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5
+version: 3
 date: '2021-01-18'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel:
+- Network_Resolution
 description: This search allows you to identify DNS requests and compute the standard
   deviation on the length of the names being resolved, then filter on two times the
   standard deviation to show you those queries that are unusually large for your environment.
-how_to_implement: To successfully implement this search, you will need to ensure that
-  DNS data is populating the Network_Resolution data model.
-id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5
-known_false_positives: It's possible there can be long domain names that are legitimate.
-name: DNS Query Length With High Standard Deviation
-references: []
 search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
   by DNS.query |  `drop_dm_object_name("DNS")` | eval query_length = len(query) |
   table query query_length record_type count | eventstats stdev(query_length) AS stdev
   avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2)
   | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` '
+how_to_implement: To successfully implement this search, you will need to ensure that
+  DNS data is populating the Network_Resolution data model.
+known_false_positives: It's possible there can be long domain names that are legitimate.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Hidden Cobra Malware
   - Suspicious DNS Traffic
   - Command and Control
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/network/remote_desktop_network_bruteforce.yml b/detections/network/remote_desktop_network_bruteforce.yml
index 9d3b497547..b202102997 100644
--- a/detections/network/remote_desktop_network_bruteforce.yml
+++ b/detections/network/remote_desktop_network_bruteforce.yml
@@ -1,23 +1,27 @@
-author: Jose Hernandez, Splunk
+name: Remote Desktop Network Bruteforce
+id: a98727cc-286b-4ff2-b898-41df64695923
+version: 2
 date: '2020-07-21'
+author: Jose Hernandez, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for RDP application network traffic and filters any
   source/destination pair generating more than twice the standard deviation of the
   average traffic.
-how_to_implement: You must ensure that your network traffic data is populating the
-  Network_Traffic data model.
-id: a98727cc-286b-4ff2-b898-41df64695923
-known_false_positives: RDP gateways may have unusually high amounts of traffic from
-  all other hosts' RDP applications in the network.
-name: Remote Desktop Network Bruteforce
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src
   All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count)
   AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src
   AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50
   stdev | `remote_desktop_network_bruteforce_filter`'
+how_to_implement: You must ensure that your network traffic data is populating the
+  Network_Traffic data model.
+known_false_positives: RDP gateways may have unusually high amounts of traffic from
+  all other hosts' RDP applications in the network.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   - Ryuk Ransomware
   asset_type: Endpoint
@@ -39,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/network/remote_desktop_network_traffic.yml b/detections/network/remote_desktop_network_traffic.yml
index 96d211213b..4114cd1ea1 100644
--- a/detections/network/remote_desktop_network_traffic.yml
+++ b/detections/network/remote_desktop_network_traffic.yml
@@ -1,10 +1,21 @@
-author: David Dorsey, Splunk
+name: Remote Desktop Network Traffic
+id: 272b8407-842d-4b3d-bead-a704584003d3
+version: 3
 date: '2020-07-07'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for network traffic on TCP/3389, the default port used
   by remote desktop. While remote desktop traffic is not uncommon on a network, it
   is usually associated with known hosts. This search will ignore common RDP sources
   and common RDP destinations so you can focus on the uncommon uses of remote desktop
   on your network.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND
+  All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source
+  by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` '
 how_to_implement: To successfully implement this search you need to identify systems
   that commonly originate remote desktop traffic and that commonly receive remote
   desktop traffic. You can use the included support search "Identify Systems Creating
@@ -14,17 +25,10 @@ how_to_implement: To successfully implement this search you need to identify sys
   to add the "common_rdp_source" or "common_rdp_destination" category to that system
   depending on the usage, using the Enterprise Security Assets and Identities framework.  This
   can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups.
-id: 272b8407-842d-4b3d-bead-a704584003d3
 known_false_positives: Remote Desktop may be used legitimately by users on the network.
-name: Remote Desktop Network Traffic
 references: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
-  as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND
-  All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source
-  by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")`
-  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - SamSam Ransomware
   - Ryuk Ransomware
   - Hidden Cobra Malware
@@ -47,5 +51,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/network/smb_traffic_spike.yml b/detections/network/smb_traffic_spike.yml
index 1f2bc1e7b4..313ae164cb 100644
--- a/detections/network/smb_traffic_spike.yml
+++ b/detections/network/smb_traffic_spike.yml
@@ -1,14 +1,13 @@
-author: David Dorsey, Splunk
+name: SMB Traffic Spike
+id: 7f5fb3e1-4209-4914-90db-0ec21b936378
+version: 3
 date: '2020-07-22'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for spikes in the number of Server Message Block (SMB)
   traffic connections.
-how_to_implement: This search requires you to be ingesting your network traffic logs
-  and populating the `Network_Traffic` data model.
-id: 7f5fb3e1-4209-4914-90db-0ec21b936378
-known_false_positives: A file server may experience high-demand loads that could cause
-  this analytic to trigger.
-name: SMB Traffic Spike
-references: []
 search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic
   where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb
   by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats
@@ -17,8 +16,13 @@ search: '| tstats `security_content_summariesonly` count from datamodel=Network_
   count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count,
   null))) as stdev by src | eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound
   AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` '
+how_to_implement: This search requires you to be ingesting your network traffic logs
+  and populating the `Network_Traffic` data model.
+known_false_positives: A file server may experience high-demand loads that could cause
+  this analytic to trigger.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Hidden Cobra Malware
   - Ransomware
@@ -37,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/network/smb_traffic_spike___mltk.yml b/detections/network/smb_traffic_spike___mltk.yml
index 888de5a289..9121b5469f 100644
--- a/detections/network/smb_traffic_spike___mltk.yml
+++ b/detections/network/smb_traffic_spike___mltk.yml
@@ -1,7 +1,20 @@
-author: Rico Valdez, Splunk
+name: SMB Traffic Spike - MLTK
+id: d25773ba-9ad8-48d1-858e-07ad0bbeb828
+version: 3
 date: '2020-07-22'
+author: Rico Valdez, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search uses the Machine Learning Toolkit (MLTK) to identify spikes
   in the number of Server Message Block (SMB) connections.
+search: '| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip)
+  as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where
+  All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by
+  _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time,
+  "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001
+  | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count |
+  table _time src dest port count | `smb_traffic_spike___mltk_filter` '
 how_to_implement: 'To successfully implement this search, you will need to ensure
   that DNS data is populating the Network_Resolution data model. In addition, the
   Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your
@@ -24,22 +37,13 @@ how_to_implement: 'To successfully implement this search, you will need to ensur
 
   Detailed documentation on how to create a new field within Incident Review is found
   here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-id: d25773ba-9ad8-48d1-858e-07ad0bbeb828
 known_false_positives: If you are seeing more results than desired, you may consider
   reducing the value of the threshold in the search. You should also periodically
   re-run the support search to re-build the ML model on the latest data. Please update
   the `smb_traffic_spike_mltk_filter` macro to filter out false positive results
-name: SMB Traffic Spike - MLTK
 references: []
-search: '| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip)
-  as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where
-  All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by
-  _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time,
-  "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001
-  | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count |
-  table _time src dest port count | `smb_traffic_spike___mltk_filter` '
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Hidden Cobra Malware
   - Ransomware
@@ -58,5 +62,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 3
diff --git a/detections/network/tor_traffic.yml b/detections/network/tor_traffic.yml
index 2e87ee1d6f..cc8a33a88f 100644
--- a/detections/network/tor_traffic.yml
+++ b/detections/network/tor_traffic.yml
@@ -1,24 +1,28 @@
-author: David Dorsey, Splunk
+name: TOR Traffic
+id: ea688274-9c06-4473-b951-e4cb7a5d7a45
+version: 2
 date: '2020-07-22'
+author: David Dorsey, Splunk
+type: batch
+datamodel:
+- Network_Traffic
 description: This search looks for network traffic identified as The Onion Router
   (TOR), a benign anonymity network which can be abused for a variety of nefarious
   purposes.
-how_to_implement: In order to properly run this search, Splunk needs to ingest data
-  from firewalls or other network control devices that mediate the traffic allowed
-  into an environment. This is necessary so that the search can identify an 'action'
-  taken on the traffic of interest. The search requires the Network_Traffic data model
-  be populated.
-id: ea688274-9c06-4473-b951-e4cb7a5d7a45
-known_false_positives: None at this time
-name: TOR Traffic
-references: []
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed
   by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")`
   | `tor_traffic_filter`'
+how_to_implement: In order to properly run this search, Splunk needs to ingest data
+  from firewalls or other network control devices that mediate the traffic allowed
+  into an environment. This is necessary so that the search can identify an 'action'
+  taken on the traffic of interest. The search requires the Network_Traffic data model
+  be populated.
+known_false_positives: None at this time
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Prohibited Traffic Allowed or Protocol Mismatch
   - Ransomware
   - Command and Control
@@ -38,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 2
diff --git a/detections/network/unusually_long_content_type_length.yml b/detections/network/unusually_long_content_type_length.yml
index 00a64f84f2..a8cba96a7e 100644
--- a/detections/network/unusually_long_content_type_length.yml
+++ b/detections/network/unusually_long_content_type_length.yml
@@ -1,20 +1,23 @@
-author: Bhavin Patel, Splunk
+name: Unusually Long Content-Type Length
+id: 57a0a2bf-353f-40c1-84dc-29293f3c35b7
+version: 1
 date: '2017-10-13'
+author: Bhavin Patel, Splunk
+type: batch
+datamodel: []
 description: This search looks for unusually long strings in the Content-Type http
   header that the client sends the server.
-how_to_implement: This particular search leverages data extracted from Stream:HTTP.
-  You must configure the http stream using the Splunk Stream App on your Splunk Stream
-  deployment server to extract the cs_content_type field.
-id: 57a0a2bf-353f-40c1-84dc-29293f3c35b7
-known_false_positives: Very few legitimate Content-Type fields will have a length
-  greater than 100 characters.
-name: Unusually Long Content-Type Length
-references: []
 search: '`stream_http` | eval cs_content_type_length = len(cs_content_type) | where
   cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length
   cs_content_type url | `unusually_long_content_type_length_filter`'
+how_to_implement: This particular search leverages data extracted from Stream:HTTP.
+  You must configure the http stream using the Splunk Stream App on your Splunk Stream
+  deployment server to extract the cs_content_type field.
+known_false_positives: Very few legitimate Content-Type fields will have a length
+  greater than 100 characters.
+references: []
 tags:
-  analytics_story:
+  analytic_story:
   - Apache Struts Vulnerability
   asset_type: Web Server
   cis20:
@@ -37,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: network
-type: ESCU
-version: 1
diff --git a/detections/web/web_fraud___account_harvesting.yml b/detections/web/web_fraud___account_harvesting.yml
index e36d970332..01cad47d36 100644
--- a/detections/web/web_fraud___account_harvesting.yml
+++ b/detections/web/web_fraud___account_harvesting.yml
@@ -1,7 +1,17 @@
-author: Jim Apger, Splunk
+name: Web Fraud - Account Harvesting
+id: 31337aaa-941d-4ada-81ac-q2a17be5bf0d
+version: 1
 date: '2018-10-08'
+author: Jim Apger, Splunk
+type: batch
+datamodel: []
 description: This search is used to identify the creation of multiple user accounts
   using the same email domain name.
+search: '`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/"
+  | rex field=cookie "form_key=(?<SessionID>\w+)" | rex field=form_data "login\[username\]=(?<Username>[^&|^$]+)"
+  | search Username=* | rex field=Username "@(?<email_domain>.*)" | stats dc(Username)
+  as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames>
+  25 | `web_fraud___account_harvesting_filter`'
 how_to_implement: We start with a dataset that provides visibility into the email
   address used for the account creation. In this example, we are narrowing our search
   down to the single web page that hosts the Magento2 e-commerce platform (via URI)
@@ -10,7 +20,6 @@ how_to_implement: We start with a dataset that provides visibility into the emai
   reasons.  After we have the username and email domain, we look for numerous account
   creations per email domain.  Common data sources used for this detection are customized
   Apache logs or Splunk Stream.
-id: 31337aaa-941d-4ada-81ac-q2a17be5bf0d
 known_false_positives: As is common with many fraud-related searches, we are usually
   looking to attribute risk or synthesize relevant context with loosely written detections
   that simply detect anamolous behavior. This search will need to be customized to
@@ -22,17 +31,11 @@ known_false_positives: As is common with many fraud-related searches, we are usu
   for an anomalous spikes, will improve this search.  You can also use Shannon entropy
   or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness
   or similarity of the email name or email domain, as the names are often machine-generated.
-name: Web Fraud - Account Harvesting
 references:
 - https://splunkbase.splunk.com/app/2734/
 - https://splunkbase.splunk.com/app/1809/
-search: '`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/"
-  | rex field=cookie "form_key=(?<SessionID>\w+)" | rex field=form_data "login\[username\]=(?<Username>[^&|^$]+)"
-  | search Username=* | rex field=Username "@(?<email_domain>.*)" | stats dc(Username)
-  as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames>
-  25 | `web_fraud___account_harvesting_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Web Fraud Detection
   asset_type: Account
   cis20:
@@ -49,5 +52,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/web/web_fraud___anomalous_user_clickspeed.yml b/detections/web/web_fraud___anomalous_user_clickspeed.yml
index 2a6c4d108f..9a82fac2eb 100644
--- a/detections/web/web_fraud___anomalous_user_clickspeed.yml
+++ b/detections/web/web_fraud___anomalous_user_clickspeed.yml
@@ -1,9 +1,19 @@
-author: Jim Apger, Splunk
+name: Web Fraud - Anomalous User Clickspeed
+id: 31337bbb-bc22-4752-b599-ef192df2dc7a
+version: 1
 date: '2018-10-08'
+author: Jim Apger, Splunk
+type: batch
+datamodel: []
 description: This search is used to examine web sessions to identify those where the
   clicks are occurring too quickly for a human or are occurring with a near-perfect
   cadence (high periodicity or low standard deviation), resembling a script driven
   session.
+search: '`stream_http` http_content_type=text* | rex field=cookie "form_key=(?<session_id>\w+)"
+  | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where
+  TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as
+  ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5)
+  | `web_fraud___anomalous_user_clickspeed_filter`'
 how_to_implement: Start with a dataset that allows you to see clickstream data for
   each user click on the website. That data must have a time stamp and must contain
   a reference to the session identifier being used by the website. This ties the clicks
@@ -12,23 +22,16 @@ how_to_implement: Start with a dataset that allows you to see clickstream data f
   such as scraping, crawling, application DDOS, credit-card testing, account takeover,
   etc. Common data sources used for this detection are customized Apache logs, customized
   IIS, and Splunk Stream.
-id: 31337bbb-bc22-4752-b599-ef192df2dc7a
 known_false_positives: As is common with many fraud-related searches, we are usually
   looking to attribute risk or synthesize relevant context with loosly written detections
   that simply detect anamoluous behavior.
-name: Web Fraud - Anomalous User Clickspeed
 references:
 - https://en.wikipedia.org/wiki/Session_ID
 - https://en.wikipedia.org/wiki/Session_(computer_science)
 - https://en.wikipedia.org/wiki/HTTP_cookie
 - https://splunkbase.splunk.com/app/1809/
-search: '`stream_http` http_content_type=text* | rex field=cookie "form_key=(?<session_id>\w+)"
-  | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where
-  TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as
-  ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5)
-  | `web_fraud___anomalous_user_clickspeed_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Web Fraud Detection
   asset_type: account
   cis20:
@@ -45,5 +48,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/detections/web/web_fraud___password_sharing_across_accounts.yml b/detections/web/web_fraud___password_sharing_across_accounts.yml
index 91eb6167bb..e80e2f4b1f 100644
--- a/detections/web/web_fraud___password_sharing_across_accounts.yml
+++ b/detections/web/web_fraud___password_sharing_across_accounts.yml
@@ -1,29 +1,32 @@
-author: Jim Apger, Splunk
+name: Web Fraud - Password Sharing Across Accounts
+id: 31337a1a-53b9-4e05-96e9-55c934cb71d3
+version: 1
 date: '2018-10-08'
+author: Jim Apger, Splunk
+type: batch
+datamodel: []
 description: This search is used to identify user accounts that share a common password.
+search: '`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost*  |
+  rex field=form_data "login\[username\]=(?<Username>[^&|^$]+)" | rex field=form_data
+  "login\[password\]=(?<Password>[^&|^$]+)" | stats dc(Username) as UniqueUsernames
+  values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5
+  | `web_fraud___password_sharing_across_accounts_filter`'
 how_to_implement: We need to start with a dataset that allows us to see the values
   of usernames and passwords that users are submitting to the website hosting the
   Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized
   or hashed value of a password is acceptable and certainly preferable to a clear-text
   password. Common data sources used for this detection are customized Apache logs,
   customized IIS, and Splunk Stream.
-id: 31337a1a-53b9-4e05-96e9-55c934cb71d3
 known_false_positives: As is common with many fraud-related searches, we are usually
   looking to attribute risk or synthesize relevant context with loosely written detections
   that simply detect anamoluous behavior.
-name: Web Fraud - Password Sharing Across Accounts
 references:
 - https://en.wikipedia.org/wiki/Session_ID
 - https://en.wikipedia.org/wiki/Session_(computer_science)
 - https://en.wikipedia.org/wiki/HTTP_cookie
 - https://splunkbase.splunk.com/app/1809/
-search: '`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost*  |
-  rex field=form_data "login\[username\]=(?<Username>[^&|^$]+)" | rex field=form_data
-  "login\[password\]=(?<Password>[^&|^$]+)" | stats dc(Username) as UniqueUsernames
-  values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5
-  | `web_fraud___password_sharing_across_accounts_filter`'
 tags:
-  analytics_story:
+  analytic_story:
   - Web Fraud Detection
   asset_type: account
   cis20:
@@ -35,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   security_domain: threat
-type: ESCU
-version: 1
diff --git a/docs/spec/README.md b/docs/spec/README.md
new file mode 100644
index 0000000000..7c4e1f7821
--- /dev/null
+++ b/docs/spec/README.md
@@ -0,0 +1,49 @@
+# README
+
+## Top-level Schemas
+
+*   [Analytics Story Schema](./stories.md "schema analytics story") – `http://example.com/example.json`
+
+*   [Baseline Schema](./baselines.md "schema for baselines") – `http://example.com/example.json`
+
+*   [Deployment Schema](./deployments.md "schema for deployment") – `http://example.com/example.json`
+
+*   [Detection Schema](./detections.md "schema for detections") – `http://example.com/example.json`
+
+*   [Lookup Manifest](./lookups.md "A object that defines a lookup file and its properties") – `https://api.splunkresearch.com/schemas/lookups.json`
+
+*   [Macro Manifest](./macros.md "An object that defines the parameters for a Splunk Macro") – `https://api.splunkresearch.com/schemas/macros.json`
+
+*   [Response Schema](./response_tasks.md "schema for response task") – `https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json`
+
+*   [Response Schema](./responses.md "schema for response") – `https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json`
+
+*   [Response Schema](./responses_phase.md "schema for phase") – `http://example.com/example.json`
+
+## Other Schemas
+
+### Objects
+
+*   [Untitled object in Baseline Schema](./baselines-properties-tags.md "An array of key value pairs for tagging") – `#/properties/tags#/properties/tags`
+
+*   [Untitled object in Deployment Schema](./deployments-properties-alert_action.md "Set alert action parameter for search") – `#/properties/alert_action#/properties/alert_action`
+
+*   [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-email.md "By enabling it, an email is sent with the results") – `#/properties/alert_action/properties/email#/properties/alert_action/properties/email`
+
+*   [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-index.md "By enabling it, the results are stored in another index") – `#/properties/alert_action/properties/index#/properties/alert_action/properties/index`
+
+*   [Untitled object in Deployment Schema](./deployments-properties-alert_action-properties-notable.md "By enabling it, a notable is generated") – `#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable`
+
+*   [Untitled object in Deployment Schema](./deployments-properties-scheduling.md "allows to set scheduling parameter") – `#/properties/scheduling#/properties/scheduling`
+
+*   [Untitled object in Response Schema](./response_tasks-properties-automation.md "An array of key value pairs for defining actions and playbooks") – `#/properties/automation#/properties/automation`
+
+### Arrays
+
+*   [Untitled array in Detection Schema](./detections-properties-references.md "A list of references for this detection") – `#/properties/references#/properties/references`
+
+*   [Untitled array in Macro Manifest](./macros-properties-arguments.md "A list of the arguments being passed to this macro") – `https://api.splunkresearch.com/schemas/macros.json#/properties/arguments`
+
+*   [Untitled array in Response Schema](./responses-properties-response_phase.md "Response divided into phases") – `#/properties/response_phases#/properties/response_phase`
+
+*   [Untitled array in Response Schema](./responses_phase-properties-response_task.md "Response phase is divided into task(s) to be completed") – `#/properties/response_task#/properties/response_task`
diff --git a/docs/spec/baselines-properties-author.md b/docs/spec/baselines-properties-author.md
new file mode 100644
index 0000000000..25b1c05002
--- /dev/null
+++ b/docs/spec/baselines-properties-author.md
@@ -0,0 +1,22 @@
+# Untitled string in Baseline Schema Schema
+
+```txt
+#/properties/author#/properties/author
+```
+
+Author of the baseline
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## author Type
+
+`string`
+
+## author Examples
+
+```yaml
+Bahvin Patel, Splunk
+
+```
diff --git a/docs/spec/baselines-properties-date.md b/docs/spec/baselines-properties-date.md
new file mode 100644
index 0000000000..f91effacdf
--- /dev/null
+++ b/docs/spec/baselines-properties-date.md
@@ -0,0 +1,22 @@
+# Untitled string in Baseline Schema Schema
+
+```txt
+#/properties/date#/properties/date
+```
+
+date of creation or modification, format yyyy-mm-dd
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## date Type
+
+`string`
+
+## date Examples
+
+```yaml
+'2019-12-06'
+
+```
diff --git a/docs/spec/baselines-properties-description.md b/docs/spec/baselines-properties-description.md
new file mode 100644
index 0000000000..e61031299b
--- /dev/null
+++ b/docs/spec/baselines-properties-description.md
@@ -0,0 +1,26 @@
+# Untitled string in Baseline Schema Schema
+
+```txt
+#/properties/description#/properties/description
+```
+
+A detailed description of the baseline
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## description Type
+
+`string`
+
+## description Examples
+
+```yaml
+>-
+  This search looks for CloudTrail events where an AWS instance is started and
+  creates a baseline of most recent time (latest) and the first time (earliest)
+  we've seen this region in our dataset grouped by the value awsRegion for the
+  last 30 days
+
+```
diff --git a/docs/spec/baselines-properties-how_to_implement.md b/docs/spec/baselines-properties-how_to_implement.md
new file mode 100644
index 0000000000..0bac7d7e11
--- /dev/null
+++ b/docs/spec/baselines-properties-how_to_implement.md
@@ -0,0 +1,24 @@
+# Untitled string in Baseline Schema Schema
+
+```txt
+#/properties/how_to_implement#/properties/how_to_implement
+```
+
+information about how to implement. Only needed for non standard implementations.
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## how_to_implement Type
+
+`string`
+
+## how_to_implement Examples
+
+```yaml
+>-
+  This search requires Sysmon Logs and a Sysmon configuration, which includes
+  EventCode 10 for lsass.exe.
+
+```
diff --git a/docs/spec/baselines-properties-id.md b/docs/spec/baselines-properties-id.md
new file mode 100644
index 0000000000..0ac191b7fb
--- /dev/null
+++ b/docs/spec/baselines-properties-id.md
@@ -0,0 +1,22 @@
+# Untitled string in Baseline Schema Schema
+
+```txt
+#/properties/id#/properties/id
+```
+
+UUID as unique identifier
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## id Type
+
+`string`
+
+## id Examples
+
+```yaml
+fc0edc95-ff2b-48b0-9f6f-63da3789fd63
+
+```
diff --git a/docs/spec/baselines-properties-name-of-baseline.md b/docs/spec/baselines-properties-name-of-baseline.md
new file mode 100644
index 0000000000..46e7b91cba
--- /dev/null
+++ b/docs/spec/baselines-properties-name-of-baseline.md
@@ -0,0 +1,22 @@
+# Name of baseline Schema
+
+```txt
+#/properties/name#/properties/name
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## name Type
+
+`string` ([Name of baseline](baselines-properties-name-of-baseline.md))
+
+## name Examples
+
+```yaml
+Previously Seen AWS Regions
+
+```
diff --git a/docs/spec/baselines-properties-search.md b/docs/spec/baselines-properties-search.md
new file mode 100644
index 0000000000..dae22b4ca4
--- /dev/null
+++ b/docs/spec/baselines-properties-search.md
@@ -0,0 +1,24 @@
+# Untitled string in Baseline Schema Schema
+
+```txt
+#/properties/search#/properties/search
+```
+
+The Splunk search for the baseline
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## search Type
+
+`string`
+
+## search Examples
+
+```yaml
+>-
+  cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as
+  latest by awsRegion | outputlookup previously_seen_aws_regions.csv
+
+```
diff --git a/docs/spec/baselines-properties-tags-default.md b/docs/spec/baselines-properties-tags-default.md
new file mode 100644
index 0000000000..6338be86cf
--- /dev/null
+++ b/docs/spec/baselines-properties-tags-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Baseline Schema Schema
+
+```txt
+#/properties/tags#/properties/tags/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/baselines-properties-tags.md b/docs/spec/baselines-properties-tags.md
new file mode 100644
index 0000000000..622015ce22
--- /dev/null
+++ b/docs/spec/baselines-properties-tags.md
@@ -0,0 +1,47 @@
+# Untitled object in Baseline Schema Schema
+
+```txt
+#/properties/tags#/properties/tags
+```
+
+An array of key value pairs for tagging
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## tags Type
+
+`object` ([Details](baselines-properties-tags.md))
+
+## tags Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+## tags Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+## tags Examples
+
+```yaml
+analytic_story: suspicious_aws_ec2_activities
+custom_key: custom_value
+
+```
+
+# tags Properties
+
+| Property              | Type | Required | Nullable    | Defined by |
+| :-------------------- | :--- | :------- | :---------- | :--------- |
+| Additional Properties | Any  | Optional | can be null |            |
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/baselines-properties-version.md b/docs/spec/baselines-properties-version.md
new file mode 100644
index 0000000000..2d241c5920
--- /dev/null
+++ b/docs/spec/baselines-properties-version.md
@@ -0,0 +1,22 @@
+# Untitled integer in Baseline Schema Schema
+
+```txt
+#/properties/version#/properties/version
+```
+
+version of baseline, e.g. 1 or 2 ...
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [baselines.spec.json*](../../out/baselines.spec.json "open original schema") |
+
+## version Type
+
+`integer`
+
+## version Examples
+
+```yaml
+1
+
+```
diff --git a/docs/spec/baselines.md b/docs/spec/baselines.md
new file mode 100644
index 0000000000..4b120c367a
--- /dev/null
+++ b/docs/spec/baselines.md
@@ -0,0 +1,282 @@
+# Baseline Schema Schema
+
+```txt
+http://example.com/example.json
+```
+
+schema for baselines
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                  |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [baselines.spec.json](../../out/baselines.spec.json "open original schema") |
+
+## Baseline Schema Type
+
+`object` ([Baseline Schema](baselines.md))
+
+# Baseline Schema Properties
+
+| Property                              | Type      | Required | Nullable       | Defined by                                                                                                               |
+| :------------------------------------ | :-------- | :------- | :------------- | :----------------------------------------------------------------------------------------------------------------------- |
+| [author](#author)                     | `string`  | Required | cannot be null | [Baseline Schema](baselines-properties-author.md "#/properties/author#/properties/author")                               |
+| [date](#date)                         | `string`  | Required | cannot be null | [Baseline Schema](baselines-properties-date.md "#/properties/date#/properties/date")                                     |
+| [description](#description)           | `string`  | Required | cannot be null | [Baseline Schema](baselines-properties-description.md "#/properties/description#/properties/description")                |
+| [how_to_implement](#how_to_implement) | `string`  | Optional | cannot be null | [Baseline Schema](baselines-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement") |
+| [id](#id)                             | `string`  | Required | cannot be null | [Baseline Schema](baselines-properties-id.md "#/properties/id#/properties/id")                                           |
+| [name](#name)                         | `string`  | Required | cannot be null | [Baseline Schema](baselines-properties-name-of-baseline.md "#/properties/name#/properties/name")                         |
+| [search](#search)                     | `string`  | Required | cannot be null | [Baseline Schema](baselines-properties-search.md "#/properties/search#/properties/search")                               |
+| [tags](#tags)                         | `object`  | Required | cannot be null | [Baseline Schema](baselines-properties-tags.md "#/properties/tags#/properties/tags")                                     |
+| [version](#version)                   | `integer` | Required | cannot be null | [Baseline Schema](baselines-properties-version.md "#/properties/version#/properties/version")                            |
+| Additional Properties                 | Any       | Optional | can be null    |                                                                                                                          |
+
+## author
+
+Author of the baseline
+
+`author`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-author.md "#/properties/author#/properties/author")
+
+### author Type
+
+`string`
+
+### author Examples
+
+```yaml
+Bahvin Patel, Splunk
+
+```
+
+## date
+
+date of creation or modification, format yyyy-mm-dd
+
+`date`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-date.md "#/properties/date#/properties/date")
+
+### date Type
+
+`string`
+
+### date Examples
+
+```yaml
+'2019-12-06'
+
+```
+
+## description
+
+A detailed description of the baseline
+
+`description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-description.md "#/properties/description#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+>-
+  This search looks for CloudTrail events where an AWS instance is started and
+  creates a baseline of most recent time (latest) and the first time (earliest)
+  we've seen this region in our dataset grouped by the value awsRegion for the
+  last 30 days
+
+```
+
+## how_to_implement
+
+information about how to implement. Only needed for non standard implementations.
+
+`how_to_implement`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement")
+
+### how_to_implement Type
+
+`string`
+
+### how_to_implement Examples
+
+```yaml
+>-
+  This search requires Sysmon Logs and a Sysmon configuration, which includes
+  EventCode 10 for lsass.exe.
+
+```
+
+## id
+
+UUID as unique identifier
+
+`id`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-id.md "#/properties/id#/properties/id")
+
+### id Type
+
+`string`
+
+### id Examples
+
+```yaml
+fc0edc95-ff2b-48b0-9f6f-63da3789fd63
+
+```
+
+## name
+
+
+
+`name`
+
+*   is required
+
+*   Type: `string` ([Name of baseline](baselines-properties-name-of-baseline.md))
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-name-of-baseline.md "#/properties/name#/properties/name")
+
+### name Type
+
+`string` ([Name of baseline](baselines-properties-name-of-baseline.md))
+
+### name Examples
+
+```yaml
+Previously Seen AWS Regions
+
+```
+
+## search
+
+The Splunk search for the baseline
+
+`search`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-search.md "#/properties/search#/properties/search")
+
+### search Type
+
+`string`
+
+### search Examples
+
+```yaml
+>-
+  cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as
+  latest by awsRegion | outputlookup previously_seen_aws_regions.csv
+
+```
+
+## tags
+
+An array of key value pairs for tagging
+
+`tags`
+
+*   is required
+
+*   Type: `object` ([Details](baselines-properties-tags.md))
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-tags.md "#/properties/tags#/properties/tags")
+
+### tags Type
+
+`object` ([Details](baselines-properties-tags.md))
+
+### tags Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+### tags Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### tags Examples
+
+```yaml
+analytic_story: suspicious_aws_ec2_activities
+custom_key: custom_value
+
+```
+
+## version
+
+version of baseline, e.g. 1 or 2 ...
+
+`version`
+
+*   is required
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Baseline Schema](baselines-properties-version.md "#/properties/version#/properties/version")
+
+### version Type
+
+`integer`
+
+### version Examples
+
+```yaml
+1
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/baselines.spec.json b/docs/spec/baselines.spec.json
deleted file mode 100644
index c13fc47497..0000000000
--- a/docs/spec/baselines.spec.json
+++ /dev/null
@@ -1,107 +0,0 @@
-{
-  "$id": "http://example.com/example.json",
-  "$schema": "http://json-schema.org/draft-07/schema",
-  "additionalProperties": true,
-  "description": "schema for baselines",
-  "properties": {
-    "author": {
-      "$id": "#/properties/author",
-      "default": "",
-      "description": "Author of the baseline",
-      "examples": [
-        "Bahvin Patel, Splunk"
-      ],
-      "type": "string"
-    },
-    "date": {
-      "$id": "#/properties/date",
-      "default": "",
-      "description": "date of creation or modification, format yyyy-mm-dd",
-      "examples": [
-        "2019-12-06"
-      ],
-      "type": "string"
-    },
-    "description": {
-      "$id": "#/properties/description",
-      "default": "",
-      "description": "A detailed description of the baseline ",
-      "examples": [
-        "This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days"
-      ],
-      "type": "string"
-    },
-    "how_to_implement": {
-      "$id": "#/properties/how_to_implement",
-      "default": "",
-      "description": "information about how to implement. Only needed for non standard implementations.",
-      "examples": [
-        "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe."
-      ],
-      "type": "string"
-    },
-    "id": {
-      "$id": "#/properties/id",
-      "default": "",
-      "description": "UUID as unique identifier",
-      "examples": [
-        "fc0edc95-ff2b-48b0-9f6f-63da3789fd63"
-      ],
-      "type": "string"
-    },
-    "name": {
-      "$id": "#/properties/name",
-      "default": "",
-      "examples": [
-        "Previously Seen AWS Regions"
-      ],
-      "title": "Name of baseline",
-      "type": "string"
-    },
-    "search": {
-      "$id": "#/properties/search",
-      "default": "",
-      "description": "The Splunk search for the baseline",
-      "examples": [
-        "cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv"
-      ],
-      "type": "string"
-    },
-    "tags": {
-      "$id": "#/properties/tags",
-      "additionalProperties": true,
-      "default": {},
-      "description": "An array of key value pairs for tagging",
-      "examples": [
-        {
-          "analytics_story": "suspicious_aws_ec2_activities",
-          "custom_key": "custom_value"
-        }
-      ],
-      "minItems": 1,
-      "type": "object",
-      "uniqueItems": true
-    },
-    "version": {
-      "$id": "#/properties/version",
-      "default": 0,
-      "description": "version of baseline, e.g. 1 or 2 ...",
-      "examples": [
-        1
-      ],
-      "type": "integer"
-    }
-  },
-  "required": [
-    "name",
-    "id",
-    "version",
-    "date",
-    "description",
-    "author",
-    "search",
-    "tags"
-  ],
-  "title": "Baseline Schema",
-  "type": "object"
-}
diff --git a/docs/spec/baselines.spec.md b/docs/spec/baselines.spec.md
deleted file mode 100644
index fc66502080..0000000000
--- a/docs/spec/baselines.spec.md
+++ /dev/null
@@ -1,283 +0,0 @@
-
-# Baseline Schema Schema
-
-```
-http://example.com/example.json
-```
-
-schema for baselines
-
-| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In |
-|----------|------------|--------|--------------|-------------------|-----------------------|------------|
-| Can be instantiated | No | Experimental | No | Forbidden | Permitted |  |
-
-# Baseline Schema Properties
-
-| Property | Type | Required | Nullable | Default | Defined by |
-|----------|------|----------|----------|---------|------------|
-| [author](#author) | `string` | **Required**  | No | `""` | Baseline Schema (this schema) |
-| [date](#date) | `string` | **Required**  | No | `""` | Baseline Schema (this schema) |
-| [description](#description) | `string` | **Required**  | No | `""` | Baseline Schema (this schema) |
-| [how_to_implement](#how_to_implement) | `string` | Optional  | No | `""` | Baseline Schema (this schema) |
-| [id](#id) | `string` | **Required**  | No | `""` | Baseline Schema (this schema) |
-| [name](#name) | `string` | **Required**  | No | `""` | Baseline Schema (this schema) |
-| [search](#search) | `string` | **Required**  | No | `""` | Baseline Schema (this schema) |
-| [tags](#tags) | `object` | **Required**  | No | `{}` | Baseline Schema (this schema) |
-| [version](#version) | `integer` | **Required**  | No | `0` | Baseline Schema (this schema) |
-| `*` | any | Additional | Yes | this schema *allows* additional properties |
-
-## author
-
-Author of the baseline
-
-`author`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### author Type
-
-
-`string`
-
-
-
-
-
-
-### author Example
-
-```json
-"Bahvin Patel, Splunk"
-```
-
-
-## date
-
-date of creation or modification, format yyyy-mm-dd
-
-`date`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### date Type
-
-
-`string`
-
-
-
-
-
-
-### date Example
-
-```json
-"2019-12-06"
-```
-
-
-## description
-
-A detailed description of the baseline 
-
-`description`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### description Type
-
-
-`string`
-
-
-
-
-
-
-### description Example
-
-```json
-"This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days"
-```
-
-
-## how_to_implement
-
-information about how to implement. Only needed for non standard implementations.
-
-`how_to_implement`
-
-* is optional
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### how_to_implement Type
-
-
-`string`
-
-
-
-
-
-
-### how_to_implement Example
-
-```json
-"This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe."
-```
-
-
-## id
-
-UUID as unique identifier
-
-`id`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### id Type
-
-
-`string`
-
-
-
-
-
-
-### id Example
-
-```json
-"fc0edc95-ff2b-48b0-9f6f-63da3789fd63"
-```
-
-
-## name
-### Name of baseline
-
-`name`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### name Type
-
-
-`string`
-
-
-
-
-
-
-### name Example
-
-```json
-"Previously Seen AWS Regions"
-```
-
-
-## search
-
-The Splunk search for the baseline
-
-`search`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### search Type
-
-
-`string`
-
-
-
-
-
-
-### search Example
-
-```json
-"cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv"
-```
-
-
-## tags
-
-An array of key value pairs for tagging
-
-`tags`
-
-* is **required**
-* type: `object`
-* default: `{}`
-* defined in this schema
-
-### tags Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required |
-|----------|------|----------|
-
-
-
-
-### tags Example
-
-```json
-{
-  "analytics_story": "suspicious_aws_ec2_activities",
-  "custom_key": "custom_value"
-}
-```
-
-
-## version
-
-version of baseline, e.g. 1 or 2 ...
-
-`version`
-
-* is **required**
-* type: `integer`
-* default: `0`
-* defined in this schema
-
-### version Type
-
-
-`integer`
-
-
-
-
-
-
-### version Example
-
-```json
-1
-```
-
diff --git a/docs/spec/deployments-default.md b/docs/spec/deployments-default.md
new file mode 100644
index 0000000000..1a7b518209
--- /dev/null
+++ b/docs/spec/deployments-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Deployment Schema Schema
+
+```txt
+http://example.com/example.json#/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/deployments-properties-alert_action-default.md b/docs/spec/deployments-properties-alert_action-default.md
new file mode 100644
index 0000000000..55e2308c13
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Deployment Schema Schema
+
+```txt
+#/properties/alert_action#/properties/alert_action/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/deployments-properties-alert_action-properties-email-default.md b/docs/spec/deployments-properties-alert_action-properties-email-default.md
new file mode 100644
index 0000000000..c8b32006bb
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-email-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/email#/properties/alert_action/properties/email/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md
new file mode 100644
index 0000000000..e01fa30a39
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-email-properties-message.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message
+```
+
+message of email
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## message Type
+
+`string`
+
+## message Examples
+
+```yaml
+Splunk Alert $name$ triggered %fields%
+
+```
diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md
new file mode 100644
index 0000000000..e72fd29af3
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-email-properties-subject.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject
+```
+
+Subject of email
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## subject Type
+
+`string`
+
+## subject Examples
+
+```yaml
+Splunk Alert $name$
+
+```
diff --git a/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md b/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md
new file mode 100644
index 0000000000..0bef21838f
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-email-properties-to.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to
+```
+
+Recipient of email
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## to Type
+
+`string`
+
+## to Examples
+
+```yaml
+test@test.com
+
+```
diff --git a/docs/spec/deployments-properties-alert_action-properties-email.md b/docs/spec/deployments-properties-alert_action-properties-email.md
new file mode 100644
index 0000000000..070e95ee64
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-email.md
@@ -0,0 +1,120 @@
+# Untitled object in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/email#/properties/alert_action/properties/email
+```
+
+By enabling it, an email is sent with the results
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## email Type
+
+`object` ([Details](deployments-properties-alert_action-properties-email.md))
+
+## email Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+## email Examples
+
+```yaml
+message: Splunk Alert $name$ triggered %fields%
+subject: Splunk Alert $name$
+to: test@test.com
+
+```
+
+# email Properties
+
+| Property              | Type     | Required | Nullable       | Defined by                                                                                                                                                                                                                   |
+| :-------------------- | :------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [message](#message)   | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-message.md "#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message") |
+| [subject](#subject)   | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-subject.md "#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject") |
+| [to](#to)             | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email-properties-to.md "#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to")                |
+| Additional Properties | Any      | Optional | can be null    |                                                                                                                                                                                                                              |
+
+## message
+
+message of email
+
+`message`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-message.md "#/properties/alert_action/properties/email/properties/message#/properties/alert_action/properties/email/properties/message")
+
+### message Type
+
+`string`
+
+### message Examples
+
+```yaml
+Splunk Alert $name$ triggered %fields%
+
+```
+
+## subject
+
+Subject of email
+
+`subject`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-subject.md "#/properties/alert_action/properties/email/properties/subject#/properties/alert_action/properties/email/properties/subject")
+
+### subject Type
+
+`string`
+
+### subject Examples
+
+```yaml
+Splunk Alert $name$
+
+```
+
+## to
+
+Recipient of email
+
+`to`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-email-properties-to.md "#/properties/alert_action/properties/email/properties/to#/properties/alert_action/properties/email/properties/to")
+
+### to Type
+
+`string`
+
+### to Examples
+
+```yaml
+test@test.com
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/deployments-properties-alert_action-properties-index-default.md b/docs/spec/deployments-properties-alert_action-properties-index-default.md
new file mode 100644
index 0000000000..0acb5762ae
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-index-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/index#/properties/alert_action/properties/index/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md b/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md
new file mode 100644
index 0000000000..be9a681a97
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-index-properties-name.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name
+```
+
+Name of the index
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## name Type
+
+`string`
+
+## name Examples
+
+```yaml
+asx
+
+```
diff --git a/docs/spec/deployments-properties-alert_action-properties-index.md b/docs/spec/deployments-properties-alert_action-properties-index.md
new file mode 100644
index 0000000000..edc5070f0e
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-index.md
@@ -0,0 +1,66 @@
+# Untitled object in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/index#/properties/alert_action/properties/index
+```
+
+By enabling it, the results are stored in another index
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## index Type
+
+`object` ([Details](deployments-properties-alert_action-properties-index.md))
+
+## index Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+## index Examples
+
+```yaml
+name: asx
+
+```
+
+# index Properties
+
+| Property              | Type     | Required | Nullable       | Defined by                                                                                                                                                                                                          |
+| :-------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [name](#name)         | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-index-properties-name.md "#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name") |
+| Additional Properties | Any      | Optional | can be null    |                                                                                                                                                                                                                     |
+
+## name
+
+Name of the index
+
+`name`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-index-properties-name.md "#/properties/alert_action/properties/index/properties/name#/properties/alert_action/properties/index/properties/name")
+
+### name Type
+
+`string`
+
+### name Examples
+
+```yaml
+asx
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-default.md b/docs/spec/deployments-properties-alert_action-properties-notable-default.md
new file mode 100644
index 0000000000..84226f6fc7
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-notable-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md
new file mode 100644
index 0000000000..23882cd515
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_description.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description
+```
+
+Rule description of the notable event
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## rule_description Type
+
+`string`
+
+## rule_description Examples
+
+```yaml
+'%description%'
+
+```
diff --git a/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md
new file mode 100644
index 0000000000..9d5eb151c2
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-notable-properties-rule_title.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title
+```
+
+Rule title of the notable event
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## rule_title Type
+
+`string`
+
+## rule_title Examples
+
+```yaml
+'%name%'
+
+```
diff --git a/docs/spec/deployments-properties-alert_action-properties-notable.md b/docs/spec/deployments-properties-alert_action-properties-notable.md
new file mode 100644
index 0000000000..d117f24ef9
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action-properties-notable.md
@@ -0,0 +1,93 @@
+# Untitled object in Deployment Schema Schema
+
+```txt
+#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable
+```
+
+By enabling it, a notable is generated
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## notable Type
+
+`object` ([Details](deployments-properties-alert_action-properties-notable.md))
+
+## notable Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+## notable Examples
+
+```yaml
+rule_description: '%description%'
+rule_title: '%name%'
+
+```
+
+# notable Properties
+
+| Property                              | Type     | Required | Nullable       | Defined by                                                                                                                                                                                                                                                    |
+| :------------------------------------ | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [rule_description](#rule_description) | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_description.md "#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description") |
+| [rule_title](#rule_title)             | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_title.md "#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title")                   |
+| Additional Properties                 | Any      | Optional | can be null    |                                                                                                                                                                                                                                                               |
+
+## rule_description
+
+Rule description of the notable event
+
+`rule_description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_description.md "#/properties/alert_action/properties/notable/properties/rule_description#/properties/alert_action/properties/notable/properties/rule_description")
+
+### rule_description Type
+
+`string`
+
+### rule_description Examples
+
+```yaml
+'%description%'
+
+```
+
+## rule_title
+
+Rule title of the notable event
+
+`rule_title`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable-properties-rule_title.md "#/properties/alert_action/properties/notable/properties/rule_title#/properties/alert_action/properties/notable/properties/rule_title")
+
+### rule_title Type
+
+`string`
+
+### rule_title Examples
+
+```yaml
+'%name%'
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/deployments-properties-alert_action.md b/docs/spec/deployments-properties-alert_action.md
new file mode 100644
index 0000000000..f5fa09670a
--- /dev/null
+++ b/docs/spec/deployments-properties-alert_action.md
@@ -0,0 +1,153 @@
+# Untitled object in Deployment Schema Schema
+
+```txt
+#/properties/alert_action#/properties/alert_action
+```
+
+Set alert action parameter for search
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## alert_action Type
+
+`object` ([Details](deployments-properties-alert_action.md))
+
+## alert_action Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+## alert_action Examples
+
+```yaml
+email:
+  message: Splunk Alert $name$ triggered %fields%
+  subject: Splunk Alert $name$
+  to: test@test.com
+index:
+  name: asx
+notable:
+  rule_description: '%description%'
+  rule_title: '%name%'
+
+```
+
+# alert_action Properties
+
+| Property              | Type     | Required | Nullable       | Defined by                                                                                                                                                                |
+| :-------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [email](#email)       | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-email.md "#/properties/alert_action/properties/email#/properties/alert_action/properties/email")       |
+| [index](#index)       | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-index.md "#/properties/alert_action/properties/index#/properties/alert_action/properties/index")       |
+| [notable](#notable)   | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action-properties-notable.md "#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable") |
+| Additional Properties | Any      | Optional | can be null    |                                                                                                                                                                           |
+
+## email
+
+By enabling it, an email is sent with the results
+
+`email`
+
+*   is optional
+
+*   Type: `object` ([Details](deployments-properties-alert_action-properties-email.md))
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-email.md "#/properties/alert_action/properties/email#/properties/alert_action/properties/email")
+
+### email Type
+
+`object` ([Details](deployments-properties-alert_action-properties-email.md))
+
+### email Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### email Examples
+
+```yaml
+message: Splunk Alert $name$ triggered %fields%
+subject: Splunk Alert $name$
+to: test@test.com
+
+```
+
+## index
+
+By enabling it, the results are stored in another index
+
+`index`
+
+*   is optional
+
+*   Type: `object` ([Details](deployments-properties-alert_action-properties-index.md))
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-index.md "#/properties/alert_action/properties/index#/properties/alert_action/properties/index")
+
+### index Type
+
+`object` ([Details](deployments-properties-alert_action-properties-index.md))
+
+### index Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### index Examples
+
+```yaml
+name: asx
+
+```
+
+## notable
+
+By enabling it, a notable is generated
+
+`notable`
+
+*   is optional
+
+*   Type: `object` ([Details](deployments-properties-alert_action-properties-notable.md))
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action-properties-notable.md "#/properties/alert_action/properties/notable#/properties/alert_action/properties/notable")
+
+### notable Type
+
+`object` ([Details](deployments-properties-alert_action-properties-notable.md))
+
+### notable Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### notable Examples
+
+```yaml
+rule_description: '%description%'
+rule_title: '%name%'
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/deployments-properties-scheduling-default.md b/docs/spec/deployments-properties-scheduling-default.md
new file mode 100644
index 0000000000..f39294b0a3
--- /dev/null
+++ b/docs/spec/deployments-properties-scheduling-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Deployment Schema Schema
+
+```txt
+#/properties/scheduling#/properties/scheduling/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md b/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md
new file mode 100644
index 0000000000..52cbbfae51
--- /dev/null
+++ b/docs/spec/deployments-properties-scheduling-properties-cron_schedule.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule
+```
+
+Cron schedule to schedule the Splunk searches.
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## cron_schedule Type
+
+`string`
+
+## cron_schedule Examples
+
+```yaml
+'*/10 * * * *'
+
+```
diff --git a/docs/spec/deployments-properties-scheduling-properties-earliest_time.md b/docs/spec/deployments-properties-scheduling-properties-earliest_time.md
new file mode 100644
index 0000000000..5808ddd97f
--- /dev/null
+++ b/docs/spec/deployments-properties-scheduling-properties-earliest_time.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time
+```
+
+earliest time of search
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## earliest_time Type
+
+`string`
+
+## earliest_time Examples
+
+```yaml
+'-10m'
+
+```
diff --git a/docs/spec/deployments-properties-scheduling-properties-latest_time.md b/docs/spec/deployments-properties-scheduling-properties-latest_time.md
new file mode 100644
index 0000000000..566141825e
--- /dev/null
+++ b/docs/spec/deployments-properties-scheduling-properties-latest_time.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time
+```
+
+latest time of search
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## latest_time Type
+
+`string`
+
+## latest_time Examples
+
+```yaml
+now
+
+```
diff --git a/docs/spec/deployments-properties-scheduling-properties-schedule_window.md b/docs/spec/deployments-properties-scheduling-properties-schedule_window.md
new file mode 100644
index 0000000000..41aad873d4
--- /dev/null
+++ b/docs/spec/deployments-properties-scheduling-properties-schedule_window.md
@@ -0,0 +1,22 @@
+# Untitled string in Deployment Schema Schema
+
+```txt
+#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window
+```
+
+schedule window for search
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## schedule_window Type
+
+`string`
+
+## schedule_window Examples
+
+```yaml
+auto
+
+```
diff --git a/docs/spec/deployments-properties-scheduling.md b/docs/spec/deployments-properties-scheduling.md
new file mode 100644
index 0000000000..cf64fb3727
--- /dev/null
+++ b/docs/spec/deployments-properties-scheduling.md
@@ -0,0 +1,147 @@
+# Untitled object in Deployment Schema Schema
+
+```txt
+#/properties/scheduling#/properties/scheduling
+```
+
+allows to set scheduling parameter
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                       |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [deployments.spec.json*](../../out/deployments.spec.json "open original schema") |
+
+## scheduling Type
+
+`object` ([Details](deployments-properties-scheduling.md))
+
+## scheduling Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+## scheduling Examples
+
+```yaml
+cron_schedule: '*/10 * * * *'
+earliest_time: '-10m'
+latest_time: now
+schedule_window: auto
+
+```
+
+# scheduling Properties
+
+| Property                            | Type     | Required | Nullable       | Defined by                                                                                                                                                                                  |
+| :---------------------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [cron_schedule](#cron_schedule)     | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-cron_schedule.md "#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule")       |
+| [earliest_time](#earliest_time)     | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-earliest_time.md "#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time")       |
+| [latest_time](#latest_time)         | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-latest_time.md "#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time")             |
+| [schedule_window](#schedule_window) | `string` | Optional | cannot be null | [Deployment Schema](deployments-properties-scheduling-properties-schedule_window.md "#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window") |
+| Additional Properties               | Any      | Optional | can be null    |                                                                                                                                                                                             |
+
+## cron_schedule
+
+Cron schedule to schedule the Splunk searches.
+
+`cron_schedule`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-scheduling-properties-cron_schedule.md "#/properties/scheduling/properties/cron_schedule#/properties/scheduling/properties/cron_schedule")
+
+### cron_schedule Type
+
+`string`
+
+### cron_schedule Examples
+
+```yaml
+'*/10 * * * *'
+
+```
+
+## earliest_time
+
+earliest time of search
+
+`earliest_time`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-scheduling-properties-earliest_time.md "#/properties/scheduling/properties/earliest_time#/properties/scheduling/properties/earliest_time")
+
+### earliest_time Type
+
+`string`
+
+### earliest_time Examples
+
+```yaml
+'-10m'
+
+```
+
+## latest_time
+
+latest time of search
+
+`latest_time`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-scheduling-properties-latest_time.md "#/properties/scheduling/properties/latest_time#/properties/scheduling/properties/latest_time")
+
+### latest_time Type
+
+`string`
+
+### latest_time Examples
+
+```yaml
+now
+
+```
+
+## schedule_window
+
+schedule window for search
+
+`schedule_window`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-scheduling-properties-schedule_window.md "#/properties/scheduling/properties/schedule_window#/properties/scheduling/properties/schedule_window")
+
+### schedule_window Type
+
+`string`
+
+### schedule_window Examples
+
+```yaml
+auto
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/deployments.md b/docs/spec/deployments.md
new file mode 100644
index 0000000000..4d1597e99c
--- /dev/null
+++ b/docs/spec/deployments.md
@@ -0,0 +1,258 @@
+# Deployment Schema Schema
+
+```txt
+http://example.com/example.json
+```
+
+schema for deployment
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                      |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------ |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [deployments.spec.json](../../out/deployments.spec.json "open original schema") |
+
+## Deployment Schema Type
+
+`object` ([Deployment Schema](deployments.md))
+
+## Deployment Schema Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+# Deployment Schema Properties
+
+| Property                      | Type     | Required | Nullable       | Defined by                                                                                                       |
+| :---------------------------- | :------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------- |
+| [alert_action](#alert_action) | `object` | Optional | cannot be null | [Deployment Schema](deployments-properties-alert_action.md "#/properties/alert_action#/properties/alert_action") |
+| [date](#date)                 | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-date.md "#/properties/date#/properties/date")                         |
+| [description](#description)   | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-description.md "#/properties/description#/properties/description")    |
+| [id](#id)                     | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-id.md "#/properties/id#/properties/id")                               |
+| [name](#name)                 | `string` | Required | cannot be null | [Deployment Schema](deployments-properties-name.md "#/properties/name#/properties/name")                         |
+| [scheduling](#scheduling)     | `object` | Required | cannot be null | [Deployment Schema](deployments-properties-scheduling.md "#/properties/scheduling#/properties/scheduling")       |
+| [tags](#tags)                 | `object` | Required | cannot be null | [Deployment Schema](deployments-properties-tags.md "#/properties/tags#/properties/tags")                         |
+| Additional Properties         | Any      | Optional | can be null    |                                                                                                                  |
+
+## alert_action
+
+Set alert action parameter for search
+
+`alert_action`
+
+*   is optional
+
+*   Type: `object` ([Details](deployments-properties-alert_action.md))
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-alert_action.md "#/properties/alert_action#/properties/alert_action")
+
+### alert_action Type
+
+`object` ([Details](deployments-properties-alert_action.md))
+
+### alert_action Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### alert_action Examples
+
+```yaml
+email:
+  message: Splunk Alert $name$ triggered %fields%
+  subject: Splunk Alert $name$
+  to: test@test.com
+index:
+  name: asx
+notable:
+  rule_description: '%description%'
+  rule_title: '%name%'
+
+```
+
+## date
+
+date of creation or modification, format yyyy-mm-dd
+
+`date`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-date.md "#/properties/date#/properties/date")
+
+### date Type
+
+`string`
+
+### date Examples
+
+```yaml
+'2019-12-06'
+
+```
+
+## description
+
+description of the deployment configuration
+
+`description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-description.md "#/properties/description#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+>-
+  This deployment configuration provides a standard scheduling policy over all
+  rules.
+
+```
+
+## id
+
+uuid as unique identifier
+
+`id`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-id.md "#/properties/id#/properties/id")
+
+### id Type
+
+`string`
+
+### id Examples
+
+```yaml
+fb4c31b0-13e8-4155-8aa5-24de4b8d6717
+
+```
+
+## name
+
+Name of deployment configuration
+
+`name`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-name.md "#/properties/name#/properties/name")
+
+### name Type
+
+`string`
+
+### name Examples
+
+```yaml
+Deployment Configuration all Detections
+
+```
+
+## scheduling
+
+allows to set scheduling parameter
+
+`scheduling`
+
+*   is required
+
+*   Type: `object` ([Details](deployments-properties-scheduling.md))
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-scheduling.md "#/properties/scheduling#/properties/scheduling")
+
+### scheduling Type
+
+`object` ([Details](deployments-properties-scheduling.md))
+
+### scheduling Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### scheduling Examples
+
+```yaml
+cron_schedule: '*/10 * * * *'
+earliest_time: '-10m'
+latest_time: now
+schedule_window: auto
+
+```
+
+## tags
+
+An array of key value pairs for tagging
+
+`tags`
+
+*   is required
+
+*   Type: `object` ([Details](deployments-properties-tags.md))
+
+*   cannot be null
+
+*   defined in: [Deployment Schema](deployments-properties-tags.md "#/properties/tags#/properties/tags")
+
+### tags Type
+
+`object` ([Details](deployments-properties-tags.md))
+
+### tags Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+### tags Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### tags Examples
+
+```yaml
+analytic_story: credential_dumping
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/deployments.spec.json b/docs/spec/deployments.spec.json
deleted file mode 100644
index c417cc0bf3..0000000000
--- a/docs/spec/deployments.spec.json
+++ /dev/null
@@ -1,264 +0,0 @@
-{
-  "$id": "http://example.com/example.json",
-  "$schema": "http://json-schema.org/draft-07/schema",
-  "additionalProperties": true,
-  "default": {},
-  "description": "schema for deployment",
-  "properties": {
-    "alert_action": {
-      "$id": "#/properties/alert_action",
-      "additionalProperties": true,
-      "default": {},
-      "description": "Set alert action parameter for search",
-      "examples": [
-        {
-          "email": {
-            "message": "Splunk Alert $name$ triggered %fields%",
-            "subject": "Splunk Alert $name$",
-            "to": "test@test.com"
-          },
-          "index": {
-            "name": "asx"
-          },
-          "notable": {
-            "rule_description": "%description%",
-            "rule_title": "%name%"
-          }
-        }
-      ],
-      "properties": {
-        "email": {
-          "$id": "#/properties/alert_action/properties/email",
-          "additionalProperties": true,
-          "default": {},
-          "description": "By enabling it, an email is sent with the results",
-          "examples": [
-            {
-              "message": "Splunk Alert $name$ triggered %fields%",
-              "subject": "Splunk Alert $name$",
-              "to": "test@test.com"
-            }
-          ],
-          "properties": {
-            "message": {
-              "$id": "#/properties/alert_action/properties/email/properties/message",
-              "default": "",
-              "description": "message of email",
-              "examples": [
-                "Splunk Alert $name$ triggered %fields%"
-              ],
-              "type": "string"
-            },
-            "subject": {
-              "$id": "#/properties/alert_action/properties/email/properties/subject",
-              "default": "",
-              "description": "Subject of email",
-              "examples": [
-                "Splunk Alert $name$"
-              ],
-              "type": "string"
-            },
-            "to": {
-              "$id": "#/properties/alert_action/properties/email/properties/to",
-              "default": "",
-              "description": "Recipient of email",
-              "examples": [
-                "test@test.com"
-              ],
-              "type": "string"
-            }
-          },
-          "required": [
-            "to",
-            "subject",
-            "message"
-          ],
-          "type": "object"
-        },
-        "index": {
-          "$id": "#/properties/alert_action/properties/index",
-          "additionalProperties": true,
-          "default": {},
-          "description": "By enabling it, the results are stored in another index",
-          "examples": [
-            {
-              "name": "asx"
-            }
-          ],
-          "properties": {
-            "name": {
-              "$id": "#/properties/alert_action/properties/index/properties/name",
-              "default": "",
-              "description": "Name of the index",
-              "examples": [
-                "asx"
-              ],
-              "type": "string"
-            }
-          },
-          "required": [
-            "name"
-          ],
-          "type": "object"
-        },
-        "notable": {
-          "$id": "#/properties/alert_action/properties/notable",
-          "additionalProperties": true,
-          "default": {},
-          "description": "By enabling it, a notable is generated",
-          "examples": [
-            {
-              "rule_description": "%description%",
-              "rule_title": "%name%"
-            }
-          ],
-          "properties": {
-            "rule_description": {
-              "$id": "#/properties/alert_action/properties/notable/properties/rule_description",
-              "default": "",
-              "description": "Rule description of the notable event",
-              "examples": [
-                "%description%"
-              ],
-              "type": "string"
-            },
-            "rule_title": {
-              "$id": "#/properties/alert_action/properties/notable/properties/rule_title",
-              "default": "",
-              "description": "Rule title of the notable event",
-              "examples": [
-                "%name%"
-              ],
-              "type": "string"
-            }
-          },
-          "required": [
-            "rule_title",
-            "rule_description"
-          ],
-          "type": "object"
-        }
-      },
-      "type": "object"
-    },
-    "date": {
-      "$id": "#/properties/date",
-      "default": "",
-      "description": "date of creation or modification, format yyyy-mm-dd",
-      "examples": [
-        "2019-12-06"
-      ],
-      "type": "string"
-    },
-    "description": {
-      "$id": "#/properties/description",
-      "default": "",
-      "description": "description of the deployment configuration",
-      "examples": [
-        "This deployment configuration provides a standard scheduling policy over all rules."
-      ],
-      "type": "string"
-    },
-    "id": {
-      "$id": "#/properties/id",
-      "default": "",
-      "description": "uuid as unique identifier",
-      "examples": [
-        "fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-      ],
-      "type": "string"
-    },
-    "name": {
-      "$id": "#/properties/name",
-      "default": "",
-      "description": "Name of deployment configuration",
-      "examples": [
-        "Deployment Configuration all Detections"
-      ],
-      "type": "string"
-    },
-    "scheduling": {
-      "$id": "#/properties/scheduling",
-      "additionalProperties": true,
-      "default": {},
-      "description": "allows to set scheduling parameter",
-      "examples": [
-        {
-          "cron_schedule": "*/10 * * * *",
-          "earliest_time": "-10m",
-          "latest_time": "now",
-          "schedule_window": "auto"
-        }
-      ],
-      "properties": {
-        "cron_schedule": {
-          "$id": "#/properties/scheduling/properties/cron_schedule",
-          "default": "",
-          "description": "Cron schedule to schedule the Splunk searches.",
-          "examples": [
-            "*/10 * * * *"
-          ],
-          "type": "string"
-        },
-        "earliest_time": {
-          "$id": "#/properties/scheduling/properties/earliest_time",
-          "default": "",
-          "description": "earliest time of search",
-          "examples": [
-            "-10m"
-          ],
-          "type": "string"
-        },
-        "latest_time": {
-          "$id": "#/properties/scheduling/properties/latest_time",
-          "default": "",
-          "description": "latest time of search",
-          "examples": [
-            "now"
-          ],
-          "type": "string"
-        },
-        "schedule_window": {
-          "$id": "#/properties/scheduling/properties/schedule_window",
-          "default": "",
-          "description": "schedule window for search",
-          "examples": [
-            "auto"
-          ],
-          "type": "string"
-        }
-      },
-      "required": [
-        "cron_schedule",
-        "earliest_time",
-        "latest_time"
-      ],
-      "type": "object"
-    },
-    "tags": {
-      "$id": "#/properties/tags",
-      "additionalProperties": true,
-      "default": {},
-      "description": "An array of key value pairs for tagging",
-      "examples": [
-        {
-          "analytics_story": "credential_dumping"
-        }
-      ],
-      "minItems": 1,
-      "type": "object",
-      "uniqueItems": true
-    }
-  },
-  "required": [
-    "name",
-    "id",
-    "date",
-    "description",
-    "scheduling",
-    "alert_action",
-    "tags"
-  ],
-  "title": "Deployment Schema",
-  "type": "object"
-}
diff --git a/docs/spec/deployments.spec.md b/docs/spec/deployments.spec.md
deleted file mode 100644
index 8d86ed47ff..0000000000
--- a/docs/spec/deployments.spec.md
+++ /dev/null
@@ -1,656 +0,0 @@
-
-# Deployment Schema Schema
-
-```
-http://example.com/example.json
-```
-
-schema for deployment
-
-| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In |
-|----------|------------|--------|--------------|-------------------|-----------------------|------------|
-| Can be instantiated | No | Experimental | No | Forbidden | Permitted |  |
-
-# Deployment Schema Properties
-
-| Property | Type | Required | Nullable | Default | Defined by |
-|----------|------|----------|----------|---------|------------|
-| [alert_action](#alert_action) | `object` | **Required**  | No | `{}` | Deployment Schema (this schema) |
-| [date](#date) | `string` | **Required**  | No | `""` | Deployment Schema (this schema) |
-| [description](#description) | `string` | **Required**  | No | `""` | Deployment Schema (this schema) |
-| [id](#id) | `string` | **Required**  | No | `""` | Deployment Schema (this schema) |
-| [name](#name) | `string` | **Required**  | No | `""` | Deployment Schema (this schema) |
-| [scheduling](#scheduling) | `object` | **Required**  | No | `{}` | Deployment Schema (this schema) |
-| [tags](#tags) | `object` | **Required**  | No | `{}` | Deployment Schema (this schema) |
-| `*` | any | Additional | Yes | this schema *allows* additional properties |
-
-## alert_action
-
-Set alert action parameter for search
-
-`alert_action`
-
-* is **required**
-* type: `object`
-* default: `{}`
-* defined in this schema
-
-### alert_action Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required | Default |
-|----------|------|----------|---------|
-| `email`| object | Optional | `{}` |
-| `index`| object | Optional | `{}` |
-| `notable`| object | Optional | `{}` |
-
-
-
-#### email
-
-By enabling it, an email is sent with the results
-
-`email`
-
-* is optional
-* type: `object`
-* default: `{}`
-
-
-##### email Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required | Default |
-|----------|------|----------|---------|
-| `message`| string | **Required** | `""` |
-| `subject`| string | **Required** | `""` |
-| `to`| string | **Required** | `""` |
-
-
-
-#### message
-
-message of email
-
-`message`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### message Type
-
-
-`string`
-
-
-
-
-
-
-##### message Example
-
-```json
-Splunk Alert $name$ triggered %fields%
-```
-
-
-
-
-#### subject
-
-Subject of email
-
-`subject`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### subject Type
-
-
-`string`
-
-
-
-
-
-
-##### subject Example
-
-```json
-Splunk Alert $name$
-```
-
-
-
-
-#### to
-
-Recipient of email
-
-`to`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### to Type
-
-
-`string`
-
-
-
-
-
-
-##### to Example
-
-```json
-test@test.com
-```
-
-
-
-
-
-##### email Example
-
-```json
-[object Object]
-```
-
-
-
-
-#### index
-
-By enabling it, the results are stored in another index
-
-`index`
-
-* is optional
-* type: `object`
-* default: `{}`
-
-
-##### index Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required | Default |
-|----------|------|----------|---------|
-| `name`| string | **Required** | `""` |
-
-
-
-#### name
-
-Name of the index
-
-`name`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### name Type
-
-
-`string`
-
-
-
-
-
-
-##### name Example
-
-```json
-asx
-```
-
-
-
-
-
-##### index Example
-
-```json
-[object Object]
-```
-
-
-
-
-#### notable
-
-By enabling it, a notable is generated
-
-`notable`
-
-* is optional
-* type: `object`
-* default: `{}`
-
-
-##### notable Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required | Default |
-|----------|------|----------|---------|
-| `rule_description`| string | **Required** | `""` |
-| `rule_title`| string | **Required** | `""` |
-
-
-
-#### rule_description
-
-Rule description of the notable event
-
-`rule_description`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### rule_description Type
-
-
-`string`
-
-
-
-
-
-
-##### rule_description Example
-
-```json
-%description%
-```
-
-
-
-
-#### rule_title
-
-Rule title of the notable event
-
-`rule_title`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### rule_title Type
-
-
-`string`
-
-
-
-
-
-
-##### rule_title Example
-
-```json
-%name%
-```
-
-
-
-
-
-##### notable Example
-
-```json
-[object Object]
-```
-
-
-
-
-
-### alert_action Example
-
-```json
-{
-  "email": {
-    "message": "Splunk Alert $name$ triggered %fields%",
-    "subject": "Splunk Alert $name$",
-    "to": "test@test.com"
-  },
-  "index": {
-    "name": "asx"
-  },
-  "notable": {
-    "rule_description": "%description%",
-    "rule_title": "%name%"
-  }
-}
-```
-
-
-## date
-
-date of creation or modification, format yyyy-mm-dd
-
-`date`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### date Type
-
-
-`string`
-
-
-
-
-
-
-### date Example
-
-```json
-"2019-12-06"
-```
-
-
-## description
-
-description of the deployment configuration
-
-`description`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### description Type
-
-
-`string`
-
-
-
-
-
-
-### description Example
-
-```json
-"This deployment configuration provides a standard scheduling policy over all rules."
-```
-
-
-## id
-
-uuid as unique identifier
-
-`id`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### id Type
-
-
-`string`
-
-
-
-
-
-
-### id Example
-
-```json
-"fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-```
-
-
-## name
-
-Name of deployment configuration
-
-`name`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### name Type
-
-
-`string`
-
-
-
-
-
-
-### name Example
-
-```json
-"Deployment Configuration all Detections"
-```
-
-
-## scheduling
-
-allows to set scheduling parameter
-
-`scheduling`
-
-* is **required**
-* type: `object`
-* default: `{}`
-* defined in this schema
-
-### scheduling Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required | Default |
-|----------|------|----------|---------|
-| `cron_schedule`| string | **Required** | `""` |
-| `earliest_time`| string | **Required** | `""` |
-| `latest_time`| string | **Required** | `""` |
-| `schedule_window`| string | Optional | `""` |
-
-
-
-#### cron_schedule
-
-Cron schedule to schedule the Splunk searches.
-
-`cron_schedule`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### cron_schedule Type
-
-
-`string`
-
-
-
-
-
-
-##### cron_schedule Example
-
-```json
-*/10 * * * *
-```
-
-
-
-
-#### earliest_time
-
-earliest time of search
-
-`earliest_time`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### earliest_time Type
-
-
-`string`
-
-
-
-
-
-
-##### earliest_time Example
-
-```json
--10m
-```
-
-
-
-
-#### latest_time
-
-latest time of search
-
-`latest_time`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### latest_time Type
-
-
-`string`
-
-
-
-
-
-
-##### latest_time Example
-
-```json
-now
-```
-
-
-
-
-#### schedule_window
-
-schedule window for search
-
-`schedule_window`
-
-* is optional
-* type: `string`
-* default: `""`
-
-
-##### schedule_window Type
-
-
-`string`
-
-
-
-
-
-
-##### schedule_window Example
-
-```json
-auto
-```
-
-
-
-
-
-### scheduling Example
-
-```json
-{
-  "cron_schedule": "*/10 * * * *",
-  "earliest_time": "-10m",
-  "latest_time": "now",
-  "schedule_window": "auto"
-}
-```
-
-
-## tags
-
-An array of key value pairs for tagging
-
-`tags`
-
-* is **required**
-* type: `object`
-* default: `{}`
-* defined in this schema
-
-### tags Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required |
-|----------|------|----------|
-
-
-
-
-### tags Example
-
-```json
-{
-  "analytics_story": "credential_dumping"
-}
-```
-
diff --git a/docs/spec/detections-properties-known_false_positives.md b/docs/spec/detections-properties-known_false_positives.md
new file mode 100644
index 0000000000..ae7c1bafbd
--- /dev/null
+++ b/docs/spec/detections-properties-known_false_positives.md
@@ -0,0 +1,24 @@
+# Untitled string in Detection Schema Schema
+
+```txt
+#/properties/knwon_false_positives#/properties/known_false_positives
+```
+
+known false postives
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                     |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [detections.spec.json*](../../out/detections.spec.json "open original schema") |
+
+## known_false_positives Type
+
+`string`
+
+## known_false_positives Examples
+
+```yaml
+>-
+  Administrators can create memory dumps for debugging purposes, but memory
+  dumps of the LSASS process would be unusual.
+
+```
diff --git a/docs/spec/detections-properties-references-the-items-schema.md b/docs/spec/detections-properties-references-the-items-schema.md
new file mode 100644
index 0000000000..c184428f27
--- /dev/null
+++ b/docs/spec/detections-properties-references-the-items-schema.md
@@ -0,0 +1,23 @@
+# The Items Schema Schema
+
+```txt
+#/properties/references/items#/properties/references/items
+```
+
+An explanation about the purpose of this instance.
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                     |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [detections.spec.json*](../../out/detections.spec.json "open original schema") |
+
+## items Type
+
+`string` ([The Items Schema](detections-properties-references-the-items-schema.md))
+
+## items Examples
+
+```yaml
+>-
+  https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
+```
diff --git a/docs/spec/detections-properties-references.md b/docs/spec/detections-properties-references.md
new file mode 100644
index 0000000000..07618fc3f0
--- /dev/null
+++ b/docs/spec/detections-properties-references.md
@@ -0,0 +1,31 @@
+# Untitled array in Detection Schema Schema
+
+```txt
+#/properties/references#/properties/references
+```
+
+A list of references for this detection
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                     |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [detections.spec.json*](../../out/detections.spec.json "open original schema") |
+
+## references Type
+
+`string[]` ([The Items Schema](detections-properties-references-the-items-schema.md))
+
+## references Default Value
+
+The default value is:
+
+```json
+[]
+```
+
+## references Examples
+
+```yaml
+- >-
+  https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
+```
diff --git a/docs/spec/detections-properties-type-items.md b/docs/spec/detections-properties-type-items.md
new file mode 100644
index 0000000000..226980c0e6
--- /dev/null
+++ b/docs/spec/detections-properties-type-items.md
@@ -0,0 +1,24 @@
+# Untitled string in Detection Schema Schema
+
+```txt
+#/properties/type#/properties/type/items
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                     |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [detections.spec.json*](../../out/detections.spec.json "open original schema") |
+
+## items Type
+
+`string`
+
+## items Constraints
+
+**enum**: the value of this property must be equal to one of the following values:
+
+| Value         | Explanation |
+| :------------ | :---------- |
+| `"batch"`     |             |
+| `"streaming"` |             |
diff --git a/docs/spec/detections-properties-type.md b/docs/spec/detections-properties-type.md
new file mode 100644
index 0000000000..98c36f20ac
--- /dev/null
+++ b/docs/spec/detections-properties-type.md
@@ -0,0 +1,22 @@
+# Untitled string in Detection Schema Schema
+
+```txt
+#/properties/type#/properties/type
+```
+
+type of detection
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                     |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [detections.spec.json*](../../out/detections.spec.json "open original schema") |
+
+## type Type
+
+`string`
+
+## type Examples
+
+```yaml
+streaming
+
+```
diff --git a/docs/spec/detections.md b/docs/spec/detections.md
new file mode 100644
index 0000000000..a07f254a09
--- /dev/null
+++ b/docs/spec/detections.md
@@ -0,0 +1,384 @@
+# Detection Schema Schema
+
+```txt
+http://example.com/example.json
+```
+
+schema for detections
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                    |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [detections.spec.json](../../out/detections.spec.json "open original schema") |
+
+## Detection Schema Type
+
+`object` ([Detection Schema](detections.md))
+
+# Detection Schema Properties
+
+| Property                                        | Type      | Required | Nullable       | Defined by                                                                                                                                |
+| :---------------------------------------------- | :-------- | :------- | :------------- | :---------------------------------------------------------------------------------------------------------------------------------------- |
+| [author](#author)                               | `string`  | Required | cannot be null | [Detection Schema](detections-properties-author.md "#/properties/author#/properties/author")                                              |
+| [date](#date)                                   | `string`  | Required | cannot be null | [Detection Schema](detections-properties-date.md "#/properties/date#/properties/date")                                                    |
+| [description](#description)                     | `string`  | Required | cannot be null | [Detection Schema](detections-properties-description.md "#/properties/description#/properties/description")                               |
+| [how_to_implement](#how_to_implement)           | `string`  | Optional | cannot be null | [Detection Schema](detections-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement")                |
+| [id](#id)                                       | `string`  | Required | cannot be null | [Detection Schema](detections-properties-id.md "#/properties/id#/properties/id")                                                          |
+| [known_false_positives](#known_false_positives) | `string`  | Required | cannot be null | [Detection Schema](detections-properties-known_false_positives.md "#/properties/knwon_false_positives#/properties/known_false_positives") |
+| [name](#name)                                   | `string`  | Required | cannot be null | [Detection Schema](detections-properties-name-of-detection.md "#/properties/name#/properties/name")                                       |
+| [references](#references)                       | `array`   | Optional | cannot be null | [Detection Schema](detections-properties-references.md "#/properties/references#/properties/references")                                  |
+| [search](#search)                               | `string`  | Required | cannot be null | [Detection Schema](detections-properties-search.md "#/properties/search#/properties/search")                                              |
+| [tags](#tags)                                   | `object`  | Required | cannot be null | [Detection Schema](detections-properties-tags.md "#/properties/tags#/properties/tags")                                                    |
+| [type](#type)                                   | `string`  | Required | cannot be null | [Detection Schema](detections-properties-type.md "#/properties/type#/properties/type")                                                    |
+| [version](#version)                             | `integer` | Required | cannot be null | [Detection Schema](detections-properties-version.md "#/properties/version#/properties/version")                                           |
+| Additional Properties                           | Any       | Optional | can be null    |                                                                                                                                           |
+
+## author
+
+Author of the detection
+
+`author`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-author.md "#/properties/author#/properties/author")
+
+### author Type
+
+`string`
+
+### author Examples
+
+```yaml
+Patrick Bareiss, Splunk
+
+```
+
+## date
+
+date of creation or modification, format yyyy-mm-dd
+
+`date`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-date.md "#/properties/date#/properties/date")
+
+### date Type
+
+`string`
+
+### date Examples
+
+```yaml
+'2019-12-06'
+
+```
+
+## description
+
+A detailed description of the detection
+
+`description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-description.md "#/properties/description#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+>-
+  dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain
+  a memory dump of a process. This search detects the usage of this DLL for
+  creating a memory dump of LSASS process. Memory dumps of the LSASS process can
+  be created with tools such as Windows Task Manager or procdump.
+
+```
+
+## how_to_implement
+
+information about how to implement. Only needed for non standard implementations.
+
+`how_to_implement`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-how_to_implement.md "#/properties/how_to_implement#/properties/how_to_implement")
+
+### how_to_implement Type
+
+`string`
+
+### how_to_implement Examples
+
+```yaml
+>-
+  This search requires Sysmon Logs and a Sysmon configuration, which includes
+  EventCode 10 for lsass.exe.
+
+```
+
+## id
+
+UUID as unique identifier
+
+`id`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-id.md "#/properties/id#/properties/id")
+
+### id Type
+
+`string`
+
+### id Examples
+
+```yaml
+fb4c31b0-13e8-4155-8aa5-24de4b8d6717
+
+```
+
+## known_false_positives
+
+known false postives
+
+`known_false_positives`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-known_false_positives.md "#/properties/knwon_false_positives#/properties/known_false_positives")
+
+### known_false_positives Type
+
+`string`
+
+### known_false_positives Examples
+
+```yaml
+>-
+  Administrators can create memory dumps for debugging purposes, but memory
+  dumps of the LSASS process would be unusual.
+
+```
+
+## name
+
+
+
+`name`
+
+*   is required
+
+*   Type: `string` ([Name of detection](detections-properties-name-of-detection.md))
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-name-of-detection.md "#/properties/name#/properties/name")
+
+### name Type
+
+`string` ([Name of detection](detections-properties-name-of-detection.md))
+
+### name Examples
+
+```yaml
+Access LSASS Memory for Dump Creation
+
+```
+
+## references
+
+A list of references for this detection
+
+`references`
+
+*   is optional
+
+*   Type: `string[]` ([The Items Schema](detections-properties-references-the-items-schema.md))
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-references.md "#/properties/references#/properties/references")
+
+### references Type
+
+`string[]` ([The Items Schema](detections-properties-references-the-items-schema.md))
+
+### references Default Value
+
+The default value is:
+
+```json
+[]
+```
+
+### references Examples
+
+```yaml
+- >-
+  https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
+```
+
+## search
+
+The Splunk search for the detection
+
+`search`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-search.md "#/properties/search#/properties/search")
+
+### search Type
+
+`string`
+
+### search Examples
+
+```yaml
+>-
+  `sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR
+  CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as
+  lastTime by Computer, TargetImage, TargetProcessId, SourceImage,
+  SourceProcessId | rename Computer as dest |
+  `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |
+  `access_lsass_memory_for_dump_creation_filter`
+
+```
+
+## tags
+
+An array of key value pairs for tagging
+
+`tags`
+
+*   is required
+
+*   Type: `object` ([Details](detections-properties-tags.md))
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-tags.md "#/properties/tags#/properties/tags")
+
+### tags Type
+
+`object` ([Details](detections-properties-tags.md))
+
+### tags Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+### tags Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### tags Examples
+
+```yaml
+analytic_story: credential_dumping
+kill_chain_phases: Action on Objectives
+mitre_attack_id: T1078.004
+cis20: CIS 13
+nist: DE.DP
+security domain: network
+asset_type: AWS Instance
+risk_object: user
+risk_object_type: network_artifacts
+risk score: '60'
+custom_key: custom_value
+
+```
+
+## type
+
+type of detection
+
+`type`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-type.md "#/properties/type#/properties/type")
+
+### type Type
+
+`string`
+
+### type Examples
+
+```yaml
+streaming
+
+```
+
+## version
+
+version of detection, e.g. 1 or 2 ...
+
+`version`
+
+*   is required
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Detection Schema](detections-properties-version.md "#/properties/version#/properties/version")
+
+### version Type
+
+`integer`
+
+### version Examples
+
+```yaml
+2
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/detections.spec.json b/docs/spec/detections.spec.json
deleted file mode 100644
index 9f9fc25482..0000000000
--- a/docs/spec/detections.spec.json
+++ /dev/null
@@ -1,157 +0,0 @@
-{
-  "$id": "http://example.com/example.json",
-  "$schema": "http://json-schema.org/draft-07/schema",
-  "additionalProperties": true,
-  "description": "schema for detections",
-  "properties": {
-    "author": {
-      "$id": "#/properties/author",
-      "default": "",
-      "description": "Author of the detection",
-      "examples": [
-        "Patrick Bareiss, Splunk"
-      ],
-      "type": "string"
-    },
-    "date": {
-      "$id": "#/properties/date",
-      "default": "",
-      "description": "date of creation or modification, format yyyy-mm-dd",
-      "examples": [
-        "2019-12-06"
-      ],
-      "type": "string"
-    },
-    "description": {
-      "$id": "#/properties/description",
-      "default": "",
-      "description": "A detailed description of the detection",
-      "examples": [
-        "dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump."
-      ],
-      "type": "string"
-    },
-    "how_to_implement": {
-      "$id": "#/properties/how_to_implement",
-      "default": "",
-      "description": "information about how to implement. Only needed for non standard implementations.",
-      "examples": [
-        "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe."
-      ],
-      "type": "string"
-    },
-    "id": {
-      "$id": "#/properties/id",
-      "default": "",
-      "description": "UUID as unique identifier",
-      "examples": [
-        "fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-      ],
-      "type": "string"
-    },
-    "known_false_positives": {
-      "$id": "#/properties/knwon_false_positives",
-      "default": "",
-      "description": "known false postives",
-      "examples": [
-        "Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual."
-      ],
-      "type": "string"
-    },
-    "name": {
-      "$id": "#/properties/name",
-      "default": "",
-      "examples": [
-        "Access LSASS Memory for Dump Creation"
-      ],
-      "title": "Name of detection",
-      "type": "string"
-    },
-    "references": {
-      "$id": "#/properties/references",
-      "additionalItems": true,
-      "default": [],
-      "description": "A list of references for this detection",
-      "examples": [
-        [
-          "https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf"
-        ]
-      ],
-      "items": {
-        "$id": "#/properties/references/items",
-        "default": "",
-        "description": "An explanation about the purpose of this instance.",
-        "examples": [
-          "https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf"
-        ],
-        "title": "The Items Schema",
-        "type": "string"
-      },
-      "type": "array"
-    },
-    "search": {
-      "$id": "#/properties/search",
-      "default": "",
-      "description": "The Splunk search for the detection",
-      "examples": [
-        "`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter`"
-      ],
-      "type": "string"
-    },
-    "tags": {
-      "$id": "#/properties/tags",
-      "additionalProperties": true,
-      "default": {},
-      "description": "An array of key value pairs for tagging",
-      "examples": [
-        {
-          "analytics_story": "credential_dumping",
-          "custom_key": "custom_value"
-        }
-      ],
-      "minItems": 1,
-      "type": "object",
-      "uniqueItems": true
-    },
-    "type": {
-      "$id": "#/properties/type",
-      "default": "",
-      "description": "type of detection",
-      "examples": [
-        "ESCU"
-      ],
-      "items": {
-        "enum": [
-          "ESCU",
-          "SSE",
-          "RBA"
-        ],
-        "type": "string"
-      },
-      "type": "string"
-    },
-    "version": {
-      "$id": "#/properties/version",
-      "default": 0,
-      "description": "version of detection, e.g. 1 or 2 ...",
-      "examples": [
-        2
-      ],
-      "type": "integer"
-    }
-  },
-  "required": [
-    "name",
-    "id",
-    "version",
-    "date",
-    "description",
-    "type",
-    "author",
-    "search",
-    "known_false_positives",
-    "tags"
-  ],
-  "title": "Detection Schema",
-  "type": "object"
-}
diff --git a/docs/spec/detections.spec.md b/docs/spec/detections.spec.md
deleted file mode 100644
index 74c9176a96..0000000000
--- a/docs/spec/detections.spec.md
+++ /dev/null
@@ -1,380 +0,0 @@
-
-# Detection Schema Schema
-
-```
-http://example.com/example.json
-```
-
-schema for detections
-
-| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In |
-|----------|------------|--------|--------------|-------------------|-----------------------|------------|
-| Can be instantiated | No | Experimental | No | Forbidden | Permitted |  |
-
-# Detection Schema Properties
-
-| Property | Type | Required | Nullable | Default | Defined by |
-|----------|------|----------|----------|---------|------------|
-| [author](#author) | `string` | **Required**  | No | `""` | Detection Schema (this schema) |
-| [date](#date) | `string` | **Required**  | No | `""` | Detection Schema (this schema) |
-| [description](#description) | `string` | **Required**  | No | `""` | Detection Schema (this schema) |
-| [how_to_implement](#how_to_implement) | `string` | Optional  | No | `""` | Detection Schema (this schema) |
-| [id](#id) | `string` | **Required**  | No | `""` | Detection Schema (this schema) |
-| [known_false_positives](#known_false_positives) | `string` | **Required**  | No | `""` | Detection Schema (this schema) |
-| [name](#name) | `string` | **Required**  | No | `""` | Detection Schema (this schema) |
-| [references](#references) | `string[]` | Optional  | No | `[]` | Detection Schema (this schema) |
-| [search](#search) | `string` | **Required**  | No | `""` | Detection Schema (this schema) |
-| [tags](#tags) | `object` | **Required**  | No | `{}` | Detection Schema (this schema) |
-| [type](#type) | `string` | **Required**  | No | `""` | Detection Schema (this schema) |
-| [version](#version) | `integer` | **Required**  | No | `0` | Detection Schema (this schema) |
-| `*` | any | Additional | Yes | this schema *allows* additional properties |
-
-## author
-
-Author of the detection
-
-`author`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### author Type
-
-
-`string`
-
-
-
-
-
-
-### author Example
-
-```json
-"Patrick Bareiss, Splunk"
-```
-
-
-## date
-
-date of creation or modification, format yyyy-mm-dd
-
-`date`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### date Type
-
-
-`string`
-
-
-
-
-
-
-### date Example
-
-```json
-"2019-12-06"
-```
-
-
-## description
-
-A detailed description of the detection
-
-`description`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### description Type
-
-
-`string`
-
-
-
-
-
-
-### description Example
-
-```json
-"dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump."
-```
-
-
-## how_to_implement
-
-information about how to implement. Only needed for non standard implementations.
-
-`how_to_implement`
-
-* is optional
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### how_to_implement Type
-
-
-`string`
-
-
-
-
-
-
-### how_to_implement Example
-
-```json
-"This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe."
-```
-
-
-## id
-
-UUID as unique identifier
-
-`id`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### id Type
-
-
-`string`
-
-
-
-
-
-
-### id Example
-
-```json
-"fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-```
-
-
-## known_false_positives
-
-known false postives
-
-`known_false_positives`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### known_false_positives Type
-
-
-`string`
-
-
-
-
-
-
-### known_false_positives Example
-
-```json
-"Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual."
-```
-
-
-## name
-### Name of detection
-
-`name`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### name Type
-
-
-`string`
-
-
-
-
-
-
-### name Example
-
-```json
-"Access LSASS Memory for Dump Creation"
-```
-
-
-## references
-
-A list of references for this detection
-
-`references`
-
-* is optional
-* type: `string[]`
-
-* default: `[]`
-* defined in this schema
-
-### references Type
-
-
-Array type: `string[]`
-
-All items must be of the type:
-`string`
-
-
-
-
-  
-An explanation about the purpose of this instance.
-
-
-
-
-
-### references Example
-
-```json
-[
-  "https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf"
-]
-```
-
-
-## search
-
-The Splunk search for the detection
-
-`search`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### search Type
-
-
-`string`
-
-
-
-
-
-
-### search Example
-
-```json
-"`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter`"
-```
-
-
-## tags
-
-An array of key value pairs for tagging
-
-`tags`
-
-* is **required**
-* type: `object`
-* default: `{}`
-* defined in this schema
-
-### tags Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required |
-|----------|------|----------|
-
-
-
-
-### tags Example
-
-```json
-{
-  "analytics_story": "credential_dumping",
-  "custom_key": "custom_value"
-}
-```
-
-
-## type
-
-type of detection
-
-`type`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### type Type
-
-
-`string`
-
-
-
-
-
-
-### type Example
-
-```json
-"ESCU"
-```
-
-
-## version
-
-version of detection, e.g. 1 or 2 ...
-
-`version`
-
-* is **required**
-* type: `integer`
-* default: `0`
-* defined in this schema
-
-### version Type
-
-
-`integer`
-
-
-
-
-
-
-### version Example
-
-```json
-2
-```
diff --git a/docs/spec/lookups-oneof-0.md b/docs/spec/lookups-oneof-0.md
new file mode 100644
index 0000000000..78403ade45
--- /dev/null
+++ b/docs/spec/lookups-oneof-0.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/oneOf/0
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## 0 Type
+
+unknown
diff --git a/docs/spec/lookups-oneof-1.md b/docs/spec/lookups-oneof-1.md
new file mode 100644
index 0000000000..4b493c4f17
--- /dev/null
+++ b/docs/spec/lookups-oneof-1.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/oneOf/1
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## 1 Type
+
+unknown
diff --git a/docs/spec/lookups-properties-case_sensitive_match.md b/docs/spec/lookups-properties-case_sensitive_match.md
new file mode 100644
index 0000000000..b067278e67
--- /dev/null
+++ b/docs/spec/lookups-properties-case_sensitive_match.md
@@ -0,0 +1,31 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match
+```
+
+What the macro is intended to filter
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## case_sensitive_match Type
+
+`string`
+
+## case_sensitive_match Constraints
+
+**enum**: the value of this property must be equal to one of the following values:
+
+| Value     | Explanation |
+| :-------- | :---------- |
+| `"true"`  |             |
+| `"false"` |             |
+
+## case_sensitive_match Examples
+
+```yaml
+'true'
+
+```
diff --git a/docs/spec/lookups-properties-collection.md b/docs/spec/lookups-properties-collection.md
new file mode 100644
index 0000000000..62b7028b4d
--- /dev/null
+++ b/docs/spec/lookups-properties-collection.md
@@ -0,0 +1,22 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/collection
+```
+
+Name of the collection to use for this lookup
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## collection Type
+
+`string`
+
+## collection Examples
+
+```yaml
+prohibited_apps_launching_cmd
+
+```
diff --git a/docs/spec/lookups-properties-default_match.md b/docs/spec/lookups-properties-default_match.md
new file mode 100644
index 0000000000..46369bbdda
--- /dev/null
+++ b/docs/spec/lookups-properties-default_match.md
@@ -0,0 +1,22 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match
+```
+
+The default value if no match is found
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## default_match Type
+
+`string`
+
+## default_match Examples
+
+```yaml
+'true'
+
+```
diff --git a/docs/spec/lookups-properties-description.md b/docs/spec/lookups-properties-description.md
new file mode 100644
index 0000000000..e9fc0cfe11
--- /dev/null
+++ b/docs/spec/lookups-properties-description.md
@@ -0,0 +1,22 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/description
+```
+
+The description of this lookup
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## description Type
+
+`string`
+
+## description Examples
+
+```yaml
+This lookup contains file names that exist in the Windows\System32 directory
+
+```
diff --git a/docs/spec/lookups-properties-fields_list.md b/docs/spec/lookups-properties-fields_list.md
new file mode 100644
index 0000000000..e0a699ee17
--- /dev/null
+++ b/docs/spec/lookups-properties-fields_list.md
@@ -0,0 +1,22 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list
+```
+
+A comma and space separated list of field names
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## fields_list Type
+
+`string`
+
+## fields_list Examples
+
+```yaml
+_key, dest, process_name
+
+```
diff --git a/docs/spec/lookups-properties-filename.md b/docs/spec/lookups-properties-filename.md
new file mode 100644
index 0000000000..80584c0215
--- /dev/null
+++ b/docs/spec/lookups-properties-filename.md
@@ -0,0 +1,22 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/filename
+```
+
+The name of the file to use for this lookup
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## filename Type
+
+`string`
+
+## filename Examples
+
+```yaml
+prohibited_apps_launching_cmd.csv
+
+```
diff --git a/docs/spec/lookups-properties-filter.md b/docs/spec/lookups-properties-filter.md
new file mode 100644
index 0000000000..ad1a68ff22
--- /dev/null
+++ b/docs/spec/lookups-properties-filter.md
@@ -0,0 +1,22 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/filter
+```
+
+Use this attribute to improve search performance when working with significantly large KV
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## filter Type
+
+`string`
+
+## filter Examples
+
+```yaml
+dest="SPLK_*"
+
+```
diff --git a/docs/spec/lookups-properties-match_type.md b/docs/spec/lookups-properties-match_type.md
new file mode 100644
index 0000000000..3ca467f6a8
--- /dev/null
+++ b/docs/spec/lookups-properties-match_type.md
@@ -0,0 +1,22 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type
+```
+
+A comma and space-delimited list of \<match_type>(\<field_name>) specification to allow for non-exact matching
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## match_type Type
+
+`string`
+
+## match_type Examples
+
+```yaml
+WILDCARD(process)
+
+```
diff --git a/docs/spec/lookups-properties-max_matches.md b/docs/spec/lookups-properties-max_matches.md
new file mode 100644
index 0000000000..652395498b
--- /dev/null
+++ b/docs/spec/lookups-properties-max_matches.md
@@ -0,0 +1,22 @@
+# Untitled integer in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches
+```
+
+The maximum number of possible matches for each input lookup value
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## max_matches Type
+
+`integer`
+
+## max_matches Examples
+
+```yaml
+'100'
+
+```
diff --git a/docs/spec/lookups-properties-min_matches.md b/docs/spec/lookups-properties-min_matches.md
new file mode 100644
index 0000000000..e950b97fbd
--- /dev/null
+++ b/docs/spec/lookups-properties-min_matches.md
@@ -0,0 +1,22 @@
+# Untitled integer in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches
+```
+
+Minimum number of possible matches for each input lookup value
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## min_matches Type
+
+`integer`
+
+## min_matches Examples
+
+```yaml
+'1'
+
+```
diff --git a/docs/spec/lookups-properties-name.md b/docs/spec/lookups-properties-name.md
new file mode 100644
index 0000000000..ca27b5144c
--- /dev/null
+++ b/docs/spec/lookups-properties-name.md
@@ -0,0 +1,22 @@
+# Untitled string in Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json#/properties/name
+```
+
+The name of the lookup to be used in searches
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [lookups.spec.json*](../../out/lookups.spec.json "open original schema") |
+
+## name Type
+
+`string`
+
+## name Examples
+
+```yaml
+isWindowsSystemFile_lookup
+
+```
diff --git a/docs/spec/lookups.md b/docs/spec/lookups.md
new file mode 100644
index 0000000000..79b6e284e3
--- /dev/null
+++ b/docs/spec/lookups.md
@@ -0,0 +1,321 @@
+# Lookup Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/lookups.json
+```
+
+A object that defines a lookup file and its properties.
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                              |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [lookups.spec.json](../../out/lookups.spec.json "open original schema") |
+
+## Lookup Manifest Type
+
+`object` ([Lookup Manifest](lookups.md))
+
+one (and only one) of
+
+*   [Untitled undefined type in Lookup Manifest](lookups-oneof-0.md "check type definition")
+
+*   [Untitled undefined type in Lookup Manifest](lookups-oneof-1.md "check type definition")
+
+# Lookup Manifest Properties
+
+| Property                                      | Type      | Required | Nullable       | Defined by                                                                                                                                           |
+| :-------------------------------------------- | :-------- | :------- | :------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [case_sensitive_match](#case_sensitive_match) | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-case_sensitive_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match") |
+| [collection](#collection)                     | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-collection.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/collection")                     |
+| [default_match](#default_match)               | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-default_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match")               |
+| [description](#description)                   | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-description.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/description")                   |
+| [fields_list](#fields_list)                   | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-fields_list.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list")                   |
+| [filename](#filename)                         | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-filename.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filename")                         |
+| [filter](#filter)                             | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-filter.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filter")                             |
+| [match_type](#match_type)                     | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-match_type.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type")                     |
+| [max_matches](#max_matches)                   | `integer` | Optional | cannot be null | [Lookup Manifest](lookups-properties-max_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches")                   |
+| [min_matches](#min_matches)                   | `integer` | Optional | cannot be null | [Lookup Manifest](lookups-properties-min_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches")                   |
+| [name](#name)                                 | `string`  | Optional | cannot be null | [Lookup Manifest](lookups-properties-name.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/name")                                 |
+
+## case_sensitive_match
+
+What the macro is intended to filter
+
+`case_sensitive_match`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-case_sensitive_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/case_sensitive_match")
+
+### case_sensitive_match Type
+
+`string`
+
+### case_sensitive_match Constraints
+
+**enum**: the value of this property must be equal to one of the following values:
+
+| Value     | Explanation |
+| :-------- | :---------- |
+| `"true"`  |             |
+| `"false"` |             |
+
+### case_sensitive_match Examples
+
+```yaml
+'true'
+
+```
+
+## collection
+
+Name of the collection to use for this lookup
+
+`collection`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-collection.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/collection")
+
+### collection Type
+
+`string`
+
+### collection Examples
+
+```yaml
+prohibited_apps_launching_cmd
+
+```
+
+## default_match
+
+The default value if no match is found
+
+`default_match`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-default_match.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/default_match")
+
+### default_match Type
+
+`string`
+
+### default_match Examples
+
+```yaml
+'true'
+
+```
+
+## description
+
+The description of this lookup
+
+`description`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-description.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+This lookup contains file names that exist in the Windows\System32 directory
+
+```
+
+## fields_list
+
+A comma and space separated list of field names
+
+`fields_list`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-fields_list.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/fields_list")
+
+### fields_list Type
+
+`string`
+
+### fields_list Examples
+
+```yaml
+_key, dest, process_name
+
+```
+
+## filename
+
+The name of the file to use for this lookup
+
+`filename`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-filename.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filename")
+
+### filename Type
+
+`string`
+
+### filename Examples
+
+```yaml
+prohibited_apps_launching_cmd.csv
+
+```
+
+## filter
+
+Use this attribute to improve search performance when working with significantly large KV
+
+`filter`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-filter.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/filter")
+
+### filter Type
+
+`string`
+
+### filter Examples
+
+```yaml
+dest="SPLK_*"
+
+```
+
+## match_type
+
+A comma and space-delimited list of \<match_type>(\<field_name>) specification to allow for non-exact matching
+
+`match_type`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-match_type.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/match_type")
+
+### match_type Type
+
+`string`
+
+### match_type Examples
+
+```yaml
+WILDCARD(process)
+
+```
+
+## max_matches
+
+The maximum number of possible matches for each input lookup value
+
+`max_matches`
+
+*   is optional
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-max_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/max_matches")
+
+### max_matches Type
+
+`integer`
+
+### max_matches Examples
+
+```yaml
+'100'
+
+```
+
+## min_matches
+
+Minimum number of possible matches for each input lookup value
+
+`min_matches`
+
+*   is optional
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-min_matches.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/min_matches")
+
+### min_matches Type
+
+`integer`
+
+### min_matches Examples
+
+```yaml
+'1'
+
+```
+
+## name
+
+The name of the lookup to be used in searches
+
+`name`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Lookup Manifest](lookups-properties-name.md "https://api.splunkresearch.com/schemas/lookups.json#/properties/name")
+
+### name Type
+
+`string`
+
+### name Examples
+
+```yaml
+isWindowsSystemFile_lookup
+
+```
diff --git a/docs/spec/lookups.spec.json b/docs/spec/lookups.spec.json
deleted file mode 100644
index 0c85720259..0000000000
--- a/docs/spec/lookups.spec.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "$id": "https://api.splunkresearch.com/schemas/lookups.json",
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "description": "A object that defines a lookup file and its properties.",
-  "oneOf": [
-    {
-      "required": [
-        "collection"
-      ]
-    },
-    {
-      "required": [
-        "filename"
-      ]
-    }
-  ],
-  "properties": {
-    "case_sensitive_match": {
-      "description": "What the macro is intended to filter",
-      "enum": [
-        "true",
-        "false"
-      ],
-      "examples": [
-        "true"
-      ],
-      "type": "string"
-    },
-    "collection": {
-      "description": "Name of the collection to use for this lookup",
-      "examples": [
-        "prohibited_apps_launching_cmd"
-      ],
-      "type": "string"
-    },
-    "default_match": {
-      "description": "The default value if no match is found",
-      "examples": [
-        "true"
-      ],
-      "type": "string"
-    },
-    "description": {
-      "description": "The description of this lookup",
-      "examples": [
-        "This lookup contains file names that exist in the Windows\\System32 directory"
-      ],
-      "type": "string"
-    },
-    "filename": {
-      "description": "The name of the file to use for this lookup",
-      "examples": [
-        "prohibited_apps_launching_cmd.csv"
-      ],
-      "type": "string"
-    },
-    "match_type": {
-      "description": "A comma and space-delimited list of <match_type>(<field_name>) specification to allow for non-exact matching",
-      "examples": [
-        "WILDCARD(process)"
-      ],
-      "type": "string"
-    },
-    "max_matches": {
-      "description": "The maximum number of possible matches for each input lookup value",
-      "examples": [
-        "100"
-      ],
-      "type": "integer"
-    },
-    "min_matches": {
-      "description": "Minimum number of possible matches for each input lookup value",
-      "examples": [
-        "1"
-      ],
-      "type": "integer"
-    },
-    "name": {
-      "description": "The name of the lookup to be used in searches",
-      "examples": [
-        "isWindowsSystemFile_lookup"
-      ],
-      "type": "string"
-    }
-  },
-  "title": "Lookup Manifest",
-  "type": "object"
-}
diff --git a/docs/spec/lookups.spec.md b/docs/spec/lookups.spec.md
deleted file mode 100644
index 38f87220d1..0000000000
--- a/docs/spec/lookups.spec.md
+++ /dev/null
@@ -1,282 +0,0 @@
-
-# Lookup Manifest Schema
-
-```
-https://api.splunkresearch.com/schemas/lookups.json
-```
-
-A object that defines a lookup file and its properties.
-
-| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In |
-|----------|------------|--------|--------------|-------------------|-----------------------|------------|
-| Can be instantiated | No | Experimental | No | Forbidden | Permitted |  |
-
-# Lookup Manifest Properties
-
-| Property | Type | Required | Nullable | Defined by |
-|----------|------|----------|----------|------------|
-| [case_sensitive_match](#case_sensitive_match) | `enum` | Optional  | No | Lookup Manifest (this schema) |
-| [collection](#collection) | `string` | Optional  | No | Lookup Manifest (this schema) |
-| [default_match](#default_match) | `string` | Optional  | No | Lookup Manifest (this schema) |
-| [description](#description) | `string` | Optional  | No | Lookup Manifest (this schema) |
-| [filename](#filename) | `string` | Optional  | No | Lookup Manifest (this schema) |
-| [match_type](#match_type) | `string` | Optional  | No | Lookup Manifest (this schema) |
-| [max_matches](#max_matches) | `integer` | Optional  | No | Lookup Manifest (this schema) |
-| [min_matches](#min_matches) | `integer` | Optional  | No | Lookup Manifest (this schema) |
-| [name](#name) | `string` | Optional  | No | Lookup Manifest (this schema) |
-| `*` | any | Additional | Yes | this schema *allows* additional properties |
-
-## case_sensitive_match
-
-What the macro is intended to filter
-
-`case_sensitive_match`
-
-* is optional
-* type: `enum`
-* defined in this schema
-
-The value of this property **must** be equal to one of the [known values below](#case_sensitive_match-known-values).
-
-### case_sensitive_match Known Values
-| Value | Description |
-|-------|-------------|
-| `true` |  |
-| `false` |  |
-
-
-
-### case_sensitive_match Example
-
-```json
-"true"
-```
-
-
-## collection
-
-Name of the collection to use for this lookup
-
-`collection`
-
-* is optional
-* type: `string`
-* defined in this schema
-
-### collection Type
-
-
-`string`
-
-
-
-
-
-
-### collection Example
-
-```json
-"prohibited_apps_launching_cmd"
-```
-
-
-## default_match
-
-The default value if no match is found
-
-`default_match`
-
-* is optional
-* type: `string`
-* defined in this schema
-
-### default_match Type
-
-
-`string`
-
-
-
-
-
-
-### default_match Example
-
-```json
-"true"
-```
-
-
-## description
-
-The description of this lookup
-
-`description`
-
-* is optional
-* type: `string`
-* defined in this schema
-
-### description Type
-
-
-`string`
-
-
-
-
-
-
-### description Example
-
-```json
-"This lookup contains file names that exist in the Windows\\System32 directory"
-```
-
-
-## filename
-
-The name of the file to use for this lookup
-
-`filename`
-
-* is optional
-* type: `string`
-* defined in this schema
-
-### filename Type
-
-
-`string`
-
-
-
-
-
-
-### filename Example
-
-```json
-"prohibited_apps_launching_cmd.csv"
-```
-
-
-## match_type
-
-A comma and space-delimited list of <match_type>(<field_name>) specification to allow for non-exact matching
-
-`match_type`
-
-* is optional
-* type: `string`
-* defined in this schema
-
-### match_type Type
-
-
-`string`
-
-
-
-
-
-
-### match_type Example
-
-```json
-"WILDCARD(process)"
-```
-
-
-## max_matches
-
-The maximum number of possible matches for each input lookup value
-
-`max_matches`
-
-* is optional
-* type: `integer`
-* defined in this schema
-
-### max_matches Type
-
-
-`integer`
-
-
-
-
-
-
-### max_matches Example
-
-```json
-"100"
-```
-
-
-## min_matches
-
-Minimum number of possible matches for each input lookup value
-
-`min_matches`
-
-* is optional
-* type: `integer`
-* defined in this schema
-
-### min_matches Type
-
-
-`integer`
-
-
-
-
-
-
-### min_matches Example
-
-```json
-"1"
-```
-
-
-## name
-
-The name of the lookup to be used in searches
-
-`name`
-
-* is optional
-* type: `string`
-* defined in this schema
-
-### name Type
-
-
-`string`
-
-
-
-
-
-
-### name Example
-
-```json
-"isWindowsSystemFile_lookup"
-```
-
-
-
-**One** of the following *conditions* need to be fulfilled.
-
-
-#### Condition 1
-
-
-
-#### Condition 2
-
-
diff --git a/docs/spec/macros-properties-arguments-items.md b/docs/spec/macros-properties-arguments-items.md
new file mode 100644
index 0000000000..34f73377e8
--- /dev/null
+++ b/docs/spec/macros-properties-arguments-items.md
@@ -0,0 +1,15 @@
+# Untitled string in Macro Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/macros.json#/properties/arguments/items
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [macros.spec.json*](../../out/macros.spec.json "open original schema") |
+
+## items Type
+
+`string`
diff --git a/docs/spec/macros-properties-arguments.md b/docs/spec/macros-properties-arguments.md
new file mode 100644
index 0000000000..1334acd3c1
--- /dev/null
+++ b/docs/spec/macros-properties-arguments.md
@@ -0,0 +1,21 @@
+# Untitled array in Macro Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/macros.json#/properties/arguments
+```
+
+A list of the arguments being passed to this macro
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [macros.spec.json*](../../out/macros.spec.json "open original schema") |
+
+## arguments Type
+
+`string[]`
+
+## arguments Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `0`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
diff --git a/docs/spec/macros-properties-definition.md b/docs/spec/macros-properties-definition.md
new file mode 100644
index 0000000000..128ea17364
--- /dev/null
+++ b/docs/spec/macros-properties-definition.md
@@ -0,0 +1,22 @@
+# Untitled string in Macro Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/macros.json#/properties/definition
+```
+
+The macro definition
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [macros.spec.json*](../../out/macros.spec.json "open original schema") |
+
+## definition Type
+
+`string`
+
+## definition Examples
+
+```yaml
+(query=fls-na* AND query = www* AND query=images*)
+
+```
diff --git a/docs/spec/macros-properties-description.md b/docs/spec/macros-properties-description.md
new file mode 100644
index 0000000000..e5f8aed632
--- /dev/null
+++ b/docs/spec/macros-properties-description.md
@@ -0,0 +1,22 @@
+# Untitled string in Macro Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/macros.json#/properties/description
+```
+
+What the macro is intended to filter
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [macros.spec.json*](../../out/macros.spec.json "open original schema") |
+
+## description Type
+
+`string`
+
+## description Examples
+
+```yaml
+Use this macro to filter out known good objects
+
+```
diff --git a/docs/spec/macros-properties-name.md b/docs/spec/macros-properties-name.md
new file mode 100644
index 0000000000..484c1e4e3f
--- /dev/null
+++ b/docs/spec/macros-properties-name.md
@@ -0,0 +1,22 @@
+# Untitled string in Macro Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/macros.json#/properties/name
+```
+
+The name of the macro
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [macros.spec.json*](../../out/macros.spec.json "open original schema") |
+
+## name Type
+
+`string`
+
+## name Examples
+
+```yaml
+detection_search_output_filter
+
+```
diff --git a/docs/spec/macros.md b/docs/spec/macros.md
new file mode 100644
index 0000000000..c3cb8dc57b
--- /dev/null
+++ b/docs/spec/macros.md
@@ -0,0 +1,123 @@
+# Macro Manifest Schema
+
+```txt
+https://api.splunkresearch.com/schemas/macros.json
+```
+
+An object that defines the parameters for a Splunk Macro
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                            |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [macros.spec.json](../../out/macros.spec.json "open original schema") |
+
+## Macro Manifest Type
+
+`object` ([Macro Manifest](macros.md))
+
+# Macro Manifest Properties
+
+| Property                    | Type     | Required | Nullable       | Defined by                                                                                                                      |
+| :-------------------------- | :------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------ |
+| [arguments](#arguments)     | `array`  | Optional | cannot be null | [Macro Manifest](macros-properties-arguments.md "https://api.splunkresearch.com/schemas/macros.json#/properties/arguments")     |
+| [definition](#definition)   | `string` | Optional | cannot be null | [Macro Manifest](macros-properties-definition.md "https://api.splunkresearch.com/schemas/macros.json#/properties/definition")   |
+| [description](#description) | `string` | Required | cannot be null | [Macro Manifest](macros-properties-description.md "https://api.splunkresearch.com/schemas/macros.json#/properties/description") |
+| [name](#name)               | `string` | Required | cannot be null | [Macro Manifest](macros-properties-name.md "https://api.splunkresearch.com/schemas/macros.json#/properties/name")               |
+
+## arguments
+
+A list of the arguments being passed to this macro
+
+`arguments`
+
+*   is optional
+
+*   Type: `string[]`
+
+*   cannot be null
+
+*   defined in: [Macro Manifest](macros-properties-arguments.md "https://api.splunkresearch.com/schemas/macros.json#/properties/arguments")
+
+### arguments Type
+
+`string[]`
+
+### arguments Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `0`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+## definition
+
+The macro definition
+
+`definition`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Macro Manifest](macros-properties-definition.md "https://api.splunkresearch.com/schemas/macros.json#/properties/definition")
+
+### definition Type
+
+`string`
+
+### definition Examples
+
+```yaml
+(query=fls-na* AND query = www* AND query=images*)
+
+```
+
+## description
+
+What the macro is intended to filter
+
+`description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Macro Manifest](macros-properties-description.md "https://api.splunkresearch.com/schemas/macros.json#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+Use this macro to filter out known good objects
+
+```
+
+## name
+
+The name of the macro
+
+`name`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Macro Manifest](macros-properties-name.md "https://api.splunkresearch.com/schemas/macros.json#/properties/name")
+
+### name Type
+
+`string`
+
+### name Examples
+
+```yaml
+detection_search_output_filter
+
+```
diff --git a/docs/spec/macros.spec.json b/docs/spec/macros.spec.json
deleted file mode 100644
index cd0f5cafe1..0000000000
--- a/docs/spec/macros.spec.json
+++ /dev/null
@@ -1,43 +0,0 @@
-{
-  "$id": "https://api.splunkresearch.com/schemas/macros.json",
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "description": "An object that defines the parameters for a Splunk Macro",
-  "properties": {
-    "arguments": {
-      "description": "A list of the arguments being passed to this macro",
-      "items": {
-        "type": "string"
-      },
-      "minItems": 0,
-      "type": "array",
-      "uniqueItems": true
-    },
-    "definition": {
-      "description": "The macro definition",
-      "examples": [
-        "(query=fls-na* AND query = www* AND query=images*)"
-      ],
-      "type": "string"
-    },
-    "description": {
-      "description": "What the macro is intended to filter",
-      "examples": [
-        "Use this macro to filter out known good objects"
-      ],
-      "type": "string"
-    },
-    "name": {
-      "description": "The name of the macro",
-      "examples": [
-        "detection_search_output_filter"
-      ],
-      "type": "string"
-    }
-  },
-  "required": [
-    "name",
-    "description"
-  ],
-  "title": "Macro Manifest",
-  "type": "object"
-}
diff --git a/docs/spec/macros.spec.md b/docs/spec/macros.spec.md
deleted file mode 100644
index f39fdeffe4..0000000000
--- a/docs/spec/macros.spec.md
+++ /dev/null
@@ -1,131 +0,0 @@
-
-# Macro Manifest Schema
-
-```
-https://api.splunkresearch.com/schemas/macros.json
-```
-
-An object that defines the parameters for a Splunk Macro
-
-| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In |
-|----------|------------|--------|--------------|-------------------|-----------------------|------------|
-| Can be instantiated | No | Experimental | No | Forbidden | Permitted |  |
-
-# Macro Manifest Properties
-
-| Property | Type | Required | Nullable | Defined by |
-|----------|------|----------|----------|------------|
-| [arguments](#arguments) | `string[]` | Optional  | No | Macro Manifest (this schema) |
-| [definition](#definition) | `string` | Optional  | No | Macro Manifest (this schema) |
-| [description](#description) | `string` | **Required**  | No | Macro Manifest (this schema) |
-| [name](#name) | `string` | **Required**  | No | Macro Manifest (this schema) |
-| `*` | any | Additional | Yes | this schema *allows* additional properties |
-
-## arguments
-
-A list of the arguments being passed to this macro
-
-`arguments`
-
-* is optional
-* type: `string[]`
-* at least `0` items in the array
-* defined in this schema
-
-### arguments Type
-
-
-Array type: `string[]`
-
-All items must be of the type:
-`string`
-
-
-
-
-
-
-
-
-
-
-## definition
-
-The macro definition
-
-`definition`
-
-* is optional
-* type: `string`
-* defined in this schema
-
-### definition Type
-
-
-`string`
-
-
-
-
-
-
-### definition Example
-
-```json
-"(query=fls-na* AND query = www* AND query=images*)"
-```
-
-
-## description
-
-What the macro is intended to filter
-
-`description`
-
-* is **required**
-* type: `string`
-* defined in this schema
-
-### description Type
-
-
-`string`
-
-
-
-
-
-
-### description Example
-
-```json
-"Use this macro to filter out known good objects"
-```
-
-
-## name
-
-The name of the macro
-
-`name`
-
-* is **required**
-* type: `string`
-* defined in this schema
-
-### name Type
-
-
-`string`
-
-
-
-
-
-
-### name Example
-
-```json
-"detection_search_output_filter"
-```
-
diff --git a/docs/spec/response_tasks-default.md b/docs/spec/response_tasks-default.md
new file mode 100644
index 0000000000..31296217d9
--- /dev/null
+++ b/docs/spec/response_tasks-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Response Schema Schema
+
+```txt
+https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json#/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/response_tasks-properties-automation-default.md b/docs/spec/response_tasks-properties-automation-default.md
new file mode 100644
index 0000000000..b2cdd45428
--- /dev/null
+++ b/docs/spec/response_tasks-properties-automation-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Response Schema Schema
+
+```txt
+#/properties/automation#/properties/automation/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/response_tasks-properties-automation.md b/docs/spec/response_tasks-properties-automation.md
new file mode 100644
index 0000000000..bdd1eb66d4
--- /dev/null
+++ b/docs/spec/response_tasks-properties-automation.md
@@ -0,0 +1,62 @@
+# Untitled object in Response Schema Schema
+
+```txt
+#/properties/automation#/properties/automation
+```
+
+An array of key value pairs for defining actions and playbooks
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") |
+
+## automation Type
+
+`object` ([Details](response_tasks-properties-automation.md))
+
+## automation Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+## automation Default Value
+
+The default value is:
+
+```json
+{
+  "is_note_required": false,
+  "sla_type": "minutes",
+  "sla": "",
+  "role": "",
+  "action": [],
+  "playbooks": []
+}
+```
+
+## automation Examples
+
+```yaml
+is_note_required: false
+sla_type: minutes
+sla: 30
+action:
+  - run_query
+playbooks:
+  - scm: local
+    playbook: automate something
+  - scm: local
+    playbook: automate something else
+
+```
+
+# automation Properties
+
+| Property              | Type | Required | Nullable    | Defined by |
+| :-------------------- | :--- | :------- | :---------- | :--------- |
+| Additional Properties | Any  | Optional | can be null |            |
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/response_tasks-properties-sla.md b/docs/spec/response_tasks-properties-sla.md
new file mode 100644
index 0000000000..9de4e3e98f
--- /dev/null
+++ b/docs/spec/response_tasks-properties-sla.md
@@ -0,0 +1,27 @@
+# Untitled integer in Response Schema Schema
+
+```txt
+#/properties/sla#/properties/sla
+```
+
+Measured integer for Service Level Agreement for completion of the phase
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") |
+
+## sla Type
+
+`integer`
+
+## sla Examples
+
+```yaml
+5
+
+```
+
+```yaml
+30
+
+```
diff --git a/docs/spec/response_tasks-properties-sla_type.md b/docs/spec/response_tasks-properties-sla_type.md
new file mode 100644
index 0000000000..28319602eb
--- /dev/null
+++ b/docs/spec/response_tasks-properties-sla_type.md
@@ -0,0 +1,40 @@
+# Untitled string in Response Schema Schema
+
+```txt
+#/properties/sla_type#/properties/sla_type
+```
+
+Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc)
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                             |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [response_tasks.spec.json*](../../out/response_tasks.spec.json "open original schema") |
+
+## sla_type Type
+
+`string`
+
+## sla_type Default Value
+
+The default value is:
+
+```json
+"minutes"
+```
+
+## sla_type Examples
+
+```yaml
+minutes
+
+```
+
+```yaml
+hours
+
+```
+
+```yaml
+days
+
+```
diff --git a/docs/spec/response_tasks.md b/docs/spec/response_tasks.md
new file mode 100644
index 0000000000..6ee8b796ed
--- /dev/null
+++ b/docs/spec/response_tasks.md
@@ -0,0 +1,395 @@
+# Response Schema Schema
+
+```txt
+https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response_tasks.spec.json
+```
+
+schema for response task
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                            |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :------------------------------------------------------------------------------------ |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [response_tasks.spec.json](../../out/response_tasks.spec.json "open original schema") |
+
+## Response Schema Type
+
+`object` ([Response Schema](response_tasks.md))
+
+## Response Schema Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+# Response Schema Properties
+
+| Property                    | Type      | Required | Nullable       | Defined by                                                                                                     |
+| :-------------------------- | :-------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------- |
+| [author](#author)           | `string`  | Required | cannot be null | [Response Schema](response_tasks-properties-author.md "#/properties/author#/properties/author")                |
+| [date](#date)               | `string`  | Required | cannot be null | [Response Schema](response_tasks-properties-date.md "#/properties/date#/properties/date")                      |
+| [description](#description) | `string`  | Required | cannot be null | [Response Schema](response_tasks-properties-description.md "#/properties/description#/properties/description") |
+| [id](#id)                   | `string`  | Required | cannot be null | [Response Schema](response_tasks-properties-id.md "#/properties/id#/properties/id")                            |
+| [name](#name)               | `string`  | Required | cannot be null | [Response Schema](response_tasks-properties-name.md "#/properties/name#/properties/name")                      |
+| [sla](#sla)                 | `integer` | Optional | cannot be null | [Response Schema](response_tasks-properties-sla.md "#/properties/sla#/properties/sla")                         |
+| [sla_type](#sla_type)       | `string`  | Optional | cannot be null | [Response Schema](response_tasks-properties-sla_type.md "#/properties/sla_type#/properties/sla_type")          |
+| [automation](#automation)   | `object`  | Optional | cannot be null | [Response Schema](response_tasks-properties-automation.md "#/properties/automation#/properties/automation")    |
+| [tags](#tags)               | `object`  | Required | cannot be null | [Response Schema](response_tasks-properties-tags.md "#/properties/tags#/properties/tags")                      |
+| [version](#version)         | `integer` | Required | cannot be null | [Response Schema](response_tasks-properties-version.md "#/properties/version#/properties/version")             |
+| [references](#references)   | `array`   | Optional | cannot be null | [Response Schema](response_tasks-properties-references.md "#/properties/references#/properties/references")    |
+| Additional Properties       | Any       | Optional | can be null    |                                                                                                                |
+
+## author
+
+Author of the response task
+
+`author`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-author.md "#/properties/author#/properties/author")
+
+### author Type
+
+`string`
+
+### author Examples
+
+```yaml
+ButterCup, Splunk
+
+```
+
+## date
+
+date of creation or modification, format yyyy-mm-dd
+
+`date`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-date.md "#/properties/date#/properties/date")
+
+### date Type
+
+`string`
+
+### date Examples
+
+```yaml
+'2019-12-06'
+
+```
+
+## description
+
+Description of response task
+
+`description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-description.md "#/properties/description#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+Response example.
+
+```
+
+## id
+
+UUID as unique identifier
+
+`id`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-id.md "#/properties/id#/properties/id")
+
+### id Type
+
+`string`
+
+### id Examples
+
+```yaml
+fb4c31b0-13e8-4155-8aa5-24de4b8d6717
+
+```
+
+## name
+
+Name of response task
+
+`name`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-name.md "#/properties/name#/properties/name")
+
+### name Type
+
+`string`
+
+### name Examples
+
+```yaml
+Response Example
+
+```
+
+## sla
+
+Measured integer for Service Level Agreement for completion of the phase
+
+`sla`
+
+*   is optional
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-sla.md "#/properties/sla#/properties/sla")
+
+### sla Type
+
+`integer`
+
+### sla Examples
+
+```yaml
+5
+
+```
+
+```yaml
+30
+
+```
+
+## sla_type
+
+Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc)
+
+`sla_type`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-sla_type.md "#/properties/sla_type#/properties/sla_type")
+
+### sla_type Type
+
+`string`
+
+### sla_type Default Value
+
+The default value is:
+
+```json
+"minutes"
+```
+
+### sla_type Examples
+
+```yaml
+minutes
+
+```
+
+```yaml
+hours
+
+```
+
+```yaml
+days
+
+```
+
+## automation
+
+An array of key value pairs for defining actions and playbooks
+
+`automation`
+
+*   is optional
+
+*   Type: `object` ([Details](response_tasks-properties-automation.md))
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-automation.md "#/properties/automation#/properties/automation")
+
+### automation Type
+
+`object` ([Details](response_tasks-properties-automation.md))
+
+### automation Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+### automation Default Value
+
+The default value is:
+
+```json
+{
+  "is_note_required": false,
+  "sla_type": "minutes",
+  "sla": "",
+  "role": "",
+  "action": [],
+  "playbooks": []
+}
+```
+
+### automation Examples
+
+```yaml
+is_note_required: false
+sla_type: minutes
+sla: 30
+action:
+  - run_query
+playbooks:
+  - scm: local
+    playbook: automate something
+  - scm: local
+    playbook: automate something else
+
+```
+
+## tags
+
+An array of key value pairs for tagging
+
+`tags`
+
+*   is required
+
+*   Type: `object` ([Details](response_tasks-properties-tags.md))
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-tags.md "#/properties/tags#/properties/tags")
+
+### tags Type
+
+`object` ([Details](response_tasks-properties-tags.md))
+
+### tags Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+### tags Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### tags Examples
+
+```yaml
+analytic_story: credential_dumping
+
+```
+
+## version
+
+version of detection, e.g. 1 or 2 ...
+
+`version`
+
+*   is required
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-version.md "#/properties/version#/properties/version")
+
+### version Type
+
+`integer`
+
+### version Examples
+
+```yaml
+1
+
+```
+
+## references
+
+A list of references for this response, phase or task (e.g. web or printed citation)
+
+`references`
+
+*   is optional
+
+*   Type: `string[]` ([Blue Team Handbook by Don Murdoch - Amazon](response_tasks-properties-references-blue-team-handbook-by-don-murdoch---amazon.md))
+
+*   cannot be null
+
+*   defined in: [Response Schema](response_tasks-properties-references.md "#/properties/references#/properties/references")
+
+### references Type
+
+`string[]` ([Blue Team Handbook by Don Murdoch - Amazon](response_tasks-properties-references-blue-team-handbook-by-don-murdoch---amazon.md))
+
+### references Default Value
+
+The default value is:
+
+```json
+[]
+```
+
+### references Examples
+
+```yaml
+- Blue Team Handbook by Don Murdoch - Alarm Triage Overview pages 146-148
+- https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/response_tasks.spec.json b/docs/spec/response_tasks.spec.json
deleted file mode 100644
index 257e12f5fb..0000000000
--- a/docs/spec/response_tasks.spec.json
+++ /dev/null
@@ -1,159 +0,0 @@
-{
-  "$id": "http://example.com/example.json",
-  "$schema": "http://json-schema.org/draft-07/schema",
-  "additionalProperties": true,
-  "default": {},
-  "description": "schema for response tasks",
-  "properties": {
-    "author": {
-      "$id": "#/properties/author",
-      "default": "",
-      "description": "Author of response task",
-      "examples": [
-        "Patrick Barei\u00df, Splunk"
-      ],
-      "type": "string"
-    },
-    "dashboard": {
-      "$id": "#/properties/dashboard",
-      "default": "",
-      "description": "Name of dashboard used as response task",
-      "examples": [
-        "process_chain_analysis.json"
-      ],
-      "type": "string"
-    },
-    "date": {
-      "$id": "#/properties/date",
-      "default": "",
-      "description": "date of creation or modification, format yyyy-mm-dd",
-      "examples": [
-        "2019-12-06"
-      ],
-      "type": "string"
-    },
-    "description": {
-      "$id": "#/properties/description",
-      "default": "",
-      "description": "Description of response task",
-      "examples": [
-        "Response Task example description"
-      ],
-      "type": "string"
-    },
-    "how_to_implement": {
-      "$id": "#/properties/how_to_implement",
-      "default": "",
-      "description": "information about how to implement. Only needed for non standard implementations.",
-      "examples": [
-        "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe."
-      ],
-      "type": "string"
-    },
-    "id": {
-      "$id": "#/properties/id",
-      "default": "",
-      "description": "UUID as unique identifier",
-      "examples": [
-        "fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-      ],
-      "type": "string"
-    },
-    "inputs": {
-      "$id": "#/properties/inputs",
-      "default": [],
-      "description": "Inputs used from the response task",
-      "examples": [
-        [
-          "lookup_file"
-        ]
-      ],
-      "type": "array"
-    },
-    "name": {
-      "$id": "#/properties/name",
-      "default": "",
-      "description": "Namo fo response task",
-      "examples": [
-        "Response Tas Example"
-      ],
-      "type": "string"
-    },
-    "playbook": {
-      "$id": "#/properties/playbook",
-      "additionalProperties": true,
-      "default": {},
-      "description": "A phantom playbook as response task",
-      "examples": [
-        {
-          "name": "lets_encrypt_domain_investigate.json",
-          "url_json": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json",
-          "url_python": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py"
-        }
-      ],
-      "properties": {
-        "name": {
-          "$id": "#/properties/playbook/properties/name",
-          "default": "",
-          "description": "Name of Phantom Playbook",
-          "examples": [
-            "lets_encrypt_domain_investigate.json"
-          ],
-          "type": "string"
-        },
-        "url_json": {
-          "$id": "#/properties/playbook/properties/url_json",
-          "default": "",
-          "description": "URL for phantom playbook json file",
-          "examples": [
-            "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json"
-          ],
-          "type": "string"
-        },
-        "url_python": {
-          "$id": "#/properties/playbook/properties/url_python",
-          "default": "",
-          "description": "URL for phantom playbook python file",
-          "examples": [
-            "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py"
-          ],
-          "type": "string"
-        }
-      },
-      "required": [
-        "name",
-        "url_json",
-        "url_python"
-      ],
-      "type": "object"
-    },
-    "search": {
-      "$id": "#/properties/search",
-      "default": "",
-      "description": "Search as response task",
-      "examples": [
-        "`sysmon` EventCode=1 | search [| inputlookup %lookup_file% ] | stats count by dest user process_name"
-      ],
-      "type": "string"
-    },
-    "version": {
-      "$id": "#/properties/version",
-      "default": 0,
-      "description": "version of detection, e.g. 1 or 2 ...",
-      "examples": [
-        3
-      ],
-      "type": "integer"
-    }
-  },
-  "required": [
-    "name",
-    "id",
-    "version",
-    "date",
-    "description",
-    "author"
-  ],
-  "title": "Response Task Schema",
-  "type": "object"
-}
diff --git a/docs/spec/response_tasks.spec.md b/docs/spec/response_tasks.spec.md
deleted file mode 100644
index be14942e11..0000000000
--- a/docs/spec/response_tasks.spec.md
+++ /dev/null
@@ -1,437 +0,0 @@
-
-# Response Task Schema Schema
-
-```
-http://example.com/example.json
-```
-
-schema for response tasks
-
-| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In |
-|----------|------------|--------|--------------|-------------------|-----------------------|------------|
-| Can be instantiated | No | Experimental | No | Forbidden | Permitted |  |
-
-# Response Task Schema Properties
-
-| Property | Type | Required | Nullable | Default | Defined by |
-|----------|------|----------|----------|---------|------------|
-| [author](#author) | `string` | **Required**  | No | `""` | Response Task Schema (this schema) |
-| [dashboard](#dashboard) | `string` | Optional  | No | `""` | Response Task Schema (this schema) |
-| [date](#date) | `string` | **Required**  | No | `""` | Response Task Schema (this schema) |
-| [description](#description) | `string` | **Required**  | No | `""` | Response Task Schema (this schema) |
-| [how_to_implement](#how_to_implement) | `string` | Optional  | No | `""` | Response Task Schema (this schema) |
-| [id](#id) | `string` | **Required**  | No | `""` | Response Task Schema (this schema) |
-| [inputs](#inputs) | `array` | Optional  | No | `[]` | Response Task Schema (this schema) |
-| [name](#name) | `string` | **Required**  | No | `""` | Response Task Schema (this schema) |
-| [playbook](#playbook) | `object` | Optional  | No | `{}` | Response Task Schema (this schema) |
-| [search](#search) | `string` | Optional  | No | `""` | Response Task Schema (this schema) |
-| [version](#version) | `integer` | **Required**  | No | `0` | Response Task Schema (this schema) |
-| `*` | any | Additional | Yes | this schema *allows* additional properties |
-
-## author
-
-Author of response task
-
-`author`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### author Type
-
-
-`string`
-
-
-
-
-
-
-### author Example
-
-```json
-"Patrick Bareiß, Splunk"
-```
-
-
-## dashboard
-
-Name of dashboard used as response task
-
-`dashboard`
-
-* is optional
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### dashboard Type
-
-
-`string`
-
-
-
-
-
-
-### dashboard Example
-
-```json
-"process_chain_analysis.json"
-```
-
-
-## date
-
-date of creation or modification, format yyyy-mm-dd
-
-`date`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### date Type
-
-
-`string`
-
-
-
-
-
-
-### date Example
-
-```json
-"2019-12-06"
-```
-
-
-## description
-
-Description of response task
-
-`description`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### description Type
-
-
-`string`
-
-
-
-
-
-
-### description Example
-
-```json
-"Response Task example description"
-```
-
-
-## how_to_implement
-
-information about how to implement. Only needed for non standard implementations.
-
-`how_to_implement`
-
-* is optional
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### how_to_implement Type
-
-
-`string`
-
-
-
-
-
-
-### how_to_implement Example
-
-```json
-"This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe."
-```
-
-
-## id
-
-UUID as unique identifier
-
-`id`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### id Type
-
-
-`string`
-
-
-
-
-
-
-### id Example
-
-```json
-"fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-```
-
-
-## inputs
-
-Inputs used from the response task
-
-`inputs`
-
-* is optional
-* type: `array`
-
-* default: `[]`
-* defined in this schema
-
-### inputs Type
-
-
-Array type: `array`
-
-
-
-
-### inputs Example
-
-```json
-[
-  "lookup_file"
-]
-```
-
-
-## name
-
-Namo fo response task
-
-`name`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### name Type
-
-
-`string`
-
-
-
-
-
-
-### name Example
-
-```json
-"Response Tas Example"
-```
-
-
-## playbook
-
-A phantom playbook as response task
-
-`playbook`
-
-* is optional
-* type: `object`
-* default: `{}`
-* defined in this schema
-
-### playbook Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required | Default |
-|----------|------|----------|---------|
-| `name`| string | **Required** | `""` |
-| `url_json`| string | **Required** | `""` |
-| `url_python`| string | **Required** | `""` |
-
-
-
-#### name
-
-Name of Phantom Playbook
-
-`name`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### name Type
-
-
-`string`
-
-
-
-
-
-
-##### name Example
-
-```json
-lets_encrypt_domain_investigate.json
-```
-
-
-
-
-#### url_json
-
-URL for phantom playbook json file
-
-`url_json`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### url_json Type
-
-
-`string`
-
-
-
-
-
-
-##### url_json Example
-
-```json
-https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json
-```
-
-
-
-
-#### url_python
-
-URL for phantom playbook python file
-
-`url_python`
-
-* is **required**
-* type: `string`
-* default: `""`
-
-
-##### url_python Type
-
-
-`string`
-
-
-
-
-
-
-##### url_python Example
-
-```json
-https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py
-```
-
-
-
-
-
-### playbook Example
-
-```json
-{
-  "name": "lets_encrypt_domain_investigate.json",
-  "url_json": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json",
-  "url_python": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py"
-}
-```
-
-
-## search
-
-Search as response task
-
-`search`
-
-* is optional
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### search Type
-
-
-`string`
-
-
-
-
-
-
-### search Example
-
-```json
-"`sysmon` EventCode=1 | search [| inputlookup %lookup_file% ] | stats count by dest user process_name"
-```
-
-
-## version
-
-version of detection, e.g. 1 or 2 ...
-
-`version`
-
-* is **required**
-* type: `integer`
-* default: `0`
-* defined in this schema
-
-### version Type
-
-
-`integer`
-
-
-
-
-
-
-### version Example
-
-```json
-3
-```
-
diff --git a/docs/spec/responses-default.md b/docs/spec/responses-default.md
new file mode 100644
index 0000000000..3478b338bd
--- /dev/null
+++ b/docs/spec/responses-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Response Schema Schema
+
+```txt
+https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json#/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [responses.spec.json*](../../out/responses.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/responses-properties-is_note_required.md b/docs/spec/responses-properties-is_note_required.md
new file mode 100644
index 0000000000..26aed247e2
--- /dev/null
+++ b/docs/spec/responses-properties-is_note_required.md
@@ -0,0 +1,27 @@
+# Untitled boolean in Response Schema Schema
+
+```txt
+#/properties/is_note_required#/properties/is_note_required
+```
+
+Global assignment for notes being required for tasks, can be individually set in the task
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [responses.spec.json*](../../out/responses.spec.json "open original schema") |
+
+## is_note_required Type
+
+`boolean`
+
+## is_note_required Examples
+
+```yaml
+true
+
+```
+
+```yaml
+false
+
+```
diff --git a/docs/spec/responses-properties-response_phase-default.md b/docs/spec/responses-properties-response_phase-default.md
new file mode 100644
index 0000000000..e773e97379
--- /dev/null
+++ b/docs/spec/responses-properties-response_phase-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Response Schema Schema
+
+```txt
+#/properties/response_phases#/properties/response_phase/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [responses.spec.json*](../../out/responses.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/responses-properties-response_phase.md b/docs/spec/responses-properties-response_phase.md
new file mode 100644
index 0000000000..bdd06286c0
--- /dev/null
+++ b/docs/spec/responses-properties-response_phase.md
@@ -0,0 +1,51 @@
+# Untitled array in Response Schema Schema
+
+```txt
+#/properties/response_phases#/properties/response_phase
+```
+
+Response divided into phases. These will used to referenced known response_phase parameters
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                   |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [responses.spec.json*](../../out/responses.spec.json "open original schema") |
+
+## response_phase Type
+
+`array`
+
+## response_phase Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+## response_phase Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+## response_phase Examples
+
+```yaml
+preparation:
+  - id: 7c72d944-3995-4485-8e57-67b4c353989b
+    name: Preparation NIST
+identification:
+  - id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892
+    name: Detection and Analysis
+  - id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0
+    name: Analysis
+
+```
+
+# response_phase Properties
+
+| Property              | Type | Required | Nullable    | Defined by |
+| :-------------------- | :--- | :------- | :---------- | :--------- |
+| Additional Properties | Any  | Optional | can be null |            |
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/responses.md b/docs/spec/responses.md
new file mode 100644
index 0000000000..314ad93450
--- /dev/null
+++ b/docs/spec/responses.md
@@ -0,0 +1,340 @@
+# Response Schema Schema
+
+```txt
+https://raw.githubusercontent.com/splunk/security_content/develop/docs/spec/response.spec.json
+```
+
+schema for response
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                  |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [responses.spec.json](../../out/responses.spec.json "open original schema") |
+
+## Response Schema Type
+
+`object` ([Response Schema](responses.md))
+
+## Response Schema Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+# Response Schema Properties
+
+| Property                              | Type      | Required | Nullable       | Defined by                                                                                                               |
+| :------------------------------------ | :-------- | :------- | :------------- | :----------------------------------------------------------------------------------------------------------------------- |
+| [author](#author)                     | `string`  | Required | cannot be null | [Response Schema](responses-properties-author.md "#/properties/author#/properties/author")                               |
+| [date](#date)                         | `string`  | Required | cannot be null | [Response Schema](responses-properties-date.md "#/properties/date#/properties/date")                                     |
+| [description](#description)           | `string`  | Required | cannot be null | [Response Schema](responses-properties-description.md "#/properties/description#/properties/description")                |
+| [id](#id)                             | `string`  | Required | cannot be null | [Response Schema](responses-properties-id.md "#/properties/id#/properties/id")                                           |
+| [name](#name)                         | `string`  | Required | cannot be null | [Response Schema](responses-properties-name.md "#/properties/name#/properties/name")                                     |
+| [response_phase](#response_phase)     | `array`   | Required | cannot be null | [Response Schema](responses-properties-response_phase.md "#/properties/response_phases#/properties/response_phase")      |
+| [tags](#tags)                         | `object`  | Required | cannot be null | [Response Schema](responses-properties-tags.md "#/properties/tags#/properties/tags")                                     |
+| [version](#version)                   | `integer` | Required | cannot be null | [Response Schema](responses-properties-version.md "#/properties/version#/properties/version")                            |
+| [is_note_required](#is_note_required) | `boolean` | Optional | cannot be null | [Response Schema](responses-properties-is_note_required.md "#/properties/is_note_required#/properties/is_note_required") |
+| [references](#references)             | `array`   | Optional | cannot be null | [Response Schema](responses-properties-references.md "#/properties/references#/properties/references")                   |
+| Additional Properties                 | Any       | Optional | can be null    |                                                                                                                          |
+
+## author
+
+Author of the response
+
+`author`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-author.md "#/properties/author#/properties/author")
+
+### author Type
+
+`string`
+
+### author Examples
+
+```yaml
+Rico Valdez, Patrick Bareiß, Splunk
+
+```
+
+## date
+
+date of creation or modification, format yyyy-mm-dd
+
+`date`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-date.md "#/properties/date#/properties/date")
+
+### date Type
+
+`string`
+
+### date Examples
+
+```yaml
+'2019-12-06'
+
+```
+
+## description
+
+Description of response
+
+`description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-description.md "#/properties/description#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+Response example.
+
+```
+
+## id
+
+UUID as unique identifier
+
+`id`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-id.md "#/properties/id#/properties/id")
+
+### id Type
+
+`string`
+
+### id Examples
+
+```yaml
+fb4c31b0-13e8-4155-8aa5-24de4b8d6717
+
+```
+
+## name
+
+Name of response
+
+`name`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-name.md "#/properties/name#/properties/name")
+
+### name Type
+
+`string`
+
+### name Examples
+
+```yaml
+Response Example
+
+```
+
+## response_phase
+
+Response divided into phases. These will used to referenced known response_phase parameters
+
+`response_phase`
+
+*   is required
+
+*   Type: `array`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-response_phase.md "#/properties/response_phases#/properties/response_phase")
+
+### response_phase Type
+
+`array`
+
+### response_phase Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+### response_phase Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### response_phase Examples
+
+```yaml
+preparation:
+  - id: 7c72d944-3995-4485-8e57-67b4c353989b
+    name: Preparation NIST
+identification:
+  - id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892
+    name: Detection and Analysis
+  - id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0
+    name: Analysis
+
+```
+
+## tags
+
+An array of key value pairs for tagging
+
+`tags`
+
+*   is required
+
+*   Type: `object` ([Details](responses-properties-tags.md))
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-tags.md "#/properties/tags#/properties/tags")
+
+### tags Type
+
+`object` ([Details](responses-properties-tags.md))
+
+### tags Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+### tags Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### tags Examples
+
+```yaml
+analytic_story: credential_dumping
+
+```
+
+## version
+
+version of detection, e.g. 1 or 2 ...
+
+`version`
+
+*   is required
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-version.md "#/properties/version#/properties/version")
+
+### version Type
+
+`integer`
+
+### version Examples
+
+```yaml
+1
+
+```
+
+## is_note_required
+
+Global assignment for notes being required for tasks, can be individually set in the task
+
+`is_note_required`
+
+*   is optional
+
+*   Type: `boolean`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-is_note_required.md "#/properties/is_note_required#/properties/is_note_required")
+
+### is_note_required Type
+
+`boolean`
+
+### is_note_required Examples
+
+```yaml
+true
+
+```
+
+```yaml
+false
+
+```
+
+## references
+
+A list of references for this response, phase or task (e.g. web or printed citation)
+
+`references`
+
+*   is optional
+
+*   Type: `string[]` ([Blue Team Handbook by Don Murdoch - Amazon](responses-properties-references-blue-team-handbook-by-don-murdoch---amazon.md))
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses-properties-references.md "#/properties/references#/properties/references")
+
+### references Type
+
+`string[]` ([Blue Team Handbook by Don Murdoch - Amazon](responses-properties-references-blue-team-handbook-by-don-murdoch---amazon.md))
+
+### references Default Value
+
+The default value is:
+
+```json
+[]
+```
+
+### references Examples
+
+```yaml
+- Blue Team Handbook by Don Murdoch - Alarm Triage Overview pages 146-148
+- https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/responses.spec.json b/docs/spec/responses.spec.json
deleted file mode 100644
index f14d63f109..0000000000
--- a/docs/spec/responses.spec.json
+++ /dev/null
@@ -1,117 +0,0 @@
-{
-  "$id": "http://example.com/example.json",
-  "$schema": "http://json-schema.org/draft-07/schema",
-  "additionalProperties": true,
-  "default": {},
-  "description": "schema for response",
-  "properties": {
-    "author": {
-      "$id": "#/properties/author",
-      "default": "",
-      "description": "Author of the response",
-      "examples": [
-        "Rico Valdez, Patrick Barei\u00df, Splunk"
-      ],
-      "type": "string"
-    },
-    "date": {
-      "$id": "#/properties/date",
-      "default": "",
-      "description": "version of detection, e.g. 1 or 2 ...",
-      "examples": [
-        "2019-12-06"
-      ],
-      "type": "string"
-    },
-    "description": {
-      "$id": "#/properties/description",
-      "default": "",
-      "description": "Description of response",
-      "examples": [
-        "Response example."
-      ],
-      "type": "string"
-    },
-    "id": {
-      "$id": "#/properties/id",
-      "default": "",
-      "description": "UUID as unique identifier",
-      "examples": [
-        "fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-      ],
-      "type": "string"
-    },
-    "name": {
-      "$id": "#/properties/name",
-      "default": "",
-      "description": "Name of response",
-      "examples": [
-        "Response Example"
-      ],
-      "type": "string"
-    },
-    "response_tasks": {
-      "$id": "#/properties/response_tasks",
-      "additionalProperties": true,
-      "default": {},
-      "description": "Response tasks divided into phases",
-      "examples": [
-        {
-          "another_phase": [
-            {
-              "id": "7c72d944-3995-4485-8e57-67b4c353989b",
-              "name": "Another investigation"
-            }
-          ],
-          "identification": [
-            {
-              "id": "c36f3f48-e0bb-4c20-a62a-cdc8f6418892",
-              "name": "Investigate Indicator of Compromise Hash"
-            },
-            {
-              "id": "0dc849b2-2eb4-4fd2-add1-b6cc475765f0",
-              "name": "Investigate Domains"
-            }
-          ]
-        }
-      ],
-      "minItems": 1,
-      "type": "array"
-    },
-    "tags": {
-      "$id": "#/properties/tags",
-      "additionalProperties": true,
-      "default": {},
-      "description": "An array of key value pairs for tagging",
-      "examples": [
-        {
-          "analytics_story": "credential_dumping"
-        }
-      ],
-      "minItems": 1,
-      "type": "object",
-      "uniqueItems": true
-    },
-    "version": {
-      "$id": "#/properties/version",
-      "default": 0,
-      "description": "version of detection, e.g. 1 or 2 ...",
-      "examples": [
-        1
-      ],
-      "type": "integer"
-    }
-  },
-  "required": [
-    "name",
-    "id",
-    "version",
-    "date",
-    "description",
-    "author",
-    "response_tasks",
-    "tags"
-  ],
-  "title": "Response Schema",
-  "type": "object"
-}
diff --git a/docs/spec/responses.spec.md b/docs/spec/responses.spec.md
deleted file mode 100644
index 55779a8dfa..0000000000
--- a/docs/spec/responses.spec.md
+++ /dev/null
@@ -1,270 +0,0 @@
-
-# Response Schema Schema
-
-```
-http://example.com/example.json
-```
-
-schema for response
-
-| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In |
-|----------|------------|--------|--------------|-------------------|-----------------------|------------|
-| Can be instantiated | No | Experimental | No | Forbidden | Permitted |  |
-
-# Response Schema Properties
-
-| Property | Type | Required | Nullable | Default | Defined by |
-|----------|------|----------|----------|---------|------------|
-| [author](#author) | `string` | **Required**  | No | `""` | Response Schema (this schema) |
-| [date](#date) | `string` | **Required**  | No | `""` | Response Schema (this schema) |
-| [description](#description) | `string` | **Required**  | No | `""` | Response Schema (this schema) |
-| [id](#id) | `string` | **Required**  | No | `""` | Response Schema (this schema) |
-| [name](#name) | `string` | **Required**  | No | `""` | Response Schema (this schema) |
-| [response_tasks](#response_tasks) | `array` | **Required**  | No | `{}` | Response Schema (this schema) |
-| [tags](#tags) | `object` | **Required**  | No | `{}` | Response Schema (this schema) |
-| [version](#version) | `integer` | **Required**  | No | `0` | Response Schema (this schema) |
-| `*` | any | Additional | Yes | this schema *allows* additional properties |
-
-## author
-
-Author of the response
-
-`author`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### author Type
-
-
-`string`
-
-
-
-
-
-
-### author Example
-
-```json
-"Rico Valdez, Patrick Bareiß, Splunk"
-```
-
-
-## date
-
-version of detection, e.g. 1 or 2 ...
-
-`date`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### date Type
-
-
-`string`
-
-
-
-
-
-
-### date Example
-
-```json
-"2019-12-06"
-```
-
-
-## description
-
-Description of response
-
-`description`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### description Type
-
-
-`string`
-
-
-
-
-
-
-### description Example
-
-```json
-"Response example."
-```
-
-
-## id
-
-UUID as unique identifier
-
-`id`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### id Type
-
-
-`string`
-
-
-
-
-
-
-### id Example
-
-```json
-"fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-```
-
-
-## name
-
-Name of response
-
-`name`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### name Type
-
-
-`string`
-
-
-
-
-
-
-### name Example
-
-```json
-"Response Example"
-```
-
-
-## response_tasks
-
-Response tasks divided into phases
-
-`response_tasks`
-
-* is **required**
-* type: `array`
-* at least `1` items in the array
-* default: `{}`
-* defined in this schema
-
-### response_tasks Type
-
-
-Array type: `array`
-
-
-
-
-### response_tasks Example
-
-```json
-{
-  "another_phase": [
-    {
-      "id": "7c72d944-3995-4485-8e57-67b4c353989b",
-      "name": "Another investigation"
-    }
-  ],
-  "identification": [
-    {
-      "id": "c36f3f48-e0bb-4c20-a62a-cdc8f6418892",
-      "name": "Investigate Indicator of Compromise Hash"
-    },
-    {
-      "id": "0dc849b2-2eb4-4fd2-add1-b6cc475765f0",
-      "name": "Investigate Domains"
-    }
-  ]
-}
-```
-
-
-## tags
-
-An array of key value pairs for tagging
-
-`tags`
-
-* is **required**
-* type: `object`
-* default: `{}`
-* defined in this schema
-
-### tags Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required |
-|----------|------|----------|
-
-
-
-
-### tags Example
-
-```json
-{
-  "analytics_story": "credential_dumping"
-}
-```
-
-
-## version
-
-version of detection, e.g. 1 or 2 ...
-
-`version`
-
-* is **required**
-* type: `integer`
-* default: `0`
-* defined in this schema
-
-### version Type
-
-
-`integer`
-
-
-
-
-
-
-### version Example
-
-```json
-1
-```
-
diff --git a/docs/spec/responses_phase-default.md b/docs/spec/responses_phase-default.md
new file mode 100644
index 0000000000..c1576f1e4e
--- /dev/null
+++ b/docs/spec/responses_phase-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Response Schema Schema
+
+```txt
+http://example.com/example.json#/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/responses_phase-properties-response_task-default.md b/docs/spec/responses_phase-properties-response_task-default.md
new file mode 100644
index 0000000000..9ede2dfaf9
--- /dev/null
+++ b/docs/spec/responses_phase-properties-response_task-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Response Schema Schema
+
+```txt
+#/properties/response_task#/properties/response_task/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/responses_phase-properties-response_task.md b/docs/spec/responses_phase-properties-response_task.md
new file mode 100644
index 0000000000..780e4b3d5b
--- /dev/null
+++ b/docs/spec/responses_phase-properties-response_task.md
@@ -0,0 +1,57 @@
+# Untitled array in Response Schema Schema
+
+```txt
+#/properties/response_task#/properties/response_task
+```
+
+Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name.
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :--------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [responses_phase.spec.json*](../../out/responses_phase.spec.json "open original schema") |
+
+## response_task Type
+
+`array`
+
+## response_task Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+## response_task Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+## response_task Examples
+
+```yaml
+id: 7c72d944-3995-4485-8e57-67b4c353989b
+name: Prepare for Incident Handling
+
+```
+
+```yaml
+id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892
+name: Preventing Incidents
+
+```
+
+```yaml
+id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0
+name: Practice
+
+```
+
+# response_task Properties
+
+| Property              | Type | Required | Nullable    | Defined by |
+| :-------------------- | :--- | :------- | :---------- | :--------- |
+| Additional Properties | Any  | Optional | can be null |            |
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/responses_phase.md b/docs/spec/responses_phase.md
new file mode 100644
index 0000000000..09d1b3be2a
--- /dev/null
+++ b/docs/spec/responses_phase.md
@@ -0,0 +1,389 @@
+# Response Schema Schema
+
+```txt
+http://example.com/example.json
+```
+
+schema for phase
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                                              |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :-------------------------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [responses_phase.spec.json](../../out/responses_phase.spec.json "open original schema") |
+
+## Response Schema Type
+
+`object` ([Response Schema](responses_phase.md))
+
+## Response Schema Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+# Response Schema Properties
+
+| Property                        | Type      | Required | Nullable       | Defined by                                                                                                            |
+| :------------------------------ | :-------- | :------- | :------------- | :-------------------------------------------------------------------------------------------------------------------- |
+| [author](#author)               | `string`  | Required | cannot be null | [Response Schema](responses_phase-properties-author.md "#/properties/author#/properties/author")                      |
+| [date](#date)                   | `string`  | Required | cannot be null | [Response Schema](responses_phase-properties-date.md "#/properties/date#/properties/date")                            |
+| [description](#description)     | `string`  | Required | cannot be null | [Response Schema](responses_phase-properties-description.md "#/properties/description#/properties/description")       |
+| [id](#id)                       | `string`  | Required | cannot be null | [Response Schema](responses_phase-properties-id.md "#/properties/id#/properties/id")                                  |
+| [name](#name)                   | `string`  | Required | cannot be null | [Response Schema](responses_phase-properties-name.md "#/properties/name#/properties/name")                            |
+| [response_task](#response_task) | `array`   | Required | cannot be null | [Response Schema](responses_phase-properties-response_task.md "#/properties/response_task#/properties/response_task") |
+| [tags](#tags)                   | `object`  | Required | cannot be null | [Response Schema](responses_phase-properties-tags.md "#/properties/tags#/properties/tags")                            |
+| [version](#version)             | `integer` | Required | cannot be null | [Response Schema](responses_phase-properties-version.md "#/properties/version#/properties/version")                   |
+| [sla](#sla)                     | `integer` | Optional | cannot be null | [Response Schema](responses_phase-properties-sla.md "#/properties/sla#/properties/sla")                               |
+| [sla_type](#sla_type)           | `string`  | Optional | cannot be null | [Response Schema](responses_phase-properties-sla_type.md "#/properties/sla_type#/properties/sla_type")                |
+| [references](#references)       | `array`   | Optional | cannot be null | [Response Schema](responses_phase-properties-references.md "#/properties/references#/properties/references")          |
+| Additional Properties           | Any       | Optional | can be null    |                                                                                                                       |
+
+## author
+
+Author of the phase
+
+`author`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-author.md "#/properties/author#/properties/author")
+
+### author Type
+
+`string`
+
+### author Examples
+
+```yaml
+Rico Valdez, Patrick Bareiß, Splunk
+
+```
+
+## date
+
+date of creation or modification, format yyyy-mm-dd
+
+`date`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-date.md "#/properties/date#/properties/date")
+
+### date Type
+
+`string`
+
+### date Examples
+
+```yaml
+'2019-12-06'
+
+```
+
+## description
+
+Description of phase
+
+`description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-description.md "#/properties/description#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+Response phase descripion.
+
+```
+
+## id
+
+UUID as unique identifier
+
+`id`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-id.md "#/properties/id#/properties/id")
+
+### id Type
+
+`string`
+
+### id Examples
+
+```yaml
+fb4c31b0-13e8-4155-8aa5-24de4b8d6717
+
+```
+
+## name
+
+Name of phase
+
+`name`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-name.md "#/properties/name#/properties/name")
+
+### name Type
+
+`string`
+
+### name Examples
+
+```yaml
+Preparation
+
+```
+
+## response_task
+
+Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name.
+
+`response_task`
+
+*   is required
+
+*   Type: `array`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-response_task.md "#/properties/response_task#/properties/response_task")
+
+### response_task Type
+
+`array`
+
+### response_task Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+### response_task Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### response_task Examples
+
+```yaml
+id: 7c72d944-3995-4485-8e57-67b4c353989b
+name: Prepare for Incident Handling
+
+```
+
+```yaml
+id: c36f3f48-e0bb-4c20-a62a-cdc8f6418892
+name: Preventing Incidents
+
+```
+
+```yaml
+id: 0dc849b2-2eb4-4fd2-add1-b6cc475765f0
+name: Practice
+
+```
+
+## tags
+
+An array of key value pairs for tagging
+
+`tags`
+
+*   is required
+
+*   Type: `object` ([Details](responses_phase-properties-tags.md))
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-tags.md "#/properties/tags#/properties/tags")
+
+### tags Type
+
+`object` ([Details](responses_phase-properties-tags.md))
+
+### tags Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+**unique items**: all items in this array must be unique. Duplicates are not allowed.
+
+### tags Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### tags Examples
+
+```yaml
+analytic_story: credential_dumping
+
+```
+
+## version
+
+version of detection, e.g. 1 or 2 ...
+
+`version`
+
+*   is required
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-version.md "#/properties/version#/properties/version")
+
+### version Type
+
+`integer`
+
+### version Examples
+
+```yaml
+1
+
+```
+
+## sla
+
+Measured integer for Service Level Agreement for completion of the phase
+
+`sla`
+
+*   is optional
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-sla.md "#/properties/sla#/properties/sla")
+
+### sla Type
+
+`integer`
+
+### sla Examples
+
+```yaml
+5
+
+```
+
+```yaml
+30
+
+```
+
+## sla_type
+
+Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc)
+
+`sla_type`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-sla_type.md "#/properties/sla_type#/properties/sla_type")
+
+### sla_type Type
+
+`string`
+
+### sla_type Default Value
+
+The default value is:
+
+```json
+"minutes"
+```
+
+### sla_type Examples
+
+```yaml
+minutes
+
+```
+
+```yaml
+hours
+
+```
+
+```yaml
+days
+
+```
+
+## references
+
+A list of references for this response, phase or task (e.g. web or printed citation)
+
+`references`
+
+*   is optional
+
+*   Type: `string[]` ([3.1 Preparation](responses_phase-properties-references-31-preparation.md))
+
+*   cannot be null
+
+*   defined in: [Response Schema](responses_phase-properties-references.md "#/properties/references#/properties/references")
+
+### references Type
+
+`string[]` ([3.1 Preparation](responses_phase-properties-references-31-preparation.md))
+
+### references Default Value
+
+The default value is:
+
+```json
+[]
+```
+
+### references Examples
+
+```yaml
+https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/stories-default.md b/docs/spec/stories-default.md
new file mode 100644
index 0000000000..9abc854a40
--- /dev/null
+++ b/docs/spec/stories-default.md
@@ -0,0 +1,15 @@
+# Untitled undefined type in Analytics Story Schema Schema
+
+```txt
+http://example.com/example.json#/default
+```
+
+
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [stories.spec.json*](../../out/stories.spec.json "open original schema") |
+
+## default Type
+
+unknown
diff --git a/docs/spec/stories-properties-narrative.md b/docs/spec/stories-properties-narrative.md
new file mode 100644
index 0000000000..cc4532dd7b
--- /dev/null
+++ b/docs/spec/stories-properties-narrative.md
@@ -0,0 +1,26 @@
+# Untitled string in Analytics Story Schema Schema
+
+```txt
+#/properties/narrative#/properties/narrative
+```
+
+narrative of the analytics story
+
+| Abstract            | Extensible | Status         | Identifiable            | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                               |
+| :------------------ | :--------- | :------------- | :---------------------- | :---------------- | :-------------------- | :------------------ | :----------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | Unknown identifiability | Forbidden         | Allowed               | none                | [stories.spec.json*](../../out/stories.spec.json "open original schema") |
+
+## narrative Type
+
+`string`
+
+## narrative Examples
+
+```yaml
+>-
+  gathering credentials from a target system, often hashed or encrypted, is a
+  common attack technique. Even though the credentials may not be in plain text,
+  an attacker can still exfiltrate the data and set to cracking it offline, on
+  their own systems.
+
+```
diff --git a/docs/spec/stories.md b/docs/spec/stories.md
new file mode 100644
index 0000000000..50b565a2fe
--- /dev/null
+++ b/docs/spec/stories.md
@@ -0,0 +1,287 @@
+# Analytics Story Schema Schema
+
+```txt
+http://example.com/example.json
+```
+
+schema analytics story
+
+| Abstract            | Extensible | Status         | Identifiable | Custom Properties | Additional Properties | Access Restrictions | Defined In                                                              |
+| :------------------ | :--------- | :------------- | :----------- | :---------------- | :-------------------- | :------------------ | :---------------------------------------------------------------------- |
+| Can be instantiated | No         | Unknown status | No           | Forbidden         | Allowed               | none                | [stories.spec.json](../../out/stories.spec.json "open original schema") |
+
+## Analytics Story Schema Type
+
+`object` ([Analytics Story Schema](stories.md))
+
+## Analytics Story Schema Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+# Analytics Story Schema Properties
+
+| Property                    | Type      | Required | Nullable       | Defined by                                                                                                     |
+| :-------------------------- | :-------- | :------- | :------------- | :------------------------------------------------------------------------------------------------------------- |
+| [author](#author)           | `string`  | Required | cannot be null | [Analytics Story Schema](stories-properties-author.md "#/properties/author#/properties/author")                |
+| [date](#date)               | `string`  | Required | cannot be null | [Analytics Story Schema](stories-properties-date.md "#/properties/date#/properties/date")                      |
+| [description](#description) | `string`  | Required | cannot be null | [Analytics Story Schema](stories-properties-description.md "#/properties/description#/properties/description") |
+| [id](#id)                   | `string`  | Required | cannot be null | [Analytics Story Schema](stories-properties-id.md "#/properties/id#/properties/id")                            |
+| [name](#name)               | `string`  | Required | cannot be null | [Analytics Story Schema](stories-properties-name.md "#/properties/name#/properties/name")                      |
+| [narrative](#narrative)     | `string`  | Required | cannot be null | [Analytics Story Schema](stories-properties-narrative.md "#/properties/narrative#/properties/narrative")       |
+| [search](#search)           | `string`  | Optional | cannot be null | [Analytics Story Schema](stories-properties-search.md "#/properties/search#/properties/search")                |
+| [tags](#tags)               | `object`  | Required | cannot be null | [Analytics Story Schema](stories-properties-tags.md "#/properties/tags#/properties/tags")                      |
+| [version](#version)         | `integer` | Required | cannot be null | [Analytics Story Schema](stories-properties-version.md "#/properties/version#/properties/version")             |
+| Additional Properties       | Any       | Optional | can be null    |                                                                                                                |
+
+## author
+
+Author of the analytics story
+
+`author`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-author.md "#/properties/author#/properties/author")
+
+### author Type
+
+`string`
+
+### author Examples
+
+```yaml
+Rico Valdez, Patrick Bareiß, Splunk
+
+```
+
+## date
+
+date of creation or modification, format yyyy-mm-dd
+
+`date`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-date.md "#/properties/date#/properties/date")
+
+### date Type
+
+`string`
+
+### date Examples
+
+```yaml
+'2019-12-06'
+
+```
+
+## description
+
+description of the analytics story
+
+`description`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-description.md "#/properties/description#/properties/description")
+
+### description Type
+
+`string`
+
+### description Examples
+
+```yaml
+>-
+  Uncover activity consistent with credential dumping, a technique where
+  attackers compromise systems and attempt to obtain and exfiltrate passwords.
+
+```
+
+## id
+
+UUID as unique identifier
+
+`id`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-id.md "#/properties/id#/properties/id")
+
+### id Type
+
+`string`
+
+### id Examples
+
+```yaml
+fb4c31b0-13e8-4155-8aa5-24de4b8d6717
+
+```
+
+## name
+
+Name of the Analytics Story
+
+`name`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-name.md "#/properties/name#/properties/name")
+
+### name Type
+
+`string`
+
+### name Examples
+
+```yaml
+Credential Dumping
+
+```
+
+## narrative
+
+narrative of the analytics story
+
+`narrative`
+
+*   is required
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-narrative.md "#/properties/narrative#/properties/narrative")
+
+### narrative Type
+
+`string`
+
+### narrative Examples
+
+```yaml
+>-
+  gathering credentials from a target system, often hashed or encrypted, is a
+  common attack technique. Even though the credentials may not be in plain text,
+  an attacker can still exfiltrate the data and set to cracking it offline, on
+  their own systems.
+
+```
+
+## search
+
+An additional Splunk search, which uses the result of the detections
+
+`search`
+
+*   is optional
+
+*   Type: `string`
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-search.md "#/properties/search#/properties/search")
+
+### search Type
+
+`string`
+
+### search Examples
+
+```yaml
+>-
+  index=asx mitre_id=t1003 | stats values(source) as detections values(process)
+  as processes values(user) as users values(_time) as time count by dest
+
+```
+
+## tags
+
+An explanation about the purpose of this instance.
+
+`tags`
+
+*   is required
+
+*   Type: `object` ([Details](stories-properties-tags.md))
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-tags.md "#/properties/tags#/properties/tags")
+
+### tags Type
+
+`object` ([Details](stories-properties-tags.md))
+
+### tags Constraints
+
+**minimum number of items**: the minimum number of items for this array is: `1`
+
+### tags Default Value
+
+The default value is:
+
+```json
+{}
+```
+
+### tags Examples
+
+```yaml
+analytic_story: credential_dumping
+
+```
+
+## version
+
+version of analytics story, e.g. 1 or 2 ...
+
+`version`
+
+*   is required
+
+*   Type: `integer`
+
+*   cannot be null
+
+*   defined in: [Analytics Story Schema](stories-properties-version.md "#/properties/version#/properties/version")
+
+### version Type
+
+`integer`
+
+### version Examples
+
+```yaml
+1
+
+```
+
+## Additional Properties
+
+Additional properties are allowed and do not have to follow a specific schema
diff --git a/docs/spec/stories.spec.json b/docs/spec/stories.spec.json
deleted file mode 100644
index 3eda58b394..0000000000
--- a/docs/spec/stories.spec.json
+++ /dev/null
@@ -1,106 +0,0 @@
-{
-  "$id": "http://example.com/example.json",
-  "$schema": "http://json-schema.org/draft-07/schema",
-  "additionalProperties": true,
-  "default": {},
-  "description": "schema analytics story",
-  "properties": {
-    "author": {
-      "$id": "#/properties/author",
-      "default": "",
-      "description": "Author of the analytics story",
-      "examples": [
-        "Rico Valdez, Patrick Barei\u00df, Splunk"
-      ],
-      "type": "string"
-    },
-    "date": {
-      "$id": "#/properties/date",
-      "default": "",
-      "description": "date of creation or modification, format yyyy-mm-dd",
-      "examples": [
-        "2019-12-06"
-      ],
-      "type": "string"
-    },
-    "description": {
-      "$id": "#/properties/description",
-      "default": "",
-      "description": "description of the analytics story",
-      "examples": [
-        "Uncover activity consistent with credential dumping, a technique where attackers compromise systems and attempt to obtain and exfiltrate passwords."
-      ],
-      "type": "string"
-    },
-    "id": {
-      "$id": "#/properties/id",
-      "default": "",
-      "description": "UUID as unique identifier",
-      "examples": [
-        "fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-      ],
-      "type": "string"
-    },
-    "name": {
-      "$id": "#/properties/name",
-      "default": "",
-      "description": "Name of the Analytics Story",
-      "examples": [
-        "Credential Dumping"
-      ],
-      "type": "string"
-    },
-    "narrative": {
-      "$id": "#/properties/narrative",
-      "default": "",
-      "description": "narrative of the analytics story",
-      "examples": [
-        "gathering credentials from a target system, often hashed or encrypted, is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems."
-      ],
-      "type": "string"
-    },
-    "search": {
-      "$id": "#/properties/search",
-      "default": "",
-      "description": "An additional Splunk search, which uses the result of the detections",
-      "examples": [
-        "index=asx mitre_id=t1003 | stats values(source) as detections values(process) as processes values(user) as users values(_time) as time count by dest"
-      ],
-      "type": "string"
-    },
-    "tags": {
-      "$id": "#/properties/tags",
-      "additionalProperties": true,
-      "default": {},
-      "description": "An explanation about the purpose of this instance.",
-      "examples": [
-        {
-          "analytics_story": "credential_dumping"
-        }
-      ],
-      "minItems": 1,
-      "type": "object"
-    },
-    "version": {
-      "$id": "#/properties/version",
-      "default": 0,
-      "description": "version of analytics story, e.g. 1 or 2 ...",
-      "examples": [
-        1
-      ],
-      "type": "integer"
-    }
-  },
-  "required": [
-    "name",
-    "id",
-    "version",
-    "date",
-    "description",
-    "narrative",
-    "author",
-    "tags"
-  ],
-  "title": "Analytics Story Schema",
-  "type": "object"
-}
diff --git a/docs/spec/stories.spec.md b/docs/spec/stories.spec.md
deleted file mode 100644
index 722c1ac936..0000000000
--- a/docs/spec/stories.spec.md
+++ /dev/null
@@ -1,283 +0,0 @@
-
-# Analytics Story Schema Schema
-
-```
-http://example.com/example.json
-```
-
-schema analytics story
-
-| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In |
-|----------|------------|--------|--------------|-------------------|-----------------------|------------|
-| Can be instantiated | No | Experimental | No | Forbidden | Permitted |  |
-
-# Analytics Story Schema Properties
-
-| Property | Type | Required | Nullable | Default | Defined by |
-|----------|------|----------|----------|---------|------------|
-| [author](#author) | `string` | **Required**  | No | `""` | Analytics Story Schema (this schema) |
-| [date](#date) | `string` | **Required**  | No | `""` | Analytics Story Schema (this schema) |
-| [description](#description) | `string` | **Required**  | No | `""` | Analytics Story Schema (this schema) |
-| [id](#id) | `string` | **Required**  | No | `""` | Analytics Story Schema (this schema) |
-| [name](#name) | `string` | **Required**  | No | `""` | Analytics Story Schema (this schema) |
-| [narrative](#narrative) | `string` | **Required**  | No | `""` | Analytics Story Schema (this schema) |
-| [search](#search) | `string` | Optional  | No | `""` | Analytics Story Schema (this schema) |
-| [tags](#tags) | `object` | **Required**  | No | `{}` | Analytics Story Schema (this schema) |
-| [version](#version) | `integer` | **Required**  | No | `0` | Analytics Story Schema (this schema) |
-| `*` | any | Additional | Yes | this schema *allows* additional properties |
-
-## author
-
-Author of the analytics story
-
-`author`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### author Type
-
-
-`string`
-
-
-
-
-
-
-### author Example
-
-```json
-"Rico Valdez, Patrick Bareiß, Splunk"
-```
-
-
-## date
-
-date of creation or modification, format yyyy-mm-dd
-
-`date`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### date Type
-
-
-`string`
-
-
-
-
-
-
-### date Example
-
-```json
-"2019-12-06"
-```
-
-
-## description
-
-description of the analytics story
-
-`description`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### description Type
-
-
-`string`
-
-
-
-
-
-
-### description Example
-
-```json
-"Uncover activity consistent with credential dumping, a technique where attackers compromise systems and attempt to obtain and exfiltrate passwords."
-```
-
-
-## id
-
-UUID as unique identifier
-
-`id`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### id Type
-
-
-`string`
-
-
-
-
-
-
-### id Example
-
-```json
-"fb4c31b0-13e8-4155-8aa5-24de4b8d6717"
-```
-
-
-## name
-
-Name of the Analytics Story
-
-`name`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### name Type
-
-
-`string`
-
-
-
-
-
-
-### name Example
-
-```json
-"Credential Dumping"
-```
-
-
-## narrative
-
-narrative of the analytics story
-
-`narrative`
-
-* is **required**
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### narrative Type
-
-
-`string`
-
-
-
-
-
-
-### narrative Example
-
-```json
-"gathering credentials from a target system, often hashed or encrypted, is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems."
-```
-
-
-## search
-
-An additional Splunk search, which uses the result of the detections
-
-`search`
-
-* is optional
-* type: `string`
-* default: `""`
-* defined in this schema
-
-### search Type
-
-
-`string`
-
-
-
-
-
-
-### search Example
-
-```json
-"index=asx mitre_id=t1003 | stats values(source) as detections values(process) as processes values(user) as users values(_time) as time count by dest"
-```
-
-
-## tags
-
-An explanation about the purpose of this instance.
-
-`tags`
-
-* is **required**
-* type: `object`
-* default: `{}`
-* defined in this schema
-
-### tags Type
-
-
-`object` with following properties:
-
-
-| Property | Type | Required |
-|----------|------|----------|
-
-
-
-
-### tags Example
-
-```json
-{
-  "analytics_story": "credential_dumping"
-}
-```
-
-
-## version
-
-version of analytics story, e.g. 1 or 2 ...
-
-`version`
-
-* is **required**
-* type: `integer`
-* default: `0`
-* defined in this schema
-
-### version Type
-
-
-`integer`
-
-
-
-
-
-
-### version Example
-
-```json
-1
-```
-
diff --git a/response_phases/contain_eradicate_recover.yml b/response_phases/contain_eradicate_recover.yml
index 2f7cb515b0..26ef12572a 100644
--- a/response_phases/contain_eradicate_recover.yml
+++ b/response_phases/contain_eradicate_recover.yml
@@ -1,31 +1,43 @@
-name: Containment, Eradication, Recovery
-id: 15442b70-49a1-4e4b-afea-bc1acd63f4bc
-sla_type: minutes
-sla:
-description: The containment, eradication and recovery phase is for the acquiring, preserving, securing, and documenting of evidence that leads to the appropriate containment or mititgation of the incident. Eradication is focused on removing any future threats from vulnerabiliies, malware or activities that produced the incident. Recovery is the restoration of normal operations for system(s) and customers affected by the incident.
-references:
-  - 3.3 Containment, Eradication, and Recovery -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_task:
-  - id: 3d481dd1-4f30-4262-a846-78af6bdce11c
-    name: identify_additional_affected_hosts
-  - id: 735335a5-7ac0-4bdf-b1d3-6f4a6767d02f
-    name: contain_incident
-  - id: edb7867c-2e81-4356-a422-92781f4fa34c
-    name: implement_additional_monitoring
-  - id: f28177ae-78de-43c9-8692-e972e8a0aa62
-    name: identify_vunlerabilities
-  - id: 70362de1-bfef-4a0f-893f-3e0d605ed9b7
-    name: mitigate_or_remediate_any_vulnerabilities
-  - id: 26cd22c6-4b67-4dc5-b8d1-f5ef9b5d8226
-    name: remove_malicious_content
-  - id: b678705c-12a6-428b-a631-ed579332bc99
-    name: validate_hosts_eradicated
-  - id: bb515cf6-40b5-4005-af04-6f63439df7b4
-    name: restore_systems_to_operational_status
-  - id: 8218bcf6-739b-4f76-8952-eb133480ad8d
-    name: validate_restored_hosts
-  - id: ecf89e9b-106a-46d1-b236-a2716f71d7ae
-    name: implement_monitoring
-date: '2020-07-30'
-version: 2
 author: ButterCup, Splunk
+date: '2020-07-30'
+description: The containment, eradication and recovery phase is for the acquiring,
+  preserving, securing, and documenting of evidence that leads to the appropriate
+  containment or mititgation of the incident. Eradication is focused on removing any
+  future threats from vulnerabiliies, malware or activities that produced the incident.
+  Recovery is the restoration of normal operations for system(s) and customers affected
+  by the incident.
+id: 15442b70-49a1-4e4b-afea-bc1acd63f4bc
+name: Containment, Eradication, Recovery
+references:
+- 3.3 Containment, Eradication, and Recovery -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+response_task:
+- id: 3d481dd1-4f30-4262-a846-78af6bdce11c
+  name: identify_additional_affected_hosts
+- id: 735335a5-7ac0-4bdf-b1d3-6f4a6767d02f
+  name: contain_incident
+- id: edb7867c-2e81-4356-a422-92781f4fa34c
+  name: implement_additional_monitoring
+- id: f28177ae-78de-43c9-8692-e972e8a0aa62
+  name: identify_vunlerabilities
+- id: 70362de1-bfef-4a0f-893f-3e0d605ed9b7
+  name: mitigate_or_remediate_any_vulnerabilities
+- id: 26cd22c6-4b67-4dc5-b8d1-f5ef9b5d8226
+  name: remove_malicious_content
+- id: b678705c-12a6-428b-a631-ed579332bc99
+  name: validate_hosts_eradicated
+- id: bb515cf6-40b5-4005-af04-6f63439df7b4
+  name: restore_systems_to_operational_status
+- id: 8218bcf6-739b-4f76-8952-eb133480ad8d
+  name: validate_restored_hosts
+- id: ecf89e9b-106a-46d1-b236-a2716f71d7ae
+  name: implement_monitoring
+sla: null
+sla_type: minutes
+tags:
+  analytic_story: NIST SP 800-61r2 Response Plan
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+  usecase: Advanced Threat Detection
+type: response
+version: 2
diff --git a/response_phases/containment.yml b/response_phases/containment.yml
index 794c7ee5c5..926130eb6f 100644
--- a/response_phases/containment.yml
+++ b/response_phases/containment.yml
@@ -1,26 +1,29 @@
-name: Containment
-id: 5d790fae-8ba6-4fc9-b288-78b67ef8370c
-sla_type: minutes
-sla:
-description: The containment phase is for the acquiring, preserving, securing, and documenting of evidence that leads to the appropriate containment or mititgation of the incident. This phase will identify additional hosts and known vulnerabilities and implememt monitoring of the containment.
-tags:
-  analytics_story:
-    NIST SP 800-61r2 Response Plan
-  usecase:
-    Advanced Threat Detection
-  nist:
-    RS.RP
-references:
-  - 3.3 Containment, Eradication, and Recovery -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_task:
-  - id: 3d481dd1-4f30-4262-a846-78af6bdce11c
-    name: identify_additional_affected_hosts
-  - id: 735335a5-7ac0-4bdf-b1d3-6f4a6767d02f
-    name: contain_incident
-  - id: edb7867c-2e81-4356-a422-92781f4fa34c
-    name: implement_additional_monitoring
-  - id: f28177ae-78de-43c9-8692-e972e8a0aa62
-    name: identify_vunlerabilities
-date: '2020-07-30'
-version: 2
 author: ButterCup
+date: '2020-07-30'
+description: The containment phase is for the acquiring, preserving, securing, and
+  documenting of evidence that leads to the appropriate containment or mititgation
+  of the incident. This phase will identify additional hosts and known vulnerabilities
+  and implememt monitoring of the containment.
+id: 5d790fae-8ba6-4fc9-b288-78b67ef8370c
+name: Containment
+references:
+- 3.3 Containment, Eradication, and Recovery -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+response_task:
+- id: 3d481dd1-4f30-4262-a846-78af6bdce11c
+  name: identify_additional_affected_hosts
+- id: 735335a5-7ac0-4bdf-b1d3-6f4a6767d02f
+  name: contain_incident
+- id: edb7867c-2e81-4356-a422-92781f4fa34c
+  name: implement_additional_monitoring
+- id: f28177ae-78de-43c9-8692-e972e8a0aa62
+  name: identify_vunlerabilities
+sla: null
+sla_type: minutes
+tags:
+  analytic_story: NIST SP 800-61r2 Response Plan
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+  usecase: Advanced Threat Detection
+type: response
+version: 2
diff --git a/response_phases/detection_analysis.yml b/response_phases/detection_analysis.yml
index 1d4e33e74f..d5a2f0c88a 100644
--- a/response_phases/detection_analysis.yml
+++ b/response_phases/detection_analysis.yml
@@ -1,21 +1,33 @@
-name: Detection and Analysis
-id: a6eec2aa-3ec8-4f16-9c09-b8537873047d
-sla_type: minutes
-sla:
-description: Events are occurances of a systems or systems. Incidents are declared violations and incidents can occur in countless ways. Detection and analysis phase is about identifying an event as an incident and properly categorizing and prioritizing incident notification and documentation. It is infeasible to develop step-by-step instructions for handling every incident. This generic detection and analysis process is a template to ensure the right process is being followed.
-references:
-  -  3.2 Detection and Analysis - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_task:
-  - id: 92ba5c50-717d-44e7-bb88-72bf6907ec83
-    name: Determine if an incident has occurred
-  - id: ef9e7a25-73f0-4b63-b43b-2f4171518931
-    name: Analyze precursors to the event
-  - id: 994298f0-75fc-4c14-b044-9b81944d3a03
-    name: Confirm Incident
-  - id: 91f1c863-c080-4b3c-921c-e1ca1c0e7ae1
-    name: Determine incident prioritization
-  - id: 3890e0b3-bb46-4b9b-8134-184dbe644a8a
-    name: Document and Notify of Incident
-date: '2020-07-17'
-version: 1
 author: ButterCup, Splunk
+date: '2020-07-17'
+description: Events are occurances of a systems or systems. Incidents are declared
+  violations and incidents can occur in countless ways. Detection and analysis phase
+  is about identifying an event as an incident and properly categorizing and prioritizing
+  incident notification and documentation. It is infeasible to develop step-by-step
+  instructions for handling every incident. This generic detection and analysis process
+  is a template to ensure the right process is being followed.
+id: a6eec2aa-3ec8-4f16-9c09-b8537873047d
+name: Detection and Analysis
+references:
+- 3.2 Detection and Analysis - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+response_task:
+- id: 92ba5c50-717d-44e7-bb88-72bf6907ec83
+  name: Determine if an incident has occurred
+- id: ef9e7a25-73f0-4b63-b43b-2f4171518931
+  name: Analyze precursors to the event
+- id: 994298f0-75fc-4c14-b044-9b81944d3a03
+  name: Confirm Incident
+- id: 91f1c863-c080-4b3c-921c-e1ca1c0e7ae1
+  name: Determine incident prioritization
+- id: 3890e0b3-bb46-4b9b-8134-184dbe644a8a
+  name: Document and Notify of Incident
+sla: null
+sla_type: minutes
+tags:
+  analytic_story: NIST SP 800-61r2 Response Plan
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+  usecase: Advanced Threat Detection
+type: response
+version: 1
diff --git a/response_phases/eradication.yml b/response_phases/eradication.yml
index 317a5f4399..e2589529d9 100644
--- a/response_phases/eradication.yml
+++ b/response_phases/eradication.yml
@@ -1,24 +1,25 @@
-name: Eradication
-id: d3b80e0e-4e85-4259-a13c-69ef20987e1c
-sla_type: minutes
-sla:
-description: The eradication phase is focused on removing any further exposure from vulnerabiliies, malware or activities that produced the incident.
-tags:
-  analytics_story:
-    NIST SP 800-61r2 Response Plan
-  usecase:
-    Advanced Threat Detection
-  nist:
-    RS.RP
-references:
-  - 3.3 Containment, Eradication, and Recovery -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_task:
-  - id: 70362de1-bfef-4a0f-893f-3e0d605ed9b7
-    name: mitigate_or_remediate_any_vulnerabilities
-  - id: 26cd22c6-4b67-4dc5-b8d1-f5ef9b5d8226
-    name: remove_malicious_content
-  - id: b678705c-12a6-428b-a631-ed579332bc99
-    name: validate_hosts_eradicated
-date: '2020-07-17'
-version: 1
 author: ButterCup, Splunk
+date: '2020-07-17'
+description: The eradication phase is focused on removing any further exposure from
+  vulnerabiliies, malware or activities that produced the incident.
+id: d3b80e0e-4e85-4259-a13c-69ef20987e1c
+name: Eradication
+references:
+- 3.3 Containment, Eradication, and Recovery -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+response_task:
+- id: 70362de1-bfef-4a0f-893f-3e0d605ed9b7
+  name: mitigate_or_remediate_any_vulnerabilities
+- id: 26cd22c6-4b67-4dc5-b8d1-f5ef9b5d8226
+  name: remove_malicious_content
+- id: b678705c-12a6-428b-a631-ed579332bc99
+  name: validate_hosts_eradicated
+sla: null
+sla_type: minutes
+tags:
+  analytic_story: NIST SP 800-61r2 Response Plan
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+  usecase: Advanced Threat Detection
+type: response
+version: 1
diff --git a/response_phases/identification.yml b/response_phases/identification.yml
index 391c25df72..2298d00598 100644
--- a/response_phases/identification.yml
+++ b/response_phases/identification.yml
@@ -1,31 +1,43 @@
-name: Identification
-id: 6cdd56ba-5ffd-46a9-9dde-d25ce755c100
-sla_type: minutes
-sla:
-description: Events are occurances of a systems or systems. Incidents are declared violations and incidents can occur in countless ways. Detection and analysis phase is about identifying an event as an incident and properly categorizing and prioritizing incident notification and documentation. It is infeasible to develop step-by-step instructions for handling every incident. This generic detection and analysis process is a template to ensure the right process is being followed.
-references:
-  -  3.2 Detection and Analysis - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_task:
-  - id: 92ba5c50-717d-44e7-bb88-72bf6907ec83
-    name: Determine if an incident has occurred
-  - id: ef9e7a25-73f0-4b63-b43b-2f4171518931
-    name: Analyze precursors to the event
-  - id: be7cce5c-29b9-405c-923a-d4565705da2e
-    name: Analyze host indicator and reputation
-  - id: a194130b-f5a8-4bfe-b09f-35f58f4397d5
-    name: Analyze IP address indicator and reputation
-  - id: 7744864c-5446-47ab-8118-4cbaa1649747
-    name: Analyze domain indicator and reputation
-  - id: 65a23d95-7b5a-405c-b5bf-893983478d35
-    name: Analyze url indicator and reputation
-  - id: 9e2d3e51-2e8f-4d49-8206-fb3e5fbf6620
-    name: Analyze email indicator and reputation
-  - id: 994298f0-75fc-4c14-b044-9b81944d3a03
-    name: Confirm Incident
-  - id: 91f1c863-c080-4b3c-921c-e1ca1c0e7ae1
-    name: Determine incident prioritization
-  - id: 3890e0b3-bb46-4b9b-8134-184dbe644a8a
-    name: Document and Notify of Incident
-date: '2020-07-17'
-version: 1
 author: ButterCup, Splunk
+date: '2020-07-17'
+description: Events are occurances of a systems or systems. Incidents are declared
+  violations and incidents can occur in countless ways. Detection and analysis phase
+  is about identifying an event as an incident and properly categorizing and prioritizing
+  incident notification and documentation. It is infeasible to develop step-by-step
+  instructions for handling every incident. This generic detection and analysis process
+  is a template to ensure the right process is being followed.
+id: 6cdd56ba-5ffd-46a9-9dde-d25ce755c100
+name: Identification
+references:
+- 3.2 Detection and Analysis - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+response_task:
+- id: 92ba5c50-717d-44e7-bb88-72bf6907ec83
+  name: Determine if an incident has occurred
+- id: ef9e7a25-73f0-4b63-b43b-2f4171518931
+  name: Analyze precursors to the event
+- id: be7cce5c-29b9-405c-923a-d4565705da2e
+  name: Analyze host indicator and reputation
+- id: a194130b-f5a8-4bfe-b09f-35f58f4397d5
+  name: Analyze IP address indicator and reputation
+- id: 7744864c-5446-47ab-8118-4cbaa1649747
+  name: Analyze domain indicator and reputation
+- id: 65a23d95-7b5a-405c-b5bf-893983478d35
+  name: Analyze url indicator and reputation
+- id: 9e2d3e51-2e8f-4d49-8206-fb3e5fbf6620
+  name: Analyze email indicator and reputation
+- id: 994298f0-75fc-4c14-b044-9b81944d3a03
+  name: Confirm Incident
+- id: 91f1c863-c080-4b3c-921c-e1ca1c0e7ae1
+  name: Determine incident prioritization
+- id: 3890e0b3-bb46-4b9b-8134-184dbe644a8a
+  name: Document and Notify of Incident
+sla: null
+sla_type: minutes
+tags:
+  analytic_story: NIST SP 800-61r2 Response Plan
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+  usecase: Advanced Threat Detection
+type: response
+version: 1
diff --git a/response_phases/post-incident_activities.yml b/response_phases/post-incident_activities.yml
deleted file mode 100644
index 2deb1664fb..0000000000
--- a/response_phases/post-incident_activities.yml
+++ /dev/null
@@ -1,24 +0,0 @@
-name: Post-Incident Activities
-id: 001209bc-2f94-4dc7-b21e-9598c41eaa80
-sla_type: minutes
-sla:
-description: One of the most important parts of incident response is learning and improving. Each incident response team should evolve to reflect new threats, improved technology, and lessons learned. Holding a “lessons learned” meeting with all involved parties after a major incident and lessor incidents at a regular interval. Consolidating the incident details and measuring the incident important data points to ensure the most effective proceses are being used.
-tags:
-    - nist
-    - picerl
-  analytics_story:
-    NIST SP 800-61r2 Response Plan
-  usecase:
-    Advanced Threat Detection
-  nist:
-    RS.RP
-references:
-  - 3.4 Post-Incident Activity -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_task:
-  - id: 69d25415-408f-462a-899f-9bc8eef8c299
-    name: create_follow-up_report
-  - id: 43fc5e87-d819-460a-a740-de2066b18a29
-    name: provide_lessons_learned_tasks_or_changes
-date: '2020-07-30'
-version: 2
-author: ButterCup, Splunk
diff --git a/response_phases/preparation.yml b/response_phases/preparation.yml
index e38fd151ef..0e2f1fdaa9 100644
--- a/response_phases/preparation.yml
+++ b/response_phases/preparation.yml
@@ -1,30 +1,35 @@
-name: Preparation NIST
-id: d360707d-9214-4449-b15d-9d3cf134209a
-sla_type: minutes
-sla:
-description: Incident response methodologies typically emphasize preparation not only for establishing an incident response capability so that the organization is ready to respond to incidents, but also preventing incidents by ensuring that systems, networks, and applications are sufficiently secure. Incident response teams need to know what they have available and what they need to prepare, aquire or configure for success within the incident response process.
-tags:
-  analytics_story:
-    NIST SP 800-61r2 Response Plan
-  usecase:
-    Advanced Threat Detection
-  nist:
-    RS.RP
-references:
-  - 3.1 Preparation - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_task:
-  - id: 91d4566e-a292-4f0a-b894-dde23bde3f08
-    name: Prepare for Incident Handling
-  - id: 5b7c5d18-6598-412b-a4f1-e66e92890503
-    name: Preventing Incidents
-  - id: 97d00b14-dd01-47e4-b7eb-0a82f4998c4e
-    name: Practice Real World Events
-  - id: df493538-e598-463b-8835-a109022c2968
-    name: Conduct Training
-  - id: 145a82b5-cafd-468e-b487-737fdf13d6a4
-    name: Raise Personnel Awareness
-  - id: f83abcae-3734-45ff-99ef-b17eb937c057
-    name: Make Personnel Report Suspicious Activity
-date: '2020-07-17'
-version: 1
 author: ButterCup, Splunk
+date: '2020-07-17'
+description: Incident response methodologies typically emphasize preparation not only
+  for establishing an incident response capability so that the organization is ready
+  to respond to incidents, but also preventing incidents by ensuring that systems,
+  networks, and applications are sufficiently secure. Incident response teams need
+  to know what they have available and what they need to prepare, aquire or configure
+  for success within the incident response process.
+id: d360707d-9214-4449-b15d-9d3cf134209a
+name: Preparation NIST
+references:
+- 3.1 Preparation - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+response_task:
+- id: 91d4566e-a292-4f0a-b894-dde23bde3f08
+  name: Prepare for Incident Handling
+- id: 5b7c5d18-6598-412b-a4f1-e66e92890503
+  name: Preventing Incidents
+- id: 97d00b14-dd01-47e4-b7eb-0a82f4998c4e
+  name: Practice Real World Events
+- id: df493538-e598-463b-8835-a109022c2968
+  name: Conduct Training
+- id: 145a82b5-cafd-468e-b487-737fdf13d6a4
+  name: Raise Personnel Awareness
+- id: f83abcae-3734-45ff-99ef-b17eb937c057
+  name: Make Personnel Report Suspicious Activity
+sla: null
+sla_type: minutes
+tags:
+  analytic_story: NIST SP 800-61r2 Response Plan
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+  usecase: Advanced Threat Detection
+type: response
+version: 1
diff --git a/response_phases/recovery.yml b/response_phases/recovery.yml
index 2b559cf5f8..e449bdf1d9 100644
--- a/response_phases/recovery.yml
+++ b/response_phases/recovery.yml
@@ -1,24 +1,25 @@
-title: Recovery
-id: cae4dcdb-f81b-45ec-b0d6-a00cec468e9a
-sla_type: minutes
-sla:
-description: The recovery phase is the restoration of normal operations for system(s) and customers affected by the incident.
-tags:
-  analytics_story:
-    NIST SP 800-61r2 Response Plan
-  usecase:
-    Advanced Threat Detection
-  nist:
-    RS.RP
-references:
-  - 3.3 Containment, Eradication, and Recovery -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_task:
-  - id: bb515cf6-40b5-4005-af04-6f63439df7b4
-    name: restore_systems_to_operational_status
-  - id: 8218bcf6-739b-4f76-8952-eb133480ad8d
-    name: validate_restored_hosts
-  - id: ecf89e9b-106a-46d1-b236-a2716f71d7ae
-    name: implement_monitoring
-date: '2020-04-21'
-version: 1
 author: ButterCup, Splunk
+date: '2020-04-21'
+description: The recovery phase is the restoration of normal operations for system(s)
+  and customers affected by the incident.
+id: cae4dcdb-f81b-45ec-b0d6-a00cec468e9a
+references:
+- 3.3 Containment, Eradication, and Recovery -  https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+response_task:
+- id: bb515cf6-40b5-4005-af04-6f63439df7b4
+  name: restore_systems_to_operational_status
+- id: 8218bcf6-739b-4f76-8952-eb133480ad8d
+  name: validate_restored_hosts
+- id: ecf89e9b-106a-46d1-b236-a2716f71d7ae
+  name: implement_monitoring
+sla: null
+sla_type: minutes
+tags:
+  analytic_story: NIST SP 800-61r2 Response Plan
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+  usecase: Advanced Threat Detection
+title: Recovery
+type: response
+version: 1
diff --git a/response_tasks/all_backup_logs_for_host.yml b/response_tasks/all_backup_logs_for_host.yml
index 412f36e5fa..749a281a18 100644
--- a/response_tasks/all_backup_logs_for_host.yml
+++ b/response_tasks/all_backup_logs_for_host.yml
@@ -1,15 +1,18 @@
-name: All backup logs for host
-id: bc91a8cf-aaaa-4bb2-8140-e756cc06fd72
-version: 1
+author: Rico Valdez, Splunk
 date: '2017-09-12'
 description: Retrieve the backup logs for the last 2 weeks for a specific host in
   order to investigate why backups are not completing successfully.
 how_to_implement: The successfully implement this search you must first send your
   backup logs to Splunk.
-author: Rico Valdez, Splunk
+id: bc91a8cf-aaaa-4bb2-8140-e756cc06fd72
 inputs:
 - dest
+name: All backup logs for host
 search: '| search sourcetype="netbackup_logs" dest=$dest$'
 tags:
-  analytics_story:
+  analytic_story:
   - Monitor Backup Solution
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/amazon_eks_kubernetes_activity_by_src_ip.yml b/response_tasks/amazon_eks_kubernetes_activity_by_src_ip.yml
index a91b9d249b..618eaee5c2 100644
--- a/response_tasks/amazon_eks_kubernetes_activity_by_src_ip.yml
+++ b/response_tasks/amazon_eks_kubernetes_activity_by_src_ip.yml
@@ -1,6 +1,4 @@
-name: Amazon EKS Kubernetes activity by src ip
-id: a636cca4-7434-4a15-a278-c70734938e39
-version: 1
+author: Rod Soto, Splunk
 date: '2020-04-13'
 description: This search provides investigation data about requests via user agent,
   authentication request URI, verb and cluster name data against Kubernetes cluster
@@ -8,12 +6,18 @@ description: This search provides investigation data about requests via user age
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Cloud Watch
   EKS inputs.
-author: Rod Soto, Splunk
+id: a636cca4-7434-4a15-a278-c70734938e39
 inputs:
 - src_ip
-search: sourcetype="aws:cloudwatchlogs:eks" |rename sourceIPs{}
-  as src_ip |search src_ip=$src_ip$ | stats count min(_time) as firstTime max(_time) as lastTime values(user.username)
-  values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision src_ip
+name: Amazon EKS Kubernetes activity by src ip
+search: sourcetype="aws:cloudwatchlogs:eks" |rename sourceIPs{} as src_ip |search
+  src_ip=$src_ip$ | stats count min(_time) as firstTime max(_time) as lastTime values(user.username)
+  values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision
+  src_ip
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Scanning Activity
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/aws_investigate_security_hub_alerts_by_dest.yml b/response_tasks/aws_investigate_security_hub_alerts_by_dest.yml
index 6f61ee287d..3c0bf1389c 100644
--- a/response_tasks/aws_investigate_security_hub_alerts_by_dest.yml
+++ b/response_tasks/aws_investigate_security_hub_alerts_by_dest.yml
@@ -1,19 +1,26 @@
-name: AWS Investigate Security Hub alerts by dest
-id: b0d2e6a8-75fa-4b1b-9486-3d32acadf822
-version: 1
+author: Bhavin Patel, Splunk
 date: '2020-06-08'
-description: This search retrieves the all the alerts created by AWS Security Hub for a specific dest(instance_id). 
+description: This search retrieves the all the alerts created by AWS Security Hub
+  for a specific dest(instance_id).
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: Bhavin Patel, Splunk
+id: b0d2e6a8-75fa-4b1b-9486-3d32acadf822
 inputs:
 - dest
-search: 'sourcetype="aws:securityhub:firehose" "findings{}.Resources{}.Type"=AWSEC2Instance 
-| rex field=findings{}.Resources{}.Id .*instance/(?<instance>.*)| rename instance as dest| search dest = $dest$ |rename findings{}.* as * | rename Remediation.Recommendation.Text as Remediation |  table dest Title ProductArn Description FirstObservedAt RecordState Remediation'
+name: AWS Investigate Security Hub alerts by dest
+search: sourcetype="aws:securityhub:firehose" "findings{}.Resources{}.Type"=AWSEC2Instance
+  | rex field=findings{}.Resources{}.Id .*instance/(?<instance>.*)| rename instance
+  as dest| search dest = $dest$ |rename findings{}.* as * | rename Remediation.Recommendation.Text
+  as Remediation |  table dest Title ProductArn Description FirstObservedAt RecordState
+  Remediation
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Compute Instance
   - Cloud Cryptomining
   - Suspicious AWS EC2 Activities
   - AWS Suspicious Provisioning Activities
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/aws_investigate_user_activities_by_accesskeyid.yml b/response_tasks/aws_investigate_user_activities_by_accesskeyid.yml
index d582513856..e60c8ed1fd 100644
--- a/response_tasks/aws_investigate_user_activities_by_accesskeyid.yml
+++ b/response_tasks/aws_investigate_user_activities_by_accesskeyid.yml
@@ -1,18 +1,22 @@
-name: AWS Investigate User Activities By AccessKeyId
-id: 703b65a4-a0ae-4171-965d-45507506c64f
-version: 1
+author: David Dorsey, Splunk
 date: '2018-06-08'
 description: This search retrieves the times, ARN, source IPs, AWS regions, event
   names, and the result of the event for specific credentials.
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: David Dorsey, Splunk
+id: 703b65a4-a0ae-4171-965d-45507506c64f
 inputs:
 - accessKeyId
-search: '| search sourcetype=aws:cloudtrail | rename userIdentity.accessKeyId as accessKeyId| search accessKeyId=$accessKeyId$
-  | spath output=user path=userIdentity.arn  | rename sourceIPAddress as src_ip |
-  table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage'
+name: AWS Investigate User Activities By AccessKeyId
+search: '| search sourcetype=aws:cloudtrail | rename userIdentity.accessKeyId as accessKeyId|
+  search accessKeyId=$accessKeyId$ | spath output=user path=userIdentity.arn  | rename
+  sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode,
+  errorMessage'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/aws_investigate_user_activities_by_arn.yml b/response_tasks/aws_investigate_user_activities_by_arn.yml
index 2f7fb7ea95..60e16780bd 100644
--- a/response_tasks/aws_investigate_user_activities_by_arn.yml
+++ b/response_tasks/aws_investigate_user_activities_by_arn.yml
@@ -1,6 +1,4 @@
-name: AWS Investigate User Activities By ARN
-id: bc91a8cd-35e7-4bb2-6140-e756cc46fd72
-version: 2
+author: Bhavin Patel, Splunk
 date: '2019-04-30'
 description: This search lists all the logged CloudTrail activities by a specific
   user ARN and will create a table containing the source of the user, the region of
@@ -9,14 +7,14 @@ description: This search lists all the logged CloudTrail activities by a specifi
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: Bhavin Patel, Splunk
+id: bc91a8cd-35e7-4bb2-6140-e756cc46fd72
 inputs:
 - user
-search: '| search sourcetype=aws:cloudtrail | search user=$user$| table _time
-  userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion
-  eventName eventType'
+name: AWS Investigate User Activities By ARN
+search: '| search sourcetype=aws:cloudtrail | search user=$user$| table _time userIdentity.type
+  userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - AWS Network ACL Activity
   - Cloud Cryptomining
@@ -30,4 +28,7 @@ tags:
   - AWS Suspicious Provisioning Activities
   - Suspicious Cloud Instance Activities
   - AWS Security Hub Alerts
-
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/aws_network_acl_details_from_id.yml b/response_tasks/aws_network_acl_details_from_id.yml
index ff855f9c31..01c28e5d36 100644
--- a/response_tasks/aws_network_acl_details_from_id.yml
+++ b/response_tasks/aws_network_acl_details_from_id.yml
@@ -1,18 +1,22 @@
-name: AWS Network ACL Details from ID
-id: f3fb4d1b-5f33-4b01-b541-c7ah9534c242
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-01-22'
 description: This search queries AWS description logs and returns all the information
   about a specific network ACL via network ACL ID
 how_to_implement: In order to implement this search, you must install the AWS App
   for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later)
   and configure your AWS description inputs.
-author: Bhavin Patel, Splunk
+id: f3fb4d1b-5f33-4b01-b541-c7ah9534c242
 inputs:
 - networkAclId
-search: '| search sourcetype=aws:description| rename id as networkAclId | search  networkAclId=$networkAclId$ | table id account_id vpc_id network_acl_entries{}.*'
+name: AWS Network ACL Details from ID
+search: '| search sourcetype=aws:description| rename id as networkAclId | search  networkAclId=$networkAclId$
+  | table id account_id vpc_id network_acl_entries{}.*'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Command and Control
   - Suspicious AWS Traffic
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/aws_network_interface_details_via_resourceid.yml b/response_tasks/aws_network_interface_details_via_resourceid.yml
index d7b216c3ff..a64ec34182 100644
--- a/response_tasks/aws_network_interface_details_via_resourceid.yml
+++ b/response_tasks/aws_network_interface_details_via_resourceid.yml
@@ -1,6 +1,4 @@
-name: AWS Network Interface details via resourceId
-id: f3fb4d1c-5f33-4b01-b541-c3ah9534c241
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-05-07'
 description: This search queries AWS configuration logs and returns the information
   about a specific network interface via network interface ID. The information will
@@ -9,14 +7,19 @@ description: This search queries AWS configuration logs and returns the informat
 how_to_implement: In order to implement this search, you must install the AWS App
   for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later)
   and configure your AWS configuration inputs
-author: Bhavin Patel, Splunk
+id: f3fb4d1c-5f33-4b01-b541-c3ah9534c241
 inputs:
 - resourceId
+name: AWS Network Interface details via resourceId
 search: '| search sourcetype=aws:config resourceId=$resourceId$ | table _time ARN
   relationships{}.resourceType relationships{}.name relationships{}.resourceId  configuration.privateIpAddresses{}.privateIpAddress
   configuration.privateIpAddresses{}.association.publicIp'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Command and Control
   - Suspicious AWS Traffic
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/aws_s3_bucket_details_via_bucketname.yml b/response_tasks/aws_s3_bucket_details_via_bucketname.yml
index 73b392737d..06860f3d08 100644
--- a/response_tasks/aws_s3_bucket_details_via_bucketname.yml
+++ b/response_tasks/aws_s3_bucket_details_via_bucketname.yml
@@ -1,6 +1,4 @@
-name: AWS S3 Bucket details via bucketName
-id: f3fb2q1c-5f33-4b01-b541-c2ah9534c242
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-06-26'
 description: This search queries AWS configuration logs and returns the information
   about a specific S3 bucket. The information returned includes the time the S3 bucket
@@ -10,11 +8,17 @@ description: This search queries AWS configuration logs and returns the informat
 how_to_implement: To implement this search, you must install the AWS App for Splunk
   (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later) and
   configure your AWS inputs.
-author: Bhavin Patel, Splunk
+id: f3fb2q1c-5f33-4b01-b541-c2ah9534c242
 inputs:
 - bucketName
-search: '| search sourcetype=aws:config | rename resourceId as bucketName |search bucketName=$bucketName$ | table resourceCreationTime
-  bucketName vendor_region action aws_account_id supplementaryConfiguration.AccessControlList'
+name: AWS S3 Bucket details via bucketName
+search: '| search sourcetype=aws:config | rename resourceId as bucketName |search
+  bucketName=$bucketName$ | table resourceCreationTime bucketName vendor_region action
+  aws_account_id supplementaryConfiguration.AccessControlList'
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious AWS S3 Activities
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/analyze_malicious_file.yml b/response_tasks/deprecated/analyze_malicious_file.yml
index 4f05a42663..aaace4b79f 100644
--- a/response_tasks/deprecated/analyze_malicious_file.yml
+++ b/response_tasks/deprecated/analyze_malicious_file.yml
@@ -1,11 +1,11 @@
-name: Analyze Malicious File
-id: 6ee5c067-8228-4926-abb2-54f2c59d726e
-version: 1
-date: '2020-04-29'
-description: Perform a static and dynamic malware analysis for the malicious file. Use the findings for further response tasks.
 author: Patrick Bareiss, Splunk
+date: '2020-04-29'
+description: Perform a static and dynamic malware analysis for the malicious file.
+  Use the findings for further response tasks.
+id: 6ee5c067-8228-4926-abb2-54f2c59d726e
+name: Analyze Malicious File
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Hidden Cobra Malware
   - Lateral Movement
@@ -14,4 +14,7 @@ tags:
   - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
   - Ransomware
   - SamSam Ransomware
-
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/aws_investigate_user_activities_by_source_user.yml b/response_tasks/deprecated/aws_investigate_user_activities_by_source_user.yml
index 3b2a42a44c..006d965516 100644
--- a/response_tasks/deprecated/aws_investigate_user_activities_by_source_user.yml
+++ b/response_tasks/deprecated/aws_investigate_user_activities_by_source_user.yml
@@ -1,20 +1,23 @@
-name: AWS Investigate User Activities By Source User
-id: b0d2e6a8-75fa-4b1b-9486-3d32acadf891
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-06-08'
 description: This search retrieves the times, ARN, source IPs, AWS regions, event
   names, and the result of the event for specific ARNs.
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: Bhavin Patel, Splunk
+id: b0d2e6a8-75fa-4b1b-9486-3d32acadf891
 inputs:
 - src_user
+name: AWS Investigate User Activities By Source User
 search: '| search sourcetype=aws:cloudtrail userIdentity.arn=$src_user$ | spath output=user
   path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip,
   awsRegion, eventName, errorCode, errorMessage'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
   - Suspicious Cloud Instance Activities
   - Suspicious Cloud Provisioning Activities
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/get_authentication_logs_for_endpoint.yml b/response_tasks/deprecated/get_authentication_logs_for_endpoint.yml
index 1539cbd67e..6d1b37b112 100644
--- a/response_tasks/deprecated/get_authentication_logs_for_endpoint.yml
+++ b/response_tasks/deprecated/get_authentication_logs_for_endpoint.yml
@@ -1,19 +1,18 @@
-name: Get Authentication Logs For Endpoint
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-11-01'
 description: This search returns all users that have attempted to access a particular
   endpoint.
 how_to_implement: To successfully implement this search you need to be ingesting authentication
   logs from your various systems and populating the Authentication data model.
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76
 inputs:
 - dest
+name: Get Authentication Logs For Endpoint
 search: '| tstats count from datamodel=Authentication where Authentication.dest=$dest$
   by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action
   | `drop_dm_object_name("Authentication")`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Account Monitoring and Controls
   - Apache Struts Vulnerability
@@ -55,3 +54,7 @@ tags:
   - Windows Privilege Escalation
   - Windows Service Abuse
   - Suspicious Zoom Child Processes
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/get_notable_info.yml b/response_tasks/deprecated/get_notable_info.yml
index 91a66fbd5d..d0d68ca2e6 100644
--- a/response_tasks/deprecated/get_notable_info.yml
+++ b/response_tasks/deprecated/get_notable_info.yml
@@ -1,19 +1,18 @@
-name: Get Notable Info
-id: f3fb4d1b-5f33-4b01-b541-c7af9534c242
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-09-20'
 description: This search queries the notable index to retrieve detailed information
   captured within the notable. Every notable has a unique ID associated with it, which
   is used to point us directly to the notable event under investigation.
 how_to_implement: If you are using Enterprise Security you are likely already creating
   notable events with your correlation rules. No additional configuration is necessary.
-author: Bhavin Patel, Splunk
+id: f3fb4d1b-5f33-4b01-b541-c7af9534c242
 inputs:
 - event_id
+name: Get Notable Info
 search: '| search `notable_by_id($event_id$)` | table time, rule_name, dest, dest_asset_id,
   dest_owner, priority, severity, owner, status_description'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - AWS Network ACL Activity
   - AWS User Monitoring
@@ -71,3 +70,7 @@ tags:
   - Kubernetes Sensitive Object Access Activity
   - F5 TMUI RCE CVE-2020-5902
   - Windows DNS SIGRed CVE-2020-1350
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/get_process_registry_activity.yml b/response_tasks/deprecated/get_process_registry_activity.yml
index c522b90708..9db3ed5b4c 100644
--- a/response_tasks/deprecated/get_process_registry_activity.yml
+++ b/response_tasks/deprecated/get_process_registry_activity.yml
@@ -1,21 +1,24 @@
-name: Get Process Registry Activity
-id: d8362a34-b78a-4364-9733-59b505f5b8d5
-version: 2
+author: David Dorsey, Splunk
 date: '2019-11-06'
 description: This search returns the registry activity for a specific process on a
   specific endpoint
 how_to_implement: To successfully implement this search you must be ingesting endpoint
   data and populating the Endpoint data model.
-author: David Dorsey, Splunk
+id: d8362a34-b78a-4364-9733-59b505f5b8d5
 inputs:
 - process_id
 - dest
+name: Get Process Registry Activity
 search: '| tstats `security_content_summariesonly` values(Registry.registry_key_name)
   as registry_key_name, values(Registry.dest) as dest, values(Registry.process_id)
   as process_id from datamodel=Endpoint.Registry where Registry.process_id=$process_id$
   AND Registry.dest=$dest$ by Registry.registry_path, Registry.action, _time | `drop_dm_object_name(Registry)`
   | sort _time | table _time, process_id, dest, action, registry_key_name, registry_path'
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   - Suspicious Zoom Child Processes
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/deprecated/get_registry_activities.yml b/response_tasks/deprecated/get_registry_activities.yml
index 5ee2c4385f..e75efdd52f 100644
--- a/response_tasks/deprecated/get_registry_activities.yml
+++ b/response_tasks/deprecated/get_registry_activities.yml
@@ -1,6 +1,4 @@
-name: Get Registry Activities
-id: fecf2918-670d-4f1c-872b-3d7317a41xf9
-version: 2
+author: Bhavin Patel, Splunk
 date: '2019-03-01'
 description: This search queries the Endpoint Datamodel to give you details of the
   latest registry values for a specific destination computer.
@@ -8,9 +6,10 @@ how_to_implement: To successfully implement this search you need to be ingesting
   on registry changes that include the name of the process responsible for the changes
   from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry`
   nodes.
-author: Bhavin Patel, Splunk
+id: fecf2918-670d-4f1c-872b-3d7317a41xf9
 inputs:
 - dest
+name: Get Registry Activities
 search: '| tstats `security_content_summariesonly` values(Registry.registry_path)
   as registry_path values(Registry.registry_key_name) as registry_key_name count FROM
   datamodel=Endpoint.Registry where Registry.dest = "$dest$" by Registry.process_id
@@ -19,7 +18,7 @@ search: '| tstats `security_content_summariesonly` values(Registry.registry_path
   values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes
   where Processes.process_name = reg.exe by Processes.process_id | `drop_dm_object_name("Processes")`]'
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   - 'Emotet Malware  DHS Report TA18-201A '
   - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
@@ -31,3 +30,7 @@ tags:
   - Windows File Extension and Association Abuse
   - Windows Persistence Techniques
   - Windows Privilege Escalation
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/deprecated/get_risk_modifiers_for_endpoint.yml b/response_tasks/deprecated/get_risk_modifiers_for_endpoint.yml
index 665c2dd28a..f49c32d7c1 100644
--- a/response_tasks/deprecated/get_risk_modifiers_for_endpoint.yml
+++ b/response_tasks/deprecated/get_risk_modifiers_for_endpoint.yml
@@ -1,20 +1,19 @@
-name: Get Risk Modifiers For Endpoint
-id: fdcfb369-1725-4c24-824a-22972d7f0d65
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-10-19'
 description: 'For the last 7 days, the search will query the Risk data model in Splunk
   Enterprise Security and calculate the count, sum of the risk\_scores, names of the
   correlation searches that contributed to create a risk score for a specific endpoint(machine\_name) '
 how_to_implement: Enable the correlation searches included in Splunk Enterprise Security
   that include Risk Analysis alert actions by leveraging the Risk Analysis Framework
-author: Bhavin Patel, Splunk
+id: fdcfb369-1725-4c24-824a-22972d7f0d65
 inputs:
 - dest
+name: Get Risk Modifiers For Endpoint
 search: '| from datamodel:Risk.All_Risk | search risk_object_type=system risk_object=$dest$
   | stats count sum(risk_score) as risk_score values(search_name)  min(_time) as firstTime
   max(_time) as lastTime by risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Account Monitoring and Controls
   - Apache Struts Vulnerability
@@ -63,3 +62,7 @@ tags:
   - Windows Persistence Techniques
   - Windows Privilege Escalation
   - Windows Service Abuse
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/get_risk_modifiers_for_user.yml b/response_tasks/deprecated/get_risk_modifiers_for_user.yml
index 37d53e5e5c..79e8da9cde 100644
--- a/response_tasks/deprecated/get_risk_modifiers_for_user.yml
+++ b/response_tasks/deprecated/get_risk_modifiers_for_user.yml
@@ -1,20 +1,19 @@
-name: Get Risk Modifiers For User
-id: fdcfb369-1725-4c24-824a-22972d7f0d55
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-10-19'
 description: 'For the last 7 days, the search will query the Risk data model in Splunk
   Enterprise Security and calculate the count, sum of the risk_scores, names of the
   correlation searches that contributed to create a risk score for a specific user '
 how_to_implement: Enable the correlation searches included in Splunk Enterprise Security
   that include Risk Analysis alert actions by leveraging the Risk Analysis Framework
-author: Bhavin Patel, Splunk
+id: fdcfb369-1725-4c24-824a-22972d7f0d55
 inputs:
 - user
+name: Get Risk Modifiers For User
 search: '| from datamodel:Risk.All_Risk | search risk_object_type=user risk_object=$user$
   | stats count sum(risk_score) as risk_score values(search_name)  min(_time) as firstTime
   max(_time) as lastTime by risk_object |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` '
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Account Monitoring and Controls
   - Apache Struts Vulnerability
@@ -57,3 +56,7 @@ tags:
   - Windows Persistence Techniques
   - Windows Privilege Escalation
   - Windows Service Abuse
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/get_update_logs_for_endpoint.yml b/response_tasks/deprecated/get_update_logs_for_endpoint.yml
index 0c26920660..031ee2d936 100644
--- a/response_tasks/deprecated/get_update_logs_for_endpoint.yml
+++ b/response_tasks/deprecated/get_update_logs_for_endpoint.yml
@@ -1,18 +1,21 @@
-name: Get Update Logs For Endpoint
-id: d98675ed-da43-4a7e-96a7-eeca3232ba8e
-version: 1
+author: David Dorsey, Splunk
 date: '2017-08-24'
 description: This search will tell you give you the update logs for a specific endpoint
   for the last week.
 how_to_implement: You need to be ingesting the update logs from your various systems.
-author: David Dorsey, Splunk
+id: d98675ed-da43-4a7e-96a7-eeca3232ba8e
 inputs:
 - dest
+name: Get Update Logs For Endpoint
 search: '| from datamodel Updates.Updates  | search (vendor_product="Microsoft Windows"
   OR vendor_product="OSX:Update" OR vendor_product="Linux:Update") dest=$dest$'
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Monitor for Unauthorized Software
   - Ransomware
   - SamSam Ransomware
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/get_user_information_from_identity_table.yml b/response_tasks/deprecated/get_user_information_from_identity_table.yml
index 60df58e6d6..f1e9a57e9d 100644
--- a/response_tasks/deprecated/get_user_information_from_identity_table.yml
+++ b/response_tasks/deprecated/get_user_information_from_identity_table.yml
@@ -1,17 +1,16 @@
-name: Get User Information from Identity Table
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-09-20'
 description: Gather more information about the user identified in the Notable Event.
 how_to_implement: To successfully implement this search you must have populated the
   identity table with information about your users.
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74
 inputs:
 - user
+name: Get User Information from Identity Table
 search: '| `identities` | search identity=$user$ | table _time, identity, first, last,
   email, category, watchlist'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - AWS Network ACL Activity
   - Account Monitoring and Controls
@@ -56,3 +55,7 @@ tags:
   - Windows Persistence Techniques
   - Windows Privilege Escalation
   - Windows Service Abuse
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/get_vulnerability_logs_for_endpoint.yml b/response_tasks/deprecated/get_vulnerability_logs_for_endpoint.yml
index 9daed1be94..6809b901f3 100644
--- a/response_tasks/deprecated/get_vulnerability_logs_for_endpoint.yml
+++ b/response_tasks/deprecated/get_vulnerability_logs_for_endpoint.yml
@@ -1,16 +1,15 @@
-name: Get Vulnerability Logs For Endpoint
-id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5
-version: 1
+author: David Dorsey, Splunk
 date: '2017-09-10'
 description: This search will show you any vulnerabilities noted for a specific endpoint
   for the last week.
 how_to_implement: You need to be ingesting the logs from your vulnerability scanner.
-author: David Dorsey, Splunk
+id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5
 inputs:
 - dest
+name: Get Vulnerability Logs For Endpoint
 search: '| from datamodel Vulnerabilities.Vulnerabilities | search dest=$dest$'
 tags:
-  analytics_story:
+  analytic_story:
   - ColdRoot MacOS RAT
   - DHS Report TA18-074A
   - 'Emotet Malware  DHS Report TA18-201A '
@@ -20,3 +19,7 @@ tags:
   - Ransomware
   - SamSam Ransomware
   - Windows Log Manipulation
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/investigate_aws_ecr_container_listing_activity.yml b/response_tasks/deprecated/investigate_aws_ecr_container_listing_activity.yml
index 435c5c701a..34610db886 100644
--- a/response_tasks/deprecated/investigate_aws_ecr_container_listing_activity.yml
+++ b/response_tasks/deprecated/investigate_aws_ecr_container_listing_activity.yml
@@ -1,6 +1,4 @@
-name: Investigate AWS ECR container listing activity
-id: 6027623f-7d10-4847-af3b-8d7e87970451
-version: 1
+author: Rod Soto, Rico Valdez, Splunk
 date: '2020-02-20'
 description: This search lists all the users performing a list image operation on
   AWS Elastic Container Registry. Listing source user, image id, source IP, user type,
@@ -9,9 +7,10 @@ description: This search lists all the users performing a list image operation o
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.You must also install Cloud Infrastructure Data Model.
-author: Rod Soto, Rico Valdez, Splunk
+id: 6027623f-7d10-4847-af3b-8d7e87970451
 inputs:
 - Compute.event_name
+name: Investigate AWS ECR container listing activity
 search: '|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute
   where Compute.user_type!="AssumeRole" AND Compute.event_name="ListImages" by Compute.image_id
   Compute.src_user Compute.src Compute.http_user_agent Compute.user_type | rename
@@ -19,5 +18,9 @@ search: '|tstats count min(_time) as firstTime max(_time) as lastTime FROM datam
   as unique_ua_count by src_user, image_id, src, user_type | where unique_ua_count
   > 1'
 tags:
-  analytics_story:
+  analytic_story:
   - Container Implantation Monitoring and Investigation
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/investigate_cloud_compute_instance_activities.yml b/response_tasks/deprecated/investigate_cloud_compute_instance_activities.yml
index 417a96ff09..170faf5739 100644
--- a/response_tasks/deprecated/investigate_cloud_compute_instance_activities.yml
+++ b/response_tasks/deprecated/investigate_cloud_compute_instance_activities.yml
@@ -1,15 +1,18 @@
-name: Investigate Cloud Compute Instance Activities
-id: 33a95cf2-900c-4636-8fca-5c5f71474720
-version: 1
+author: David Dorsey, Splunk
 date: '2018-03-12'
 description: This search returns a logs of events that operated on the compute instance.
 how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
   and have the Security Research cloud data model installed.
-author: David Dorsey, Splunk
+id: 33a95cf2-900c-4636-8fca-5c5f71474720
 inputs:
 - dest
+name: Investigate Cloud Compute Instance Activities
 search: '| from datamodel:Cloud_Infrastructure.Compute | search dest=$dest$ | fields
   - _*'
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/investigate_user_activities_in_all_cloud_regions.yml b/response_tasks/deprecated/investigate_user_activities_in_all_cloud_regions.yml
index e162510d2a..0644c9022c 100644
--- a/response_tasks/deprecated/investigate_user_activities_in_all_cloud_regions.yml
+++ b/response_tasks/deprecated/investigate_user_activities_in_all_cloud_regions.yml
@@ -1,17 +1,20 @@
-name: Investigate User Activities In All Cloud Regions
-id: 2ef6310f-8e79-42af-b20b-b4eeaba9608a
-version: 2
+author: David Dorsey, Splunk
 date: '2019-04-30'
 description: This search lists all the logged cloud infrastructure activities by a
   specific cloud user
 how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
   and have the Security Research cloud data model installed.
-author: David Dorsey, Splunk
+id: 2ef6310f-8e79-42af-b20b-b4eeaba9608a
 inputs:
 - region
 - src_user
+name: Investigate User Activities In All Cloud Regions
 search: '| from datamodel:Cloud_Infrastructure.Compute | search user=$src_user$ |
   fields - _*'
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/deprecated/investigate_user_activities_in_single_cloud_region.yml b/response_tasks/deprecated/investigate_user_activities_in_single_cloud_region.yml
index be63a4df81..2797127b99 100644
--- a/response_tasks/deprecated/investigate_user_activities_in_single_cloud_region.yml
+++ b/response_tasks/deprecated/investigate_user_activities_in_single_cloud_region.yml
@@ -1,17 +1,20 @@
-name: Investigate User Activities In Single Cloud Region
-id: 3dc3a8e7-394b-44ae-8262-4ef8e90b723d
-version: 2
+author: David Dorsey, Splunk
 date: '2019-04-30'
 description: This search lists all the logged cloud infrastructure activities by a
   specific cloud user in a specific cloud region
 how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs
   and have the Security Research cloud data model installed.
-author: David Dorsey, Splunk
+id: 3dc3a8e7-394b-44ae-8262-4ef8e90b723d
 inputs:
 - region
 - src_user
+name: Investigate User Activities In Single Cloud Region
 search: '| from datamodel:Cloud_Infrastructure.Compute | search region=$region$ user=$src_user$
   | fields - _*'
 tags:
-  analytics_story:
+  analytic_story:
   - Cloud Cryptomining
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/deprecated/investigate_web_activity_from_host.yml b/response_tasks/deprecated/investigate_web_activity_from_host.yml
index e8b14e65f3..51d0afe0ab 100644
--- a/response_tasks/deprecated/investigate_web_activity_from_host.yml
+++ b/response_tasks/deprecated/investigate_web_activity_from_host.yml
@@ -1,18 +1,17 @@
-name: Investigate Web Activity From Host
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-11-09'
 description: This search allows you to find all the web activity from a specific host.
   During an investigation, it is important to profile web activity to characterize
   user or host activity.
 how_to_implement: To successfully implement this search you must be ingesting your
   web traffic and populating the Web data model.
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22
 inputs:
 - dest
+name: Investigate Web Activity From Host
 search: '| from datamodel Web.Web | search src=$dest$'
 tags:
-  analytics_story:
+  analytic_story:
   - Brand Monitoring
   - DHS Report TA18-074A
   - Disabling Security Tools
@@ -33,3 +32,7 @@ tags:
   - Windows Log Manipulation
   - Windows Persistence Techniques
   - Windows Privilege Escalation
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/investigate_web_activity_from_src_ip.yml b/response_tasks/deprecated/investigate_web_activity_from_src_ip.yml
index f23344f44a..1d24aadc57 100644
--- a/response_tasks/deprecated/investigate_web_activity_from_src_ip.yml
+++ b/response_tasks/deprecated/investigate_web_activity_from_src_ip.yml
@@ -1,18 +1,21 @@
-name: Investigate Web Activity From src ip
-id: 2f5b960b-71df-49c0-affc-74992ce60e45
-version: 1
+author: David Dorsey, Splunk
 date: '2018-06-15'
 description: This search searches for all web activity from a specific host. During
   an investigation, it is important to profile web activity to characterize user or
   host activity.
 how_to_implement: To successfully implement this search, you must be ingesting your
   web traffic and populating the web data model.
-author: David Dorsey, Splunk
+id: 2f5b960b-71df-49c0-affc-74992ce60e45
 inputs:
 - src_ip
+name: Investigate Web Activity From src ip
 search: '| from datamodel Web.Web | search src=$src_ip$'
 tags:
-  analytics_story:
+  analytic_story:
   - ColdRoot MacOS RAT
   - Dynamic DNS
   - Splunk Enterprise Vulnerability CVE-2018-11409
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/process_chain_analysis.yml b/response_tasks/deprecated/process_chain_analysis.yml
index 7baae05075..683b30aea7 100644
--- a/response_tasks/deprecated/process_chain_analysis.yml
+++ b/response_tasks/deprecated/process_chain_analysis.yml
@@ -1,14 +1,28 @@
-name: Process Chain Analysis
-id: c5506139-ef86-4cd9-8535-0512aa732e79
-version: 1
-date: '2020-04-29'
-description: Analyze the Process Chain and identify the malicious file. By analyzing the parent process guid and searching for the process guid, the spawning process chain can be identified.
 author: Patrick Bareiss, Splunk
+date: '2020-04-29'
+description: Analyze the Process Chain and identify the malicious file. By analyzing
+  the parent process guid and searching for the process guid, the spawning process
+  chain can be identified.
+id: c5506139-ef86-4cd9-8535-0512aa732e79
 inputs:
 - process_guid
-search: '`sysmon` EventCode=1 NOT process=*Splunk* | rename process_guid AS out_process_guid process_name AS out_process_name parent_process_guid AS out_parent_process_guid parent_process_name AS out_parent_process_name | stats count by out_process_guid out_process_name out_parent_process_guid out_parent_process_name | eval join_process_guid = out_process_guid | join join_process_guid [ search `sysmon` process_guid={process_guid} EventCode=1 | rename process_name AS sub_process_name process_guid AS sub_process_guid parent_process_name AS sub_parent_process_name parent_process_guid AS sub_parent_process_guid | stats count by sub_process_name sub_process_guid sub_parent_process_name sub_parent_process_guid | eval join_process_guid = sub_parent_process_guid] | rename sub_process_guid AS process_guid sub_process_name AS process_name out_process_guid AS parent_process_guid out_process_name AS parent_process_name out_parent_process_guid AS grandparent_process_guid out_parent_process_name AS grandparent_process_name | stats count by process_guid process_name parent_process_guid parent_process_name grandparent_process_guid grandparent_process_name | head 1 | fields - count'
+name: Process Chain Analysis
+search: '`sysmon` EventCode=1 NOT process=*Splunk* | rename process_guid AS out_process_guid
+  process_name AS out_process_name parent_process_guid AS out_parent_process_guid
+  parent_process_name AS out_parent_process_name | stats count by out_process_guid
+  out_process_name out_parent_process_guid out_parent_process_name | eval join_process_guid
+  = out_process_guid | join join_process_guid [ search `sysmon` process_guid={process_guid}
+  EventCode=1 | rename process_name AS sub_process_name process_guid AS sub_process_guid
+  parent_process_name AS sub_parent_process_name parent_process_guid AS sub_parent_process_guid
+  | stats count by sub_process_name sub_process_guid sub_parent_process_name sub_parent_process_guid
+  | eval join_process_guid = sub_parent_process_guid] | rename sub_process_guid AS
+  process_guid sub_process_name AS process_name out_process_guid AS parent_process_guid
+  out_process_name AS parent_process_name out_parent_process_guid AS grandparent_process_guid
+  out_parent_process_name AS grandparent_process_name | stats count by process_guid
+  process_name parent_process_guid parent_process_name grandparent_process_guid grandparent_process_name
+  | head 1 | fields - count'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Collection and Staging
   - Command and Control
@@ -39,3 +53,7 @@ tags:
   - Windows Persistence Techniques
   - Windows Privilege Escalation
   - Windows Service Abuse
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/deprecated/quarantaine_infected_host.yml b/response_tasks/deprecated/quarantaine_infected_host.yml
index 76fbf36d7f..c34e843dd7 100644
--- a/response_tasks/deprecated/quarantaine_infected_host.yml
+++ b/response_tasks/deprecated/quarantaine_infected_host.yml
@@ -1,9 +1,14 @@
-name: Quarantaine Infected Host
-id: 60c4cfa5-81b7-44e2-9ad4-71524e4a3e78
-version: 1
-date: '2020-04-29'
-description: Quarantine the infected hosts in order to stop the malware from spreading to further hosts. This is a short-term containment with the focus on limiting the damage as soon as possible.
 author: Patrick Bareiss, Splunk
+date: '2020-04-29'
+description: Quarantine the infected hosts in order to stop the malware from spreading
+  to further hosts. This is a short-term containment with the focus on limiting the
+  damage as soon as possible.
+id: 60c4cfa5-81b7-44e2-9ad4-71524e4a3e78
+name: Quarantaine Infected Host
 tags:
-  analytics_story:
+  analytic_story:
   - Ransomware
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/dns_hijack_enrichment.yml b/response_tasks/dns_hijack_enrichment.yml
index 5e80570f74..8df22b836f 100644
--- a/response_tasks/dns_hijack_enrichment.yml
+++ b/response_tasks/dns_hijack_enrichment.yml
@@ -1,6 +1,4 @@
-name: DNS Hijack Enrichment
-id: c096f721-8842-42ce-bfc7-74bd9a72c712
-version: 1
+author: Bhavin Patel, Splunk
 date: '2019-02-14'
 description: 'This Playbook is part of the Splunk Analytic Story called DNS Hijacking.
   It is made to be run when the Detection Search within that story called "DNS Record
@@ -26,12 +24,16 @@ how_to_implement: 'If Splunk>Phantom is also configured in your environment, a P
   (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\
 
   '
-author: Bhavin Patel, Splunk
+id: c096f721-8842-42ce-bfc7-74bd9a72c712
+name: DNS Hijack Enrichment
 playbook:
   name: dns_hijack_enrichment
   url_json: https://github.com/phantomcyber/playbooks/blob/4.6/dns_hijack_enrichment.json
   url_python: https://github.com/phantomcyber/playbooks/blob/4.6/dns_hijack_enrichment.py
-
 tags:
-  analytics_story:
+  analytic_story:
   - DNS Hijacking
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/domain_certificate_investigation.yml b/response_tasks/domain_certificate_investigation.yml
index 1f93a68844..9800bdbde0 100644
--- a/response_tasks/domain_certificate_investigation.yml
+++ b/response_tasks/domain_certificate_investigation.yml
@@ -1,6 +1,4 @@
-name: Domain Certificate Investigation
-id: c096f721-8842-42ce-2fc7-742d8272b712
-version: 1
+author: Philip Royer, Splunk
 date: '2019-04-29'
 description: Investigate domain names and URLs of a potentially malicious website.
   These domain names and URLs could come from anywhere, but this Playbook was designed
@@ -16,11 +14,16 @@ how_to_implement: To successfully implement this phantom playbook, you must inte
   Enterprise Security with Phantom. Configure this playbook in the correlation search
   `Detect DNS requests to Phishing Sites leveraging EvilGinx2` ,as an adaptive response
   action.
-author: Philip Royer, Splunk
+id: c096f721-8842-42ce-2fc7-742d8272b712
+name: Domain Certificate Investigation
 playbook:
   name: lets_encrypt_domain_investigate
   url_json: https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json
   url_python: https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py
 tags:
-  analytics_story:
+  analytic_story:
   - Common Phishing Frameworks
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/excessive_account_lockouts_enrichment_and_response.yml b/response_tasks/excessive_account_lockouts_enrichment_and_response.yml
index a729fb48cb..6ee46e3900 100644
--- a/response_tasks/excessive_account_lockouts_enrichment_and_response.yml
+++ b/response_tasks/excessive_account_lockouts_enrichment_and_response.yml
@@ -1,6 +1,4 @@
-name: Excessive Account Lockouts Enrichment And Response
-id: ab62b5c1-95d4-4e71-8fd7-53a55db33da4
-version: 2
+author: Bhavin Patel, Splunk
 date: '2019-02-14'
 description: This Playbook is part of the Splunk Analytic Story called Account Monitoring
   and Controls. It is made to be run when the Detection Search within that story called
@@ -13,12 +11,16 @@ description: This Playbook is part of the Splunk Analytic Story called Account M
   to login to the source of the lockouts, gather more information, and allow Phantom
   to shutdown the server after prompting an analyst or responder.
 how_to_implement: Import playbook into phantom
-author: Bhavin Patel, Splunk
+id: ab62b5c1-95d4-4e71-8fd7-53a55db33da4
+name: Excessive Account Lockouts Enrichment And Response
 playbook:
   name: excessive_account_lockouts_enrichment_and_response
   url_json: https://github.com/phantomcyber/playbooks/blob/4.6/excessive_account_lockouts_enrichment_and_response.json
   url_python: https://github.com/phantomcyber/playbooks/blob/4.6/excessive_account_lockouts_enrichment_and_response.py
-
 tags:
-  analytics_story:
+  analytic_story:
   - Account Monitoring and Controls
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/gcp_kubernetes_activity_by_src_ip.yml b/response_tasks/gcp_kubernetes_activity_by_src_ip.yml
index 490faf1631..6135ae18ee 100644
--- a/response_tasks/gcp_kubernetes_activity_by_src_ip.yml
+++ b/response_tasks/gcp_kubernetes_activity_by_src_ip.yml
@@ -1,6 +1,4 @@
-name: GCP Kubernetes activity by src ip
-id: c00e7626-92cc-4e06-9a51-b6db0a50bd1f
-version: 1
+author: Rod Soto, Splunk
 date: '2020-04-13'
 description: This search provides investigation data about requests via user agent,
   authentication request URI, resource path and cluster name data against Kubernetes
@@ -9,15 +7,21 @@ how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or late
   then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
   You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection
   to filter out FPs.
-author: Rod Soto, Splunk
+id: c00e7626-92cc-4e06-9a51-b6db0a50bd1f
 inputs:
 - src_ip
-search: sourcetype="google:gcp:pubsub:message" | rename data.protoPayload.requestMetadata.callerIp as src_ip | search src_ip =$src_ip$ | stats count min(_time)
-  as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_names
-  values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent)
+name: GCP Kubernetes activity by src ip
+search: sourcetype="google:gcp:pubsub:message" | rename data.protoPayload.requestMetadata.callerIp
+  as src_ip | search src_ip =$src_ip$ | stats count min(_time) as firstTime max(_time)
+  as lastTime values(data.protoPayload.methodName) as method_names values(data.protoPayload.resourceName)
+  as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent)
   as http_user_agent values(data.protoPayload.authenticationInfo.principalEmail) as
   user values(data.protoPayload.status.message) by src_ip data.resource.labels.cluster_name
   data.resource.type
 tags:
-  analytics_story:
+  analytic_story:
   - Kubernetes Scanning Activity
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_all_aws_activity_from_city.yml b/response_tasks/get_all_aws_activity_from_city.yml
index 5724dcaf9d..aef5ca129a 100644
--- a/response_tasks/get_all_aws_activity_from_city.yml
+++ b/response_tasks/get_all_aws_activity_from_city.yml
@@ -1,6 +1,4 @@
-name: Get All AWS Activity From City
-id: 0abeeb40-1255-4b68-91d1-7a7eb410c4b8
-version: 1
+author: David Dorsey, Splunk
 date: '2018-03-19'
 description: This search retrieves all the activity from a specific city and will
   create a table containing the time, city, ARN, username, the type of user, the source
@@ -9,14 +7,19 @@ description: This search retrieves all the activity from a specific city and wil
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: David Dorsey, Splunk
+id: 0abeeb40-1255-4b68-91d1-7a7eb410c4b8
 inputs:
 - City
+name: Get All AWS Activity From City
 search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search
   City=$City$ | spath output=user path=userIdentity.arn | spath output=awsUserName
   path=userIdentity.userName | spath output=userType path=userIdentity.type | rename
   sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip,
   awsRegion, eventName, errorCode'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Suspicious Provisioning Activities
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_all_aws_activity_from_country.yml b/response_tasks/get_all_aws_activity_from_country.yml
index beb035556c..0834657f80 100644
--- a/response_tasks/get_all_aws_activity_from_country.yml
+++ b/response_tasks/get_all_aws_activity_from_country.yml
@@ -1,6 +1,4 @@
-name: Get All AWS Activity From Country
-id: e763cdb9-00da-41e0-9bda-444debc9501a
-version: 1
+author: David Dorsey, Splunk
 date: '2018-03-19'
 description: This search retrieves all the activity from a specific country and will
   create a table containing the time, country, ARN, username, the type of user, the
@@ -9,14 +7,19 @@ description: This search retrieves all the activity from a specific country and
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: David Dorsey, Splunk
+id: e763cdb9-00da-41e0-9bda-444debc9501a
 inputs:
 - Country
+name: Get All AWS Activity From Country
 search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search
   Country=$Country$ | spath output=user path=userIdentity.arn | spath output=awsUserName
   path=userIdentity.userName | spath output=userType path=userIdentity.type | rename
   sourceIPAddress as src_ip | table _time, Country, user, userName, userType, src_ip,
   awsRegion, eventName, errorCode'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Suspicious Provisioning Activities
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_all_aws_activity_from_ip_address.yml b/response_tasks/get_all_aws_activity_from_ip_address.yml
index 01863254e1..2c90cd6481 100644
--- a/response_tasks/get_all_aws_activity_from_ip_address.yml
+++ b/response_tasks/get_all_aws_activity_from_ip_address.yml
@@ -1,6 +1,4 @@
-name: Get All AWS Activity From IP Address
-id: 446ec87a-85c6-40d4-b060-bea4498281d6
-version: 1
+author: David Dorsey, Splunk
 date: '2018-03-19'
 description: This search retrieves all the activity from a specific IP address and
   will create a table containing the time, ARN, username, the type of user, the IP
@@ -9,20 +7,24 @@ description: This search retrieves all the activity from a specific IP address a
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: David Dorsey, Splunk
+id: 446ec87a-85c6-40d4-b060-bea4498281d6
 inputs:
 - src_ip
+name: Get All AWS Activity From IP Address
 search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search
   src_ip=$src_ip$ | spath output=user path=userIdentity.arn | spath output=awsUserName
   path=userIdentity.userName | spath output=userType path=userIdentity.type | rename
   sourceIPAddress as src_ip | table _time, user, userName, userType, src_ip, awsRegion,
   eventName, errorCode'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - AWS Suspicious Provisioning Activities
   - Command and Control
   - Suspicious AWS S3 Activities
   - Suspicious AWS Traffic
   - Suspicious Cloud Instance Activities
-
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_all_aws_activity_from_region.yml b/response_tasks/get_all_aws_activity_from_region.yml
index 8a0a73b1a3..d11a37d7ce 100644
--- a/response_tasks/get_all_aws_activity_from_region.yml
+++ b/response_tasks/get_all_aws_activity_from_region.yml
@@ -1,6 +1,4 @@
-name: Get All AWS Activity From Region
-id: 5b794bef-1743-4f6f-804a-43915a2702ff
-version: 1
+author: David Dorsey, Splunk
 date: '2018-03-19'
 description: This search retrieves all the activity from a specific geographic region
   and will create a table containing the time, geographic region, ARN, username, the
@@ -9,14 +7,19 @@ description: This search retrieves all the activity from a specific geographic r
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: David Dorsey, Splunk
+id: 5b794bef-1743-4f6f-804a-43915a2702ff
 inputs:
 - Region
+name: Get All AWS Activity From Region
 search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search
   Region=$Region$ | spath output=user path=userIdentity.arn | spath output=awsUserName
   path=userIdentity.userName | spath output=userType path=userIdentity.type | rename
   sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip,
   awsRegion, eventName, errorCode'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Suspicious Provisioning Activities
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_backup_logs_for_endpoint.yml b/response_tasks/get_backup_logs_for_endpoint.yml
index 30d16c96e0..6201da7bdc 100644
--- a/response_tasks/get_backup_logs_for_endpoint.yml
+++ b/response_tasks/get_backup_logs_for_endpoint.yml
@@ -1,16 +1,19 @@
-name: Get Backup Logs For Endpoint
-id: fdcfb369-1725-4c24-824a-22972d7f0d44
-version: 1
+author: David Dorsey, Splunk
 date: '2017-09-14'
 description: This search will tell you the backup status from your netbackup_logs
   of a specific endpoint for the last week.
 how_to_implement: You must be ingesting your backup logs.
-author: David Dorsey, Splunk
+id: fdcfb369-1725-4c24-824a-22972d7f0d44
 inputs:
 - dest
+name: Get Backup Logs For Endpoint
 search: '| search sourcetype="netbackup_logs" COMPUTERNAME=$dest$ | rename COMPUTERNAME
   as dest, MESSAGE as signature | table _time, dest, signature'
 tags:
-  analytics_story:
+  analytic_story:
   - Ransomware
   - SamSam Ransomware
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_certificate_logs_for_a_domain.yml b/response_tasks/get_certificate_logs_for_a_domain.yml
index a0838f51f4..c3bfe3fbf9 100644
--- a/response_tasks/get_certificate_logs_for_a_domain.yml
+++ b/response_tasks/get_certificate_logs_for_a_domain.yml
@@ -1,6 +1,4 @@
-name: Get Certificate logs for a domain
-id: bc91a8cf-35e7-4bb2-2240-e756cc06fd73
-version: 2
+author: Bhavin Patel, Splunk
 date: '2019-04-29'
 description: This search queries the Certificates datamodel and give you all the information
   for a specific domain. Please note that the certificates issued by "Let's Encrypt"
@@ -8,9 +6,10 @@ description: This search queries the Certificates datamodel and give you all the
 how_to_implement: You must be ingesting your certificates or SSL logs from your network
   traffic into your Certificates datamodel. Please note the wildcard(*) before domain
   in the search syntax, we use to match for all domain and subdomain combinations
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-2240-e756cc06fd73
 inputs:
 - domain
+name: Get Certificate logs for a domain
 search: '| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime
   FROM datamodel=Certificates.All_Certificates where All_Certificates.SSL.ssl_subject_common_name=*$domain$  by
   All_Certificates.dest All_Certificates.src All_Certificates.SSL.ssl_issuer_common_name
@@ -18,5 +17,9 @@ search: '| tstats `summariesonly` count min(_time) as firstTime max(_time) as la
   | `drop_dm_object_name(SSL)` | rename ssl_subject_common_name as domain | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - Common Phishing Frameworks
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_dns_server_history_for_a_host.yml b/response_tasks/get_dns_server_history_for_a_host.yml
index c392604282..674b861d5d 100644
--- a/response_tasks/get_dns_server_history_for_a_host.yml
+++ b/response_tasks/get_dns_server_history_for_a_host.yml
@@ -1,6 +1,4 @@
-name: Get DNS Server History for a host
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-11-09'
 description: While investigating any detections it is important to understand which
   and how many DNS servers a host has connected to in the past. This search uses data
@@ -8,14 +6,15 @@ description: While investigating any detections it is important to understand wh
   host has connected to the previous 24 hours.
 how_to_implement: To successfully implement this search, you must be ingesting your
   DNS traffic
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72
 inputs:
 - src_ip
+name: Get DNS Server History for a host
 search: '| search tag=dns src_ip=$src_ip$ dest_port=53 | streamstats time_window=1d
   count values(dest_ip) as dcip by src_ip | table date_mday src_ip dcip count | sort
   -count'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Command and Control
   - DNS Hijacking
@@ -26,3 +25,7 @@ tags:
   - Prohibited Traffic Allowed or Protocol Mismatch
   - Suspicious AWS Traffic
   - Suspicious DNS Traffic
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_dns_traffic_ratio.yml b/response_tasks/get_dns_traffic_ratio.yml
index 1f2ba1a2f3..a42f7b008f 100644
--- a/response_tasks/get_dns_traffic_ratio.yml
+++ b/response_tasks/get_dns_traffic_ratio.yml
@@ -1,6 +1,4 @@
-name: Get DNS traffic ratio
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-11-09'
 description: 'This search calculates the ratio of DNS traffic originating and coming
   from a host to a list of DNS servers over the last 24 hours. A high value of this
@@ -8,16 +6,18 @@ description: 'This search calculates the ratio of DNS traffic originating and co
   high volume of data out via port 53, could be an indicator of data exfiltration
   via DNS.  '
 how_to_implement: You must be ingesting your network traffic
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73
 inputs:
 - src_ip
 - dest_ip
+name: Get DNS traffic ratio
 search: '| tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out"
   sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic
-  All_Traffic.dest_port=53 by All_Traffic.src All_Traffic.dest| `drop_dm_object_name(All_Traffic)` | rename src as src_ip | rename dest as dest_ip | search src_ip=$src_ip$ | search dest_ip = $dest_ip | eval
-  ratio = (bytes_out/bytes_in) | table ratio'
+  All_Traffic.dest_port=53 by All_Traffic.src All_Traffic.dest| `drop_dm_object_name(All_Traffic)`
+  | rename src as src_ip | rename dest as dest_ip | search src_ip=$src_ip$ | search
+  dest_ip = $dest_ip | eval ratio = (bytes_out/bytes_in) | table ratio'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Command and Control
   - Data Protection
@@ -25,3 +25,7 @@ tags:
   - Hidden Cobra Malware
   - Suspicious AWS Traffic
   - Suspicious DNS Traffic
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_ec2_instance_details_by_instanceid.yml b/response_tasks/get_ec2_instance_details_by_instanceid.yml
index afb490a09f..3d926b688a 100644
--- a/response_tasks/get_ec2_instance_details_by_instanceid.yml
+++ b/response_tasks/get_ec2_instance_details_by_instanceid.yml
@@ -1,27 +1,31 @@
-name: Get EC2 Instance Details by instanceId
-id: f3db4d1b-5f33-4b01-c541-c7ah9514c242
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-02-12'
 description: This search queries AWS description logs and returns all the information
   about a specific instance via the instanceId field
 how_to_implement: In order to implement this search, you must install the AWS App
   for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later)
   and configure your AWS description inputs.
-author: Bhavin Patel, Splunk
+id: f3db4d1b-5f33-4b01-c541-c7ah9514c242
 inputs:
 - instanceId
+name: Get EC2 Instance Details by instanceId
 search: '| search sourcetype="aws:description" source="*:ec2_instances"| dedup id
-  sortby -_time |rename id as instanceId|  search instanceId=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value,"
-  = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table
-  id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time,
-  state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID,
-  instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time
-  as "Launch Time", placement as "Availability Zone", state as State, subnet_id as
-  Subnet, "tags.Name" as Name, vpc_id as VPC'
+  sortby -_time |rename id as instanceId|  search instanceId=$instanceId$ | spath
+  output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address
+  == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id,
+  placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id,
+  tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address
+  as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement
+  as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name,
+  vpc_id as VPC'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Cloud Cryptomining
   - Suspicious AWS EC2 Activities
   - Unusual AWS EC2 Modifications
   - AWS Security Hub Alerts
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_ec2_launch_details.yml b/response_tasks/get_ec2_launch_details.yml
index 33b30b37d5..f46b534a8d 100644
--- a/response_tasks/get_ec2_launch_details.yml
+++ b/response_tasks/get_ec2_launch_details.yml
@@ -1,23 +1,25 @@
-name: Get EC2 Launch Details
-id: 0e40fe83-3edb-4d86-8206-8fed36529ca6
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-03-12'
 description: This search returns some of the launch details for a EC2 instance.
 how_to_implement: In order to implement this search, you must install the AWS App
   for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later)
   and configure your AWS description inputs.
-author: Bhavin Patel, Splunk
+id: 0e40fe83-3edb-4d86-8206-8fed36529ca6
 inputs:
 - dest
-search: '| search sourcetype=aws:cloudtrail dest=$dest$
-  |rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId
-  as dest, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress,
-  responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture
+name: Get EC2 Launch Details
+search: '| search sourcetype=aws:cloudtrail dest=$dest$ |rename userIdentity.arn as
+  arn, responseElements.instancesSet.items{}.instanceId as dest, responseElements.instancesSet.items{}.privateIpAddress
+  as privateIpAddress, responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture
   as architecture, responseElements.instancesSet.items{}.keyName as keyName | table
   arn, awsRegion, dest, architecture, privateIpAddress, amiID, keyName'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Cloud Cryptomining
   - Suspicious AWS EC2 Activities
   - AWS Security Hub Alerts
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_email_info.yml b/response_tasks/get_email_info.yml
index 48e051569b..3e7980027e 100644
--- a/response_tasks/get_email_info.yml
+++ b/response_tasks/get_email_info.yml
@@ -1,16 +1,19 @@
-name: Get Email Info
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd75
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-11-09'
 description: This search returns all the information Splunk might have collected a
   specific email message over the last 2 hours.
 how_to_implement: To successfully implement this search you must be ingesting your
   email logs or capturing unencrypted network traffic which contains email communications.
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd75
 inputs:
 - message_id
+name: Get Email Info
 search: '| from datamodel Email.All_Email | search message_id=$message_id$'
 tags:
-  analytics_story:
+  analytic_story:
   - Brand Monitoring
   - Suspicious Emails
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_emails_from_specific_sender.yml b/response_tasks/get_emails_from_specific_sender.yml
index e7c4b4f3f8..d522b40bde 100644
--- a/response_tasks/get_emails_from_specific_sender.yml
+++ b/response_tasks/get_emails_from_specific_sender.yml
@@ -1,18 +1,21 @@
-name: Get Emails From Specific Sender
-id: 5df39b3f-447d-4869-b673-8f45ad4616fe
-version: 1
+author: David Dorsey, Splunk
 date: '2017-11-09'
 description: This search returns all the emails from a specific sender over the last
   24 and next hours.
 how_to_implement: To successfully implement this search you must ingest your email
   logs or capture unencrypted email communications within network traffic, and populate
   the Email data model.
-author: David Dorsey, Splunk
+id: 5df39b3f-447d-4869-b673-8f45ad4616fe
 inputs:
-  - src_user
+- src_user
+name: Get Emails From Specific Sender
 search: '| from datamodel Email.All_Email | search src_user=$src_user$'
 tags:
-  analytics_story:
+  analytic_story:
   - Brand Monitoring
   - Suspicious Emails
   - Web Fraud Detection
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml b/response_tasks/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml
index 351dc4c90b..03e1d288ec 100644
--- a/response_tasks/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml
+++ b/response_tasks/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml
@@ -1,6 +1,4 @@
-name: Get First Occurrence and Last Occurrence of a MAC Address
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd33
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-09-13'
 description: This search allows you to gather more context around a notable which
   has detected a new device connecting to your network. Use this search to determine
@@ -8,13 +6,18 @@ description: This search allows you to gather more context around a notable whic
   your network.
 how_to_implement: To successfully implement this search, you must be ingesting the
   logs from your DHCP server.
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd33
 inputs:
 - src_mac
+name: Get First Occurrence and Last Occurrence of a MAC Address
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST
   All_Sessions.All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user
   | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - Asset Tracking
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_history_of_email_sources.yml b/response_tasks/get_history_of_email_sources.yml
index e5e615dc8e..91b9be36ff 100644
--- a/response_tasks/get_history_of_email_sources.yml
+++ b/response_tasks/get_history_of_email_sources.yml
@@ -1,6 +1,4 @@
-name: Get History Of Email Sources
-id: ddc7af28-c34d-4392-af93-7f29a4e8806c
-version: 1
+author: Rico Valdez, Splunk
 date: '2019-02-21'
 description: This search returns a list of all email sources seen in the 48 hours
   prior to the notable event to 24 hours after, and the number of emails from each
@@ -8,15 +6,16 @@ description: This search returns a list of all email sources seen in the 48 hour
 how_to_implement: To successfully implement this search you must ingest your email
   logs or capture unencrypted email communications within network traffic, and populate
   the Email data model.
-author: Rico Valdez, Splunk
+id: ddc7af28-c34d-4392-af93-7f29a4e8806c
 inputs:
 - src
+name: Get History Of Email Sources
 search: '|tstats `security_content_summariesonly` values(All_Email.dest) as dest values(All_Email.recipient)
   as recepient  min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email
   by All_Email.src |`drop_dm_object_name(All_Email)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | search src=$src$'
 tags:
-  analytics_story:
+  analytic_story:
   - 'Emotet Malware  DHS Report TA18-201A '
   - Hidden Cobra Malware
   - Lateral Movement
@@ -24,4 +23,8 @@ tags:
   - Orangeworm Attack Group
   - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
   - Ransomware
-  - SamSam Ransomware
\ No newline at end of file
+  - SamSam Ransomware
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_logon_rights_modifications_for_endpoint.yml b/response_tasks/get_logon_rights_modifications_for_endpoint.yml
index 8bf608d76d..7fb0ce4cc2 100644
--- a/response_tasks/get_logon_rights_modifications_for_endpoint.yml
+++ b/response_tasks/get_logon_rights_modifications_for_endpoint.yml
@@ -1,17 +1,20 @@
-name: Get Logon Rights Modifications For Endpoint
-id: 03bffe94-ec7a-4cbe-b677-6af40d1c4505
-version: 2
+author: David Dorsey, Splunk
 date: '2017-09-12'
 description: This search allows you to retrieve any modifications to logon rights
   associated with a specific host.
 how_to_implement: To successfully implement this search you must be ingesting your
   Windows event logs
-author: David Dorsey, Splunk
+id: 03bffe94-ec7a-4cbe-b677-6af40d1c4505
 inputs:
 - dest
+name: Get Logon Rights Modifications For Endpoint
 search: '| search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717)
   dest=$dest$ | rename user as "Account Modified" | table _time, dest, "Account Modified",
   Access_Right, signature'
 tags:
-  analytics_story:
+  analytic_story:
   - Account Monitoring and Controls
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_logon_rights_modifications_for_user.yml b/response_tasks/get_logon_rights_modifications_for_user.yml
index 9118a6df0f..91ce50d68d 100644
--- a/response_tasks/get_logon_rights_modifications_for_user.yml
+++ b/response_tasks/get_logon_rights_modifications_for_user.yml
@@ -1,17 +1,20 @@
-name: Get Logon Rights Modifications For User
-id: 552bc86c-f72c-4d44-b3f2-06ede13af7bb
-version: 2
+author: David Dorsey, Splunk
 date: '2019-02-27'
 description: This search allows you to retrieve any modifications to logon rights
   for a specific user account.
 how_to_implement: To successfully implement this search you must be ingesting your
   Windows event logs
-author: David Dorsey, Splunk
+id: 552bc86c-f72c-4d44-b3f2-06ede13af7bb
 inputs:
 - user
+name: Get Logon Rights Modifications For User
 search: '| search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717)
   user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified",
   Access_Right, signature'
 tags:
-  analytics_story:
+  analytic_story:
   - Account Monitoring and Controls
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_notable_history.yml b/response_tasks/get_notable_history.yml
index 1c9e9a5f52..1bd04fc26b 100644
--- a/response_tasks/get_notable_history.yml
+++ b/response_tasks/get_notable_history.yml
@@ -1,19 +1,18 @@
-name: Get Notable History
-id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7
-version: 2
+author: Bhavin Patel, Splunk
 date: '2017-09-20'
 description: This search queries the notable index and returns all the Notable Events
   for the particular destination host, giving the analyst an overview of the incidents
   that may have occurred with the host under investigation.
 how_to_implement: If you are using Enterprise Security you are likely already creating
   notable events with your correlation rules. No additional configuration is necessary.
-author: Bhavin Patel, Splunk
+id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7
 inputs:
 - dest
+name: Get Notable History
 search: '| search `notable` | search dest=$dest$ | table _time, dest, rule_name, owner,
   priority, severity, status_description'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cross Account Activity
   - AWS Cryptomining
   - AWS Network ACL Activity
@@ -82,4 +81,8 @@ tags:
   - Ryuk Ransomware
   - Suspicious Cloud Provisioning Activities
   - Suspicious GCP Storage Activities
-  - Windows DNS SIGRed CVE-2020-1350
\ No newline at end of file
+  - Windows DNS SIGRed CVE-2020-1350
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_outbound_emails_to_hidden_cobra_threat_actors.yml b/response_tasks/get_outbound_emails_to_hidden_cobra_threat_actors.yml
index 672480a6d0..c057f9797f 100644
--- a/response_tasks/get_outbound_emails_to_hidden_cobra_threat_actors.yml
+++ b/response_tasks/get_outbound_emails_to_hidden_cobra_threat_actors.yml
@@ -1,6 +1,4 @@
-name: Get Outbound Emails to Hidden Cobra Threat Actors
-id: 5df39b3f-347d-4869-b673-8r45ad4616fe
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-06-14'
 description: 'This search returns the information of the users that sent emails to
   the accounts controlled by the Hidden Cobra Threat Actors: specifically to `misswang8107@gmail.com`,
@@ -8,14 +6,19 @@ description: 'This search returns the information of the users that sent emails
 how_to_implement: To successfully implement this search you must ingest your email
   logs or capture unencrypted email communications within network traffic, and populate
   the Email data model.
-author: Bhavin Patel, Splunk
+id: 5df39b3f-347d-4869-b673-8r45ad4616fe
 inputs:
 - src_user
 - recipient
+name: Get Outbound Emails to Hidden Cobra Threat Actors
 search: '| from datamodel Email.All_Email | search recipient=misswang8107@gmail.com
   OR src_user=redhat@gmail.com | stats count earliest(_time) as firstTime, latest(_time)
   as lastTime values(dest) values(src) by src_user recipient | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)`'
 tags:
-  analytics_story:
-  - Hidden Cobra Malware
\ No newline at end of file
+  analytic_story:
+  - Hidden Cobra Malware
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_parent_process_info.yml b/response_tasks/get_parent_process_info.yml
index 6dbff7d256..168b37e231 100644
--- a/response_tasks/get_parent_process_info.yml
+++ b/response_tasks/get_parent_process_info.yml
@@ -1,6 +1,4 @@
-name: Get Parent Process Info
-id: fecf2918-670d-4f1c-872b-3d7317a41bf9
-version: 2
+author: Bhavin Patel, Splunk
 date: '2019-02-28'
 description: This search queries the Endpoint data model to give you details about
   the parent process of a process running on a host which is under investigation.
@@ -9,18 +7,18 @@ how_to_implement: You must be ingesting endpoint data that tracks process activi
   including parent-child relationships from your endpoints to populate the Endpoint
   data model in the Processes node. The command-line arguments are mapped to the "process"
   field in the Endpoint data model.
-author: Bhavin Patel, Splunk
+id: fecf2918-670d-4f1c-872b-3d7317a41bf9
 inputs:
 - parent_process_name
 - dest
+name: Get Parent Process Info
 search: '| tstats `summariesonly` count values(Processes.process) as process min(_time)
-    as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest
-| `drop_dm_object_name("Processes")` 
-| search  parent_process_name= $parent_process_name$ |search dest = $dest$
-| `security_content_ctime(firstTime)` 
-| `security_content_ctime(lastTime)`'
+  as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user
+  Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name("Processes")`
+  | search  parent_process_name= $parent_process_name$ |search dest = $dest$ | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - Collection and Staging
   - Command and Control
   - DHS Report TA18-074A
@@ -48,4 +46,8 @@ tags:
   - Windows Log Manipulation
   - Windows Persistence Techniques
   - Windows Privilege Escalation
-  - Windows Service Abuse
\ No newline at end of file
+  - Windows Service Abuse
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_process_file_activity.yml b/response_tasks/get_process_file_activity.yml
index ca681e30c1..2c532ae2ad 100644
--- a/response_tasks/get_process_file_activity.yml
+++ b/response_tasks/get_process_file_activity.yml
@@ -1,20 +1,25 @@
-name: Get Process File Activity
-id: 6a9ad4d9-6ef2-4b85-953f-a37ab256acd5
-version: 2
+author: David Dorsey, Splunk
 date: '2019-11-06'
 description: This search returns the file activity for a specific process on a specific
   endpoint
 how_to_implement: To successfully implement this search you must be ingesting endpoint
   data and populating the Endpoint data model.
-author: David Dorsey, Splunk
+id: 6a9ad4d9-6ef2-4b85-953f-a37ab256acd5
 inputs:
 - process_name
 - dest
+name: Get Process File Activity
 search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) as
   file_name values(Filesystem.dest) as dest, values(Filesystem.process_name) as process_name
-  from datamodel=Endpoint.Filesystem
-  by Filesystem.dest Filesystem.process_name Filesystem.file_path, Filesystem.action, _time | `drop_dm_object_name(Filesystem)`  | search dest=$dest$  | search process_name=$process_name$ | table _time, process_name, dest, action, file_name, file_path'
+  from datamodel=Endpoint.Filesystem by Filesystem.dest Filesystem.process_name Filesystem.file_path,
+  Filesystem.action, _time | `drop_dm_object_name(Filesystem)`  | search dest=$dest$  |
+  search process_name=$process_name$ | table _time, process_name, dest, action, file_name,
+  file_path'
 tags:
-  analytics_story:
+  analytic_story:
   - DHS Report TA18-074A
   - Suspicious Zoom Child Processes
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_process_info.yml b/response_tasks/get_process_info.yml
index 83b5ca7f57..3cb9d60fe6 100644
--- a/response_tasks/get_process_info.yml
+++ b/response_tasks/get_process_info.yml
@@ -1,25 +1,22 @@
-name: Get Process Info
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71
-version: 2
+author: Bhavin Patel, Splunk
 date: '2019-04-01'
 description: This search queries the Endpoint data model to give you details about
   the process running on a host which is under investigation. To gather the process
   info, enter the values for the process name in question and the destination IP address.
 how_to_implement: To successfully implement this search you must be ingesting endpoint
   data and populating the Endpoint data model.
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71
 inputs:
 - process_name
 - dest
+name: Get Process Info
 search: '| tstats `summariesonly` count values(Processes.process) as process min(_time)
-    as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest
-| `drop_dm_object_name("Processes")` 
-| search  process_name= $process_name$ 
-| search dest = $dest$
-| `security_content_ctime(firstTime)` 
-| `security_content_ctime(lastTime)`'
+  as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user
+  Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name("Processes")`
+  | search  process_name= $process_name$ | search dest = $dest$ | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Collection and Staging
   - Command and Control
@@ -50,3 +47,7 @@ tags:
   - Windows Persistence Techniques
   - Windows Privilege Escalation
   - Windows Service Abuse
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_process_information_for_port_activity.yml b/response_tasks/get_process_information_for_port_activity.yml
index 7eb5011af8..b53d7cd8db 100644
--- a/response_tasks/get_process_information_for_port_activity.yml
+++ b/response_tasks/get_process_information_for_port_activity.yml
@@ -1,29 +1,23 @@
-name: Get Process Information For Port Activity
-id: 9925d08f-561e-4faa-8912-e3888a842341
-version: 2
+author: Bhavin Patel, Splunk
 date: '2019-04-01'
 description: This search will return information about the process associated with
   observed network traffic to a specific destination port from a specific host.
 how_to_implement: To successfully implement this search you must be ingesting endpoint
   data that associates processes with network events and populate the Endpoint Datamodel
-author: Bhavin Patel, Splunk
+id: 9925d08f-561e-4faa-8912-e3888a842341
 inputs:
 - dest_port
 - dest
+name: Get Process Information For Port Activity
 search: '| tstats `security_content_summariesonly` count min(_time) max(_time) as
-    lastTime from datamodel=Endpoint.Processes by Processes.process_name
-    Processes.user Processes.dest Processes.process_id 
-| `drop_dm_object_name(Processes)` 
-| `security_content_ctime(firstTime)` 
-| `security_content_ctime(lastTime)` 
-| search dest=$dest$ 
-| join dest type=inner 
-    [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports by Ports.process_id Ports.src Ports.dest_port 
-    | `drop_dm_object_name(Ports)` 
-    | search dest_port=$dest_port$ 
-    | rename src as dest]'
+  lastTime from datamodel=Endpoint.Processes by Processes.process_name Processes.user
+  Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | search dest=$dest$ | join dest type=inner
+  [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports by
+  Ports.process_id Ports.src Ports.dest_port | `drop_dm_object_name(Ports)` | search
+  dest_port=$dest_port$ | rename src as dest]'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Command and Control
   - DHS Report TA18-074A
@@ -35,3 +29,7 @@ tags:
   - SamSam Ransomware
   - Suspicious AWS Traffic
   - Use of Cleartext Protocols
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_process_responsible_for_the_dns_traffic.yml b/response_tasks/get_process_responsible_for_the_dns_traffic.yml
index b2b536a8a7..3028da57f7 100644
--- a/response_tasks/get_process_responsible_for_the_dns_traffic.yml
+++ b/response_tasks/get_process_responsible_for_the_dns_traffic.yml
@@ -1,6 +1,4 @@
-name: Get Process Responsible For The DNS Traffic
-id: 910e6512-edc9-4f93-ba24-5b786f47a672
-version: 2
+author: Bhavin Patel, Splunk
 date: '2019-04-01'
 description: While investigating, an analyst will want to know what process and parent_process
   is responsible for generating suspicious DNS traffic. Use the following search and
@@ -9,18 +7,19 @@ description: While investigating, an analyst will want to know what process and
 how_to_implement: You must be ingesting endpoint data that associates processes with
   network events into the Endpoint datamodel. This can come from endpoint protection
   products such as carbon black, or endpoint data sources such as Sysmon.
-author: Bhavin Patel, Splunk
+id: 910e6512-edc9-4f93-ba24-5b786f47a672
 inputs:
 - dest
+name: Get Process Responsible For The DNS Traffic
 search: '| tstats `security_content_summariesonly` count min(_time) max(_time) as
-    lastTime from datamodel=Endpoint.Processes by Processes.parent_process
-    Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search dest = $dest$ | join dest type=inner 
-    [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where
-        Ports.dest_port=53 by Ports.process_id Ports.src 
-    | `drop_dm_object_name(Ports)` 
-    | rename src as dest]'
+  lastTime from datamodel=Endpoint.Processes by Processes.parent_process Processes.process_name
+  Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search
+  dest = $dest$ | join dest type=inner [| tstats `security_content_summariesonly`
+  count from datamodel=Endpoint.Ports where Ports.dest_port=53 by Ports.process_id
+  Ports.src | `drop_dm_object_name(Ports)` | rename src as dest]'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Network ACL Activity
   - Brand Monitoring
   - Command and Control
@@ -29,3 +28,7 @@ tags:
   - Hidden Cobra Malware
   - Suspicious AWS Traffic
   - Suspicious DNS Traffic
+  product:
+  - Splunk Phantom
+type: response
+version: 2
diff --git a/response_tasks/get_sysmon_wmi_activity_for_host.yml b/response_tasks/get_sysmon_wmi_activity_for_host.yml
index 266e3f2cb9..ad8ae0458f 100644
--- a/response_tasks/get_sysmon_wmi_activity_for_host.yml
+++ b/response_tasks/get_sysmon_wmi_activity_for_host.yml
@@ -1,20 +1,23 @@
-name: Get Sysmon WMI Activity for Host
-id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94
-version: 1
+author: Rico Valdez, Splunk
 date: '2018-10-23'
 description: This search queries Sysmon WMI events for the host of interest.
 how_to_implement: To successfully implement this search, you must be collecting Sysmon
   data using Sysmon version 6.1 or greater and have Sysmon configured to generate
   events for WMI activity. In addition, you must have at least version 6.0.4 of the
   Sysmon TA installed to properly parse the fields.
-author: Rico Valdez, Splunk
+id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94
 inputs:
 - process
 - dest
+name: Get Sysmon WMI Activity for Host
 search: sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18
-  EventCode<22 | rename host as dest | search dest=$dest$| table _time, dest, user, Name,
-  Operation, EventType, Type, Query, Consumer, Filter
+  EventCode<22 | rename host as dest | search dest=$dest$| table _time, dest, user,
+  Name, Operation, EventType, Type, Query, Consumer, Filter
 tags:
-  analytics_story:
+  analytic_story:
   - Ransomware
   - Suspicious WMI Use
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/get_web_session_information_via_session_id.yml b/response_tasks/get_web_session_information_via_session_id.yml
index 5ce6d009df..0037a709fc 100644
--- a/response_tasks/get_web_session_information_via_session_id.yml
+++ b/response_tasks/get_web_session_information_via_session_id.yml
@@ -1,6 +1,4 @@
-name: Get Web Session Information via session id
-id: bc91a8cf-35e7-4bb2-1120-e756cc06fd89
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-10-08'
 description: This search helps an analyst investigate a notable event to find out
   more about a specific web session. The search looks for a specific web session ID
@@ -9,11 +7,16 @@ description: This search helps an analyst investigate a notable event to find ou
 how_to_implement: This search leverages data extracted from Stream:HTTP. You must
   configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment
   server.
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-1120-e756cc06fd89
 inputs:
 - session_id
-search: '| search sourcetype=stream:http session_id  = $session_id$ | stats values(url) values(http_user_agent)
-  by src_ip status'
+name: Get Web Session Information via session id
+search: '| search sourcetype=stream:http session_id  = $session_id$ | stats values(url)
+  values(http_user_agent) by src_ip status'
 tags:
-  analytics_story:
+  analytic_story:
   - Web Fraud Detection
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_aws_activities_via_region_name.yml b/response_tasks/investigate_aws_activities_via_region_name.yml
index 75f6f8379c..a0b6362121 100644
--- a/response_tasks/investigate_aws_activities_via_region_name.yml
+++ b/response_tasks/investigate_aws_activities_via_region_name.yml
@@ -1,6 +1,4 @@
-name: Investigate AWS activities via region name
-id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-02-09'
 description: This search lists all the user activities logged by CloudTrail for a
   specific region in question and will create a table of the values of parameters
@@ -8,14 +6,20 @@ description: This search lists all the user activities logged by CloudTrail for
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: Bhavin Patel, Splunk
+id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11
 inputs:
 - vendor_region
-search: '| search sourcetype=aws:cloudtrail vendor_region=$vendor_region$| rename requestParameters.instancesSet.items{}.instanceId
-  as instanceId | stats values(eventName) by user instanceId vendor_region'
+name: Investigate AWS activities via region name
+search: '| search sourcetype=aws:cloudtrail vendor_region=$vendor_region$| rename
+  requestParameters.instancesSet.items{}.instanceId as instanceId | stats values(eventName)
+  by user instanceId vendor_region'
 tags:
-  analytics_story:
+  analytic_story:
   - AWS Cryptomining
   - Cloud Cryptomining
   - Suspicious AWS EC2 Activities
   - Suspicious AWS S3 Activities
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_aws_user_activities_by_user_field.yml b/response_tasks/investigate_aws_user_activities_by_user_field.yml
index cc9aebbd48..494c69a62d 100644
--- a/response_tasks/investigate_aws_user_activities_by_user_field.yml
+++ b/response_tasks/investigate_aws_user_activities_by_user_field.yml
@@ -1,6 +1,4 @@
-name: Investigate AWS User Activities by user field
-id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76
-version: 1
+author: Bhavin Patel, Splunk
 date: '2018-03-12'
 description: This search lists all the logged CloudTrail activities by a specific
   user and will create a table containing the source of the user, the region of the
@@ -9,12 +7,17 @@ description: This search lists all the logged CloudTrail activities by a specifi
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
-author: Bhavin Patel, Splunk
+id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76
 inputs:
 - user
+name: Investigate AWS User Activities by user field
 search: '| search sourcetype=aws:cloudtrail user=$user$ | table _time userIdentity.type
   userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType '
 tags:
-  analytics_story:
+  analytic_story:
   - AWS User Monitoring
   - Suspicious Cloud Authentication Activities
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_failed_logins_for_multiple_destinations.yml b/response_tasks/investigate_failed_logins_for_multiple_destinations.yml
index 835474a349..1c7c9b6440 100644
--- a/response_tasks/investigate_failed_logins_for_multiple_destinations.yml
+++ b/response_tasks/investigate_failed_logins_for_multiple_destinations.yml
@@ -1,13 +1,12 @@
-name: Investigate Failed Logins for Multiple Destinations
-id: 097e8030-8662-4254-a735-bf0bdda696e3
-version: 1
+author: Patrick Bareiss, Splunk
 date: '2019-12-10'
 description: This search returns failed logins to multiple destinations by user.
 how_to_implement: To successfully implement this search you need to be ingesting authentication
   logs from your various systems and populating the Authentication data model.
-author: Patrick Bareiss, Splunk
+id: 097e8030-8662-4254-a735-bf0bdda696e3
 inputs:
 - user
+name: Investigate Failed Logins for Multiple Destinations
 search: '| tstats count `security_content_summariesonly` earliest(_time) as first_login
   latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest)
   AS Authentication.dest values(Authentication.app) AS Authentication.app  from datamodel=Authentication
@@ -15,5 +14,9 @@ search: '| tstats count `security_content_summariesonly` earliest(_time) as firs
   > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)`
   | `drop_dm_object_name("Authentication")` | search user=$user$'
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_network_traffic_from_src_ip.yml b/response_tasks/investigate_network_traffic_from_src_ip.yml
index af381cae68..3ee19e85b5 100644
--- a/response_tasks/investigate_network_traffic_from_src_ip.yml
+++ b/response_tasks/investigate_network_traffic_from_src_ip.yml
@@ -1,16 +1,19 @@
-name: Investigate Network Traffic From src ip
-id: 9df9ca9c-a02b-4f48-9eba-0bac55179050
-version: 1
+author: David Dorsey, Splunk
 date: '2018-06-15'
 description: This search allows you to find all the network traffic from a specific
   IP address.
 how_to_implement: To successfully implement this search, you must be ingesting your
   web-traffic logs and populating the web data model.
-author: David Dorsey, Splunk
+id: 9df9ca9c-a02b-4f48-9eba-0bac55179050
 inputs:
 - src_ip
+name: Investigate Network Traffic From src ip
 search: '| from datamodel Network_Traffic.All_Traffic | search src_ip=$src_ip$'
 tags:
-  analytics_story:
+  analytic_story:
   - ColdRoot MacOS RAT
   - Splunk Enterprise Vulnerability CVE-2018-11409
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_okta_activity_by_app.yml b/response_tasks/investigate_okta_activity_by_app.yml
index b281e92d21..54e4d103aa 100644
--- a/response_tasks/investigate_okta_activity_by_app.yml
+++ b/response_tasks/investigate_okta_activity_by_app.yml
@@ -1,15 +1,18 @@
-name: Investigate Okta Activity by app
-id: 420eb1b8-2992-45d1-80cf-0b1b2759524d
-version: 1
+author: Rico Valdez, Splunk
 date: '2020-04-02'
 description: This search returns all okta events associated with a specific app
 how_to_implement: You must be ingesting Okta logs
-author: Rico Valdez, Splunk
+id: 420eb1b8-2992-45d1-80cf-0b1b2759524d
 inputs:
 - app
+name: Investigate Okta Activity by app
 search: eventtype=okta_log app=$app$ | rename client.geographicalContext.country as
   country, client.geographicalContext.state as state, client.geographicalContext.city
   as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Okta Activity
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_okta_activity_by_ip_address.yml b/response_tasks/investigate_okta_activity_by_ip_address.yml
index 4216491b2a..7ccd7c7067 100644
--- a/response_tasks/investigate_okta_activity_by_ip_address.yml
+++ b/response_tasks/investigate_okta_activity_by_ip_address.yml
@@ -1,15 +1,18 @@
-name: Investigate Okta Activity by IP Address
-id: 56aae066-d619-477c-93e3-3fb83b2d23c3
-version: 1
+author: Rico Valdez, Splunk
 date: '2020-04-02'
 description: This search returns all okta events from a specific IP address.
 how_to_implement: You must be ingesting Okta logs
-author: Rico Valdez, Splunk
+id: 56aae066-d619-477c-93e3-3fb83b2d23c3
 inputs:
 - user
+name: Investigate Okta Activity by IP Address
 search: eventtype=okta_log src_ip={src_ip} | rename client.geographicalContext.country
   as country, client.geographicalContext.state as state, client.geographicalContext.city
   as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Okta Activity
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_pass_the_hash_attempts.yml b/response_tasks/investigate_pass_the_hash_attempts.yml
index bbfbd6efc7..91a03da224 100644
--- a/response_tasks/investigate_pass_the_hash_attempts.yml
+++ b/response_tasks/investigate_pass_the_hash_attempts.yml
@@ -1,6 +1,4 @@
-name: Investigate Pass the Hash Attempts
-id: ed3fff45-cba6-4990-983f-6fac72bee659
-version: 1
+author: Patrick Bareiss, Splunk
 date: '2019-12-10'
 description: This search hunts for dumped NTLM hashes used for pass the hash.
 how_to_implement: To successfully implement this search you need be ingesting windows
@@ -9,12 +7,18 @@ how_to_implement: To successfully implement this search you need be ingesting wi
   source, sourcetype, etc.) for Windows Security logs. Replace the macro definition
   with configurations for your Splunk environment. The search also uses a post-filter
   macro designed to filter out known false positives.
-author: Patrick Bareiss, Splunk
+id: ed3fff45-cba6-4990-983f-6fac72bee659
 inputs:
 - dest
+name: Investigate Pass the Hash Attempts
 search: '`wineventlog_security` EventCode=4624 Logon_Type=9 AuthenticationPackageName=Negotiate
   | stats count earliest(_time) as first_login latest(_time) as last_login by src_user
-  dest | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | search dest=$dest$'
+  dest | `security_content_ctime(first_login)` | `security_content_ctime(last_login)`
+  | search dest=$dest$'
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_pass_the_ticket_attempts.yml b/response_tasks/investigate_pass_the_ticket_attempts.yml
index 05bb3ded2a..60686023d6 100644
--- a/response_tasks/investigate_pass_the_ticket_attempts.yml
+++ b/response_tasks/investigate_pass_the_ticket_attempts.yml
@@ -1,6 +1,4 @@
-name: Investigate Pass the Ticket Attempts
-id: 990007ad-d798-4b29-ab2f-f0034144c937
-version: 1
+author: Patrick Bareiss, Splunk
 date: '2019-12-10'
 description: This search hunts for dumped kerberos ticket from LSASS memory.
 how_to_implement: To successfully implement this search you need to be ingesting windows
@@ -9,13 +7,18 @@ how_to_implement: To successfully implement this search you need to be ingesting
   source, sourcetype, etc.) for Windows Security logs. Replace the macro definition
   with configurations for your Splunk environment. The search also uses a post-filter
   macro designed to filter out known false positives.
-author: Patrick Bareiss, Splunk
+id: 990007ad-d798-4b29-ab2f-f0034144c937
 inputs:
 - dest
+name: Investigate Pass the Ticket Attempts
 search: '`wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user
   "(?<new_user>[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count)
-  AS max_count sum(count) AS sum_count BY new_user, dest| search dest=$dest$ | where sum_count/max_count!=2
-  | rename new_user AS user '
+  AS max_count sum(count) AS sum_count BY new_user, dest| search dest=$dest$ | where
+  sum_count/max_count!=2 | rename new_user AS user '
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_previous_unseen_user.yml b/response_tasks/investigate_previous_unseen_user.yml
index 0f80e0647b..9155d639d7 100644
--- a/response_tasks/investigate_previous_unseen_user.yml
+++ b/response_tasks/investigate_previous_unseen_user.yml
@@ -1,14 +1,13 @@
-name: Investigate Previous Unseen User
-id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad
-version: 1
+author: Patrick Bareiss, Splunk
 date: '2019-12-10'
 description: This search returns previous unseen user, which didn't log in for 30
   days.
 how_to_implement: To successfully implement this search you need to be ingesting authentication
   logs from your various systems and populating the Authentication data model.
-author: Patrick Bareiss, Splunk
+id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad
 inputs:
 - dest
+name: Investigate Previous Unseen User
 search: '| tstats count `security_content_summariesonly` earliest(_time) as first_login
   latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app)
   AS Authentication.app values(Authentication.action) AS Authentication.action from
@@ -16,7 +15,12 @@ search: '| tstats count `security_content_summariesonly` earliest(_time) as firs
   | bucket _time span=30d | stats count min(first_login) as first_login max(last_login)
   as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user
   | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)`
-  | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` | search dest=$dest$'
+  | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")`
+  | search dest=$dest$'
 tags:
-  analytics_story:
+  analytic_story:
   - Credential Dumping
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_successful_remote_desktop_authentications.yml b/response_tasks/investigate_successful_remote_desktop_authentications.yml
index 198e516d20..1d8e9c3806 100644
--- a/response_tasks/investigate_successful_remote_desktop_authentications.yml
+++ b/response_tasks/investigate_successful_remote_desktop_authentications.yml
@@ -1,23 +1,27 @@
-name: Investigate Successful Remote Desktop Authentications
-id: b6618e8e-be04-40a0-a0b9-f0bd4b6c81bc
-version: 1
+author: Jose Hernandez, Splunk
 date: '2018-12-14'
 description: 'This search returns the source, destination, and user for all successful
   remote-desktop authentications. A successful authentication after a brute-force
   attack on a destination machine is suspicious behavior. '
 how_to_implement: You must be populating the Authentication data model with security
   events from your Windows event logs.
-author: Jose Hernandez, Splunk
+id: b6618e8e-be04-40a0-a0b9-f0bd4b6c81bc
 inputs:
 - dest
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain 
-| `security_content_ctime(lastTime)` 
-| `security_content_ctime(firstTime)` 
-| `drop_dm_object_name("Authentication")` | search dest=$dest$
-| table firstTime lastTime src src_nt_domain dest user app count 
-| sort count'
+name: Investigate Successful Remote Desktop Authentications
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Authentication where Authentication.signature_id=4624
+  Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app
+  Authentication.user Authentication.signature Authentication.src_nt_domain | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `drop_dm_object_name("Authentication")`
+  | search dest=$dest$ | table firstTime lastTime src src_nt_domain dest user app
+  count | sort count'
 tags:
-  analytics_story:
+  analytic_story:
   - Hidden Cobra Malware
   - Lateral Movement
   - SamSam Ransomware
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_suspicious_strings_in_http_header.yml b/response_tasks/investigate_suspicious_strings_in_http_header.yml
index 72238e20f3..db9b482aa9 100644
--- a/response_tasks/investigate_suspicious_strings_in_http_header.yml
+++ b/response_tasks/investigate_suspicious_strings_in_http_header.yml
@@ -1,6 +1,4 @@
-name: Investigate Suspicious Strings in HTTP Header
-id: bc91a8cf-35e7-4bb2-8140-e756cc06fd89
-version: 1
+author: Bhavin Patel, Splunk
 date: '2017-10-20'
 description: This search helps an analyst investigate a notable event related to a
   potential Apache Struts exploitation. To investigate, we will want to isolate and
@@ -11,16 +9,21 @@ description: This search helps an analyst investigate a notable event related to
 how_to_implement: This particular search leverages data extracted from Stream:HTTP.
   You must configure the http stream using the Splunk Stream App on your Splunk Stream
   deployment server to extract the cs_content_type field.
-author: Bhavin Patel, Splunk
+id: bc91a8cf-35e7-4bb2-8140-e756cc06fd89
 inputs:
 - src_ip
 - dest_ip
-search: '| search sourcetype=stream:http | search src_ip=$src_ip$ | search dest_ip=$dest_ip$ | eval
-  cs_content_type_length = len(cs_content_type) | search cs_content_type_length >
-  100 | rex field="cs_content_type" (?<suspicious_strings>cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type,
+name: Investigate Suspicious Strings in HTTP Header
+search: '| search sourcetype=stream:http | search src_ip=$src_ip$ | search dest_ip=$dest_ip$
+  | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length
+  > 100 | rex field="cs_content_type" (?<suspicious_strings>cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type,
   "application"), "True", "False")  | rename suspicious_strings_found AS "Suspicious
   Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings,
   cs_content_type, cs_content_type_length, url'
 tags:
-  analytics_story:
+  analytic_story:
   - Apache Struts Vulnerability
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_user_activities_in_okta.yml b/response_tasks/investigate_user_activities_in_okta.yml
index 942d9d7e76..c8d1a3223a 100644
--- a/response_tasks/investigate_user_activities_in_okta.yml
+++ b/response_tasks/investigate_user_activities_in_okta.yml
@@ -1,15 +1,18 @@
-name: Investigate User Activities In Okta
-id: 24ff145d-4d16-420a-b047-480f2a51c403
-version: 1
+author: Rico Valdez, Splunk
 date: '2020-04-02'
 description: This search returns all okta events by a specific user
 how_to_implement: You must be ingesting Okta logs
-author: Rico Valdez, Splunk
+id: 24ff145d-4d16-420a-b047-480f2a51c403
 inputs:
 - user
+name: Investigate User Activities In Okta
 search: eventtype=okta_log user=$user$ | rename client.geographicalContext.country
   as country, client.geographicalContext.state as state, client.geographicalContext.city
   as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason
 tags:
-  analytics_story:
+  analytic_story:
   - Suspicious Okta Activity
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/investigate_web_posts_from_src.yml b/response_tasks/investigate_web_posts_from_src.yml
index 7dec8c7e08..cef7a6b8f5 100644
--- a/response_tasks/investigate_web_posts_from_src.yml
+++ b/response_tasks/investigate_web_posts_from_src.yml
@@ -1,17 +1,21 @@
-name: Investigate Web POSTs From src
-id: f5c39fac-205c-4e07-9004-8fd61ea3431a
-version: 1
+author: Jose Hernandez, Splunk
 date: '2018-12-06'
 description: 'This investigative search retrieves POST requests from a specified source
   IP or hostname. Identifying the POST requests, as well as their associated destination
   URLs and user agent(s), may help you scope and characterize the suspicious traffic. '
 how_to_implement: To successfully implement this search, you must be ingesting your
   web-traffic logs and populating the web data model.
-author: Jose Hernandez, Splunk
+id: f5c39fac-205c-4e07-9004-8fd61ea3431a
 inputs:
 - src
+name: Investigate Web POSTs From src
 search: '| tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web
-  by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| search http_method, "POST" | search src=$src$'
+  by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| search
+  http_method, "POST" | search src=$src$'
 tags:
-  analytics_story:
+  analytic_story:
   - Apache Struts Vulnerability
+  product:
+  - Splunk Phantom
+type: response
+version: 1
diff --git a/response_tasks/playbooks/accept_and_assign_event.yml b/response_tasks/playbooks/accept_and_assign_event.yml
index 9da6ddaea5..57228b7eff 100644
--- a/response_tasks/playbooks/accept_and_assign_event.yml
+++ b/response_tasks/playbooks/accept_and_assign_event.yml
@@ -1,24 +1,35 @@
-name: Accept and assign event
-id: 667b8d15-2564-4994-929d-bda2532341bf
-tags:
-  nist:
-    RS.RP
-description: |
-  Accepts the event and starts the response plan process by assigning the event to the person executing the playbook and assigns them to this task and closes this step as completed.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - set status
-  playbooks:
-    - scm: local
-      playook: Accept event and assign owner
-references:
-  - 3.2.2 Signs of an Incident - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  This task is a default accept task and allows the analyst to start the response process and start the timer for mean time to detect (MTTD).  This allows measurement of analyst review and acceptance of the task at hand.  This can be superceded by assign other tasks to individuals (if the owner is coordinating processor).  The event owner can accept the event, and then retask other sub-phase and/or task to other individuals or teams.  This is considered a procedural or policy task.
-date: '2020-07-30'
-version: 1
 author: ButterCup
+automation:
+  actions:
+  - set status
+  is_note_required: false
+  playbooks:
+  - playook: Accept event and assign owner
+    scm: local
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Accepts the event and starts the response plan process by assigning
+  the event to the person executing the playbook and assigns them to this task and
+  closes this step as completed.
+
+  '
+id: 667b8d15-2564-4994-929d-bda2532341bf
+name: Accept and assign event
+references:
+- 3.2.2 Signs of an Incident - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: 'This task is a default accept task and allows the analyst to start the
+  response process and start the timer for mean time to detect (MTTD).  This allows
+  measurement of analyst review and acceptance of the task at hand.  This can be superceded
+  by assign other tasks to individuals (if the owner is coordinating processor).  The
+  event owner can accept the event, and then retask other sub-phase and/or task to
+  other individuals or teams.  This is considered a procedural or policy task.
+
+  '
diff --git a/response_tasks/playbooks/analyze_domain_indicator_and_reputation.yml b/response_tasks/playbooks/analyze_domain_indicator_and_reputation.yml
index b1f1824785..1782b9fccb 100644
--- a/response_tasks/playbooks/analyze_domain_indicator_and_reputation.yml
+++ b/response_tasks/playbooks/analyze_domain_indicator_and_reputation.yml
@@ -1,34 +1,42 @@
-name: Analyze domain indicator and reputation
-id: 7744864c-5446-47ab-8118-4cbaa1649747
-tags:
-  nist:
-    RS.RP
-description: |
-  Validate indicator existence, reputation, detonation and determine if Known APT, Commodity, Suspicious or Not Malicious?
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-    - whois domain
-    - domain reputation
-    - hunt domain
-  playbooks:
-    - scm:
-      playbook: ""
-references:
-    - 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  These are domains that are not related to urls and should be separately reviewed.
-  1. If Splunk present, run Splunk Query on each indictor and prevelence in the environment. Summarize # of times indicator seen in the last 24 hrs, 7 days, 1 month, six months increments. Return the hosts that have used this indictor. (make an artifact for each host?)
-    a. search -> ```| stats ...```
-  1. Gather reputational, intelligence and general information regarding indicator into a note (a note for each indicator)
-  1. Perform any additional research regarding indicators and understanding what normal behaviour is or should be by using using search engines, knowledge bases etc.
-  1. Make a determination of indicator, Known APT, Commodity, Suspicous or Not Malicious and whether to tag to block indicator
-    a. This should align to a severity change (Known APT = High, Commodity = Med, Suspicous = Low, Not Malicious = Info)
-    a. hange container and artifact severity and tag artifact & indicator(s) with blocked and determination tag
-date: '2020-04-21'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - run query
+  - whois domain
+  - domain reputation
+  - hunt domain
+  is_note_required: false
+  playbooks:
+  - playbook: ''
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-04-21'
+description: 'Validate indicator existence, reputation, detonation and determine if
+  Known APT, Commodity, Suspicious or Not Malicious?
+
+  '
+id: 7744864c-5446-47ab-8118-4cbaa1649747
+name: Analyze domain indicator and reputation
+references:
+- 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "These are domains that are not related to urls and should be separately\
+  \ reviewed.\n1. If Splunk present, run Splunk Query on each indictor and prevelence\
+  \ in the environment. Summarize # of times indicator seen in the last 24 hrs, 7\
+  \ days, 1 month, six months increments. Return the hosts that have used this indictor.\
+  \ (make an artifact for each host?)\n  a. search -> ```| stats ...```\n1. Gather\
+  \ reputational, intelligence and general information regarding indicator into a\
+  \ note (a note for each indicator)\n1. Perform any additional research regarding\
+  \ indicators and understanding what normal behaviour is or should be by using using\
+  \ search engines, knowledge bases etc.\n1. Make a determination of indicator, Known\
+  \ APT, Commodity, Suspicous or Not Malicious and whether to tag to block indicator\n\
+  \  a. This should align to a severity change (Known APT = High, Commodity = Med,\
+  \ Suspicous = Low, Not Malicious = Info)\n  a. hange container and artifact severity\
+  \ and tag artifact & indicator(s) with blocked and determination tag\n"
diff --git a/response_tasks/playbooks/analyze_email_indicators_and_reputation.yml b/response_tasks/playbooks/analyze_email_indicators_and_reputation.yml
index 8c713d11e4..91aa34c7c3 100644
--- a/response_tasks/playbooks/analyze_email_indicators_and_reputation.yml
+++ b/response_tasks/playbooks/analyze_email_indicators_and_reputation.yml
@@ -1,31 +1,43 @@
-name: Analyze email indicators and reputation
-id: 9e2d3e51-2e8f-4d49-8206-fb3e5fbf6620
-tags:
-  nist:
-    RS.RP
-description: |
-  Validate email indicators existence, reputation, detonation and determine if Phish, Spam, Suspicious or Clean ?
-  Analyst should be reviewing SPF, DKIM, DMARC along with To: and Reply to: fields for non-matching data. Does the subject contain suspicious content. Is there a file or url? Does the x-origin-ip come from the same location and the sending domains? Does the email body seem too good to be true or create a sense of urgency?
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  1. If Splunk present, run Splunk Query on each indictor and prevalence in the environment. Summarize # of times indicator seen in the last 24 hrs, 7 days, 1 month, six months increments. Return the hosts that have used this indictor. (make an artifact for each host?)
-    a. search -> ```| stats ...```
-  1. Gather reputation, intelligence and general information regarding indicator into a note (a note for each indicator)
-  1. Perform any additional research regarding indicators and understanding what normal behaviour is or should be by using using search engines, knowledge bases etc.
-  1. Make a determination of indicator, Known APT, Commodity, Suspicious or Not Malicious and whether to tag to block indicator
-    a. This should align to a severity change (Known APT = High, Commodity = Med, Suspicious = Low, Not Malicious = Info)
-    a. Change container and artifact severity and tag artifact & indicator(s) with blocked and determination tag
-date: '2020-04-21'
-version: 1
 author: ButterCup
+automation:
+  actions:
+  - run query
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-04-21'
+description: 'Validate email indicators existence, reputation, detonation and determine
+  if Phish, Spam, Suspicious or Clean ?
+
+  Analyst should be reviewing SPF, DKIM, DMARC along with To: and Reply to: fields
+  for non-matching data. Does the subject contain suspicious content. Is there a file
+  or url? Does the x-origin-ip come from the same location and the sending domains?
+  Does the email body seem too good to be true or create a sense of urgency?
+
+  '
+id: 9e2d3e51-2e8f-4d49-8206-fb3e5fbf6620
+name: Analyze email indicators and reputation
+references:
+- 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "1. If Splunk present, run Splunk Query on each indictor and prevalence\
+  \ in the environment. Summarize # of times indicator seen in the last 24 hrs, 7\
+  \ days, 1 month, six months increments. Return the hosts that have used this indictor.\
+  \ (make an artifact for each host?)\n  a. search -> ```| stats ...```\n1. Gather\
+  \ reputation, intelligence and general information regarding indicator into a note\
+  \ (a note for each indicator)\n1. Perform any additional research regarding indicators\
+  \ and understanding what normal behaviour is or should be by using using search\
+  \ engines, knowledge bases etc.\n1. Make a determination of indicator, Known APT,\
+  \ Commodity, Suspicious or Not Malicious and whether to tag to block indicator\n\
+  \  a. This should align to a severity change (Known APT = High, Commodity = Med,\
+  \ Suspicious = Low, Not Malicious = Info)\n  a. Change container and artifact severity\
+  \ and tag artifact & indicator(s) with blocked and determination tag\n"
diff --git a/response_tasks/playbooks/analyze_host_indicator_and_reputation.yml b/response_tasks/playbooks/analyze_host_indicator_and_reputation.yml
index 303f8ac568..0bf45a844b 100644
--- a/response_tasks/playbooks/analyze_host_indicator_and_reputation.yml
+++ b/response_tasks/playbooks/analyze_host_indicator_and_reputation.yml
@@ -1,29 +1,37 @@
-name: Analyze host indicator and reputation
-id: be7cce5c-29b9-405c-923a-d4565705da2e
-tags:
-  nist:
-    RS.RP
-description: |
-  Validate indicator existence, reputation, detonation and determine if Known APT, Commodity, Suspicious or Not Malicious?
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-  playbooks:
-    - scm:
-      playook: ""
-references:
-    - 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  1. If Splunk present, run Splunk Query on each indictor and prevalence in the environment. Summarize # of times indicator seen in the last 24 hrs, 7 days, 1 month, six months increments. Return the hosts that have used this indictor. (make an artifact for each host?)
-    a. search -> ```| stats ...```
-  1. Gather repetitional, intelligence and general information regarding indicator into a note (a note for each indicator)
-  1. Perform any additional research regarding indicators and understanding what normal behaviour is or should be by using using search engines, knowledge bases etc.
-  1. Make a determination of indicator, Known APT, Commodity, Suspicious or Not Malicious and whether to tag to block indicator
-    a. This should align to a severity change (Known APT = High, Commodity = Med, Suspicious = Low, Not Malicious = Info)
-    a. Change container and artifact severity and tag artifact & indicator(s) with blocked and determination tag
-date: '2020-07-30'
-version: 1
 author: ButterCup
+automation:
+  actions: null
+  is_note_required: false
+  playbooks:
+  - playook: ''
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Validate indicator existence, reputation, detonation and determine if
+  Known APT, Commodity, Suspicious or Not Malicious?
+
+  '
+id: be7cce5c-29b9-405c-923a-d4565705da2e
+name: Analyze host indicator and reputation
+references:
+- 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "1. If Splunk present, run Splunk Query on each indictor and prevalence\
+  \ in the environment. Summarize # of times indicator seen in the last 24 hrs, 7\
+  \ days, 1 month, six months increments. Return the hosts that have used this indictor.\
+  \ (make an artifact for each host?)\n  a. search -> ```| stats ...```\n1. Gather\
+  \ repetitional, intelligence and general information regarding indicator into a\
+  \ note (a note for each indicator)\n1. Perform any additional research regarding\
+  \ indicators and understanding what normal behaviour is or should be by using using\
+  \ search engines, knowledge bases etc.\n1. Make a determination of indicator, Known\
+  \ APT, Commodity, Suspicious or Not Malicious and whether to tag to block indicator\n\
+  \  a. This should align to a severity change (Known APT = High, Commodity = Med,\
+  \ Suspicious = Low, Not Malicious = Info)\n  a. Change container and artifact severity\
+  \ and tag artifact & indicator(s) with blocked and determination tag\n"
diff --git a/response_tasks/playbooks/analyze_ip_address_indicator_and_reputation.yml b/response_tasks/playbooks/analyze_ip_address_indicator_and_reputation.yml
index da5c3651db..8a1a5982a9 100644
--- a/response_tasks/playbooks/analyze_ip_address_indicator_and_reputation.yml
+++ b/response_tasks/playbooks/analyze_ip_address_indicator_and_reputation.yml
@@ -1,36 +1,44 @@
-name: Analyze IP address indicator and reputation
-id: a194130b-f5a8-4bfe-b09f-35f58f4397d5
-tags:
-  nist:
-    RS.RP
-description: |
-  Validate indicator existence, reputation, detonation and determine if Known APT, Commodity, Suspicious or Not Malicious?
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-    - whois ip
-    - geolocate ip
-    - ip reputation
-    - ip intelligence
-    - hunt ip
-    - lookup ip
-  playbooks:
-    - scm:
-      playook: ""
-references:
-    - 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  1. If Splunk present, run Splunk Query on each indictor and prevalence in the environment. Summarize # of times indicator seen in the last 24 hrs, 7 days, 1 month, six months increments. Return the hosts that have used this indictor. (make an artifact for each host?)
-    a. search -> ```| stats ...```
-  1. Gather repetitional, intelligence and general information regarding indicator into a note (a note for each indicator)
-  1. Perform any additional research regarding indicators and understanding what normal behaviour is or should be by using using search engines, knowledge bases etc.
-  1. Make a determination of indicator, Known APT, Commodity, Suspicious or Not Malicious and whether to tag to block indicator
-    a. This should align to a severity change (Known APT = High, Commodity = Med, Suspicious = Low, Not Malicious = Info)
-    a. Change container and artifact severity and tag artifact & indicator(s) with blocked and determination tag
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - run query
+  - whois ip
+  - geolocate ip
+  - ip reputation
+  - ip intelligence
+  - hunt ip
+  - lookup ip
+  is_note_required: false
+  playbooks:
+  - playook: ''
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Validate indicator existence, reputation, detonation and determine if
+  Known APT, Commodity, Suspicious or Not Malicious?
+
+  '
+id: a194130b-f5a8-4bfe-b09f-35f58f4397d5
+name: Analyze IP address indicator and reputation
+references:
+- 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "1. If Splunk present, run Splunk Query on each indictor and prevalence\
+  \ in the environment. Summarize # of times indicator seen in the last 24 hrs, 7\
+  \ days, 1 month, six months increments. Return the hosts that have used this indictor.\
+  \ (make an artifact for each host?)\n  a. search -> ```| stats ...```\n1. Gather\
+  \ repetitional, intelligence and general information regarding indicator into a\
+  \ note (a note for each indicator)\n1. Perform any additional research regarding\
+  \ indicators and understanding what normal behaviour is or should be by using using\
+  \ search engines, knowledge bases etc.\n1. Make a determination of indicator, Known\
+  \ APT, Commodity, Suspicious or Not Malicious and whether to tag to block indicator\n\
+  \  a. This should align to a severity change (Known APT = High, Commodity = Med,\
+  \ Suspicious = Low, Not Malicious = Info)\n  a. Change container and artifact severity\
+  \ and tag artifact & indicator(s) with blocked and determination tag\n"
diff --git a/response_tasks/playbooks/analyze_network_indicators_and_reputation.yml b/response_tasks/playbooks/analyze_network_indicators_and_reputation.yml
index c301506212..b437a91635 100644
--- a/response_tasks/playbooks/analyze_network_indicators_and_reputation.yml
+++ b/response_tasks/playbooks/analyze_network_indicators_and_reputation.yml
@@ -1,43 +1,51 @@
-name: Analyze network indicators and reputation
-id: 710b1249-88b4-4dfd-95cc-541cc688e1a3
-tags:
-  nist:
-    RS.RP
-description: |
-  Validate indicator existance, reputation, detonation and determine if Known APT, Commodity, Suspicous or Not Malicious?
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-    - whois ip
-    - whois domain
-    - geolocate ip
-    - ip reputation
-    - domain reputation
-    - url reputation
-    - ip intelligence
-    - domain intelligence
-    - url intelligence
-    - hunt ip
-    - hunt domain
-    - hunt url
-    - detonate url
-  playbooks:
-    - scm:
-      playook: ""
-references:
-    - 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  1. If Splunk present, run Splunk Query on each indictor and prevelence in the environment. Summarize # of times indicator seen in the last 24 hrs, 7 days, 1 month, six months increments. Return the hosts that have used this indictor. (make an artifact for each host?)
-    a. search -> ```| stats ...```
-  1. Gather reputational, intelligence and general information regarding indicator into a note (a note for each indicator)
-  1. Perform any additional research regarding indicators and understanding what normal behaviour is or should be by using using search engines, knowledge bases etc.
-  1. Make a determination of indicator, Known APT, Commodity, Suspicous or Not Malicious and whether to tag to block indicator
-    a. This should align to a severity change (Known APT = High, Commodity = Med, Suspicous = Low, Not Malicious = Info)
-    a. hange container and artifact severity and tag artifact & indicator(s) with blocked and determination tag
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - run query
+  - whois ip
+  - whois domain
+  - geolocate ip
+  - ip reputation
+  - domain reputation
+  - url reputation
+  - ip intelligence
+  - domain intelligence
+  - url intelligence
+  - hunt ip
+  - hunt domain
+  - hunt url
+  - detonate url
+  is_note_required: false
+  playbooks:
+  - playook: ''
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Validate indicator existance, reputation, detonation and determine if
+  Known APT, Commodity, Suspicous or Not Malicious?
+
+  '
+id: 710b1249-88b4-4dfd-95cc-541cc688e1a3
+name: Analyze network indicators and reputation
+references:
+- 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "1. If Splunk present, run Splunk Query on each indictor and prevelence\
+  \ in the environment. Summarize # of times indicator seen in the last 24 hrs, 7\
+  \ days, 1 month, six months increments. Return the hosts that have used this indictor.\
+  \ (make an artifact for each host?)\n  a. search -> ```| stats ...```\n1. Gather\
+  \ reputational, intelligence and general information regarding indicator into a\
+  \ note (a note for each indicator)\n1. Perform any additional research regarding\
+  \ indicators and understanding what normal behaviour is or should be by using using\
+  \ search engines, knowledge bases etc.\n1. Make a determination of indicator, Known\
+  \ APT, Commodity, Suspicous or Not Malicious and whether to tag to block indicator\n\
+  \  a. This should align to a severity change (Known APT = High, Commodity = Med,\
+  \ Suspicous = Low, Not Malicious = Info)\n  a. hange container and artifact severity\
+  \ and tag artifact & indicator(s) with blocked and determination tag\n"
diff --git a/response_tasks/playbooks/analyze_precursors_to_the_event.yml b/response_tasks/playbooks/analyze_precursors_to_the_event.yml
index e9471f98c2..6831054fb5 100644
--- a/response_tasks/playbooks/analyze_precursors_to_the_event.yml
+++ b/response_tasks/playbooks/analyze_precursors_to_the_event.yml
@@ -1,26 +1,41 @@
-name: Analyze precursors to the event
-id: ef9e7a25-73f0-4b63-b43b-2f4171518931
-tags:
-  nist:
-    RS.RP
-description: |
-  Review precursor and indicator data and try to prove the data observed is normal activity. This analysis is provided by reviewing additional logs and sources to include ids's, siem, network logs, host and host application event logs and vulnerabiltiy information. This is not an exhaustive list, but a summary of the data available.  Data available should be aligned the type of event and resources available to the customer.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. Depending the attack vector, use your siem or logging collector to find logs regarding the host, application and network connections surrounding the event detected.
-  2. Identify evidence information that proves the incident occurred as detected or corraborates the event(s).
-  3. Perform research regarding indicators and understanding what normal behaviour is or should be by using using search engines, knowledge bases etc.
-date: '2020-04-21'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions: null
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-04-21'
+description: 'Review precursor and indicator data and try to prove the data observed
+  is normal activity. This analysis is provided by reviewing additional logs and sources
+  to include ids''s, siem, network logs, host and host application event logs and
+  vulnerabiltiy information. This is not an exhaustive list, but a summary of the
+  data available.  Data available should be aligned the type of event and resources
+  available to the customer.
+
+  '
+id: ef9e7a25-73f0-4b63-b43b-2f4171518931
+name: Analyze precursors to the event
+references:
+- 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Depending the attack vector, use your siem or logging collector to find
+  logs regarding the host, application and network connections surrounding the event
+  detected.
+
+  2. Identify evidence information that proves the incident occurred as detected or
+  corraborates the event(s).
+
+  3. Perform research regarding indicators and understanding what normal behaviour
+  is or should be by using using search engines, knowledge bases etc.
+
+  '
diff --git a/response_tasks/playbooks/analyze_url_indicator_and_reputation.yml b/response_tasks/playbooks/analyze_url_indicator_and_reputation.yml
index f9ba6f8916..66015b94a6 100644
--- a/response_tasks/playbooks/analyze_url_indicator_and_reputation.yml
+++ b/response_tasks/playbooks/analyze_url_indicator_and_reputation.yml
@@ -1,38 +1,46 @@
-name: Analyze url indicator and reputation
-id: 65a23d95-7b5a-405c-b5bf-893983478d35
-tags:
-  nist:
-    RS.RP
-description: |
-  Validate indicator existance, reputation, detonation and determine if Known APT, Commodity, Suspicous or Not Malicious?
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - whois domain
-    - domain reputation
-    - url reputation
-    - domain intelligence
-    - url intelligence
-    - hunt domain
-    - hunt url
-    - detonate url
-  playbooks:
-    - scm:
-      playook: ""
-references:
-    - 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  This should be TLD domain and url combinations analysis.
-  1. If Splunk present, run Splunk Query on each indictor and prevelence in the environment. Summarize # of times indicator seen in the last 24 hrs, 7 days, 1 month, six months increments. Return the hosts that have used this indictor. (make an artifact for each host?)
-    a. search -> ```| stats ...```
-  1. Gather reputational, intelligence and general information regarding indicator into a note (a note for each indicator)
-  1. Perform any additional research regarding indicators and understanding what normal behaviour is or should be by using using search engines, knowledge bases etc.
-  1. Make a determination of indicator, Known APT, Commodity, Suspicous or Not Malicious and whether to tag to block indicator
-    a. This should align to a severity change (Known APT = High, Commodity = Med, Suspicous = Low, Not Malicious = Info)
-    a. Change container and artifact severity and tag artifact & indicator(s) with blocked and determination tag
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - whois domain
+  - domain reputation
+  - url reputation
+  - domain intelligence
+  - url intelligence
+  - hunt domain
+  - hunt url
+  - detonate url
+  is_note_required: false
+  playbooks:
+  - playook: ''
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Validate indicator existance, reputation, detonation and determine if
+  Known APT, Commodity, Suspicous or Not Malicious?
+
+  '
+id: 65a23d95-7b5a-405c-b5bf-893983478d35
+name: Analyze url indicator and reputation
+references:
+- 3.2.3 Sources of Precursors and Indicators - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "This should be TLD domain and url combinations analysis.\n1. If Splunk\
+  \ present, run Splunk Query on each indictor and prevelence in the environment.\
+  \ Summarize # of times indicator seen in the last 24 hrs, 7 days, 1 month, six months\
+  \ increments. Return the hosts that have used this indictor. (make an artifact for\
+  \ each host?)\n  a. search -> ```| stats ...```\n1. Gather reputational, intelligence\
+  \ and general information regarding indicator into a note (a note for each indicator)\n\
+  1. Perform any additional research regarding indicators and understanding what normal\
+  \ behaviour is or should be by using using search engines, knowledge bases etc.\n\
+  1. Make a determination of indicator, Known APT, Commodity, Suspicous or Not Malicious\
+  \ and whether to tag to block indicator\n  a. This should align to a severity change\
+  \ (Known APT = High, Commodity = Med, Suspicous = Low, Not Malicious = Info)\n \
+  \ a. Change container and artifact severity and tag artifact & indicator(s) with\
+  \ blocked and determination tag\n"
diff --git a/response_tasks/playbooks/conduct_training.yml b/response_tasks/playbooks/conduct_training.yml
index cf320c64be..9f662b994b 100644
--- a/response_tasks/playbooks/conduct_training.yml
+++ b/response_tasks/playbooks/conduct_training.yml
@@ -1,36 +1,50 @@
-
-name: Conduct training
-id: df493538-e598-463b-8835-a109022c2968
-tags:
-  nist:
-    RS.RP
-description: Take training courses to gain relevant knowledge - Sharpen the saw.
-automation:
-    is_note_required: false
-    role:
-    sla_type: minutes
-    sla:
-    action:
-    playbook: ""
-references:
-  - ""
-workflow: |
-  We do not rise to the level of our expectations. We fall to the level of our training.  - @atc_project
-  > “The more that you read, the more things you will know. The more that you learn, the more places you’ll go.” ― Dr. Seuss
-  We assume that you already have a strong technical background in fundamental disciplines — Networking, Operating Systems, and Programming.
-  Here are some relevant training courses that will help you in the Incident Response activities:
-  1. [Investigation Theory](https://chrissanders.org/training/investigationtheory/) by Chris Sanders. We recommend you to have it as a mandatory training for every member of your Incident Response team
-  1. [SANS Digital Forensics & Incident Response](https://digital-forensics.sans.org/training/courses) trainings
-    * SEC450: Blue Team Fundamentals: Security Operations and Analysis - https://www.sans.org/course/blue-team-fundamentals-security-operations-analysis. We recommend you to have it as a mandatory training for every member of your Incident Response team
-    * SEC504: Hacker Tools, Techniques, Exploits, and Incident Handling - https://www.sans.org/course/hacker-techniques-exploits-incident-handling We recommend you to have it as a mandatory training for every member of your Incident Response team
-    * FOR500: Windows Forensic Analysis - https://www.sans.org/course/windows-forensic-analysis
-    * FOR508: Advanced Incident Response, Threat Hunting, and Digital Forensics - https://www.sans.org/course/advanced-incident-response-threat-hunting-training
-    * SEC503: Intrusion Detection In-Depth - https://www.sans.org/course/intrusion-detection-in-depth
-    * FOR572: Advanced Network Forensics: Threat Hunting, Analysis, and Incident Response - https://www.sans.org/course/advanced-network-forensics-threat-hunting-incident-response
-    * SEC560: Network Penetration Testing and Ethical Hacking - https://www.sans.org/course/network-penetration-testing-ethical-hacking OR Offensive Security trainings mentioned below
-    * SEC599: Defeating Advanced Adversaries - Purple Team Tactics & Kill Chain Defenses - https://www.sans.org/course/defeating-advanced-adversaries-kill-chain-defenses
-  1. [Offensive Security](https://www.offensive-security.com/courses-and-certifications/) trainings. We recommend [PWK](https://www.offensive-security.com/pwk-oscp/) by Offensive Security trainings are in the list because to fight a threat, you need to understand their motivation, tactics, and techniques.
-  The training above is a recommendation and certainly the size of the organization will depend on the amount of training possible.
-date: '2020-07-17'
-version: 1
 author: ButterCup
+automation:
+  action: null
+  is_note_required: false
+  playbook: ''
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-17'
+description: Take training courses to gain relevant knowledge - Sharpen the saw.
+id: df493538-e598-463b-8835-a109022c2968
+name: Conduct training
+references:
+- ''
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "We do not rise to the level of our expectations. We fall to the level of\
+  \ our training.  - @atc_project\n> \u201CThe more that you read, the more things\
+  \ you will know. The more that you learn, the more places you\u2019ll go.\u201D\
+  \ \u2015 Dr. Seuss\nWe assume that you already have a strong technical background\
+  \ in fundamental disciplines \u2014 Networking, Operating Systems, and Programming.\n\
+  Here are some relevant training courses that will help you in the Incident Response\
+  \ activities:\n1. [Investigation Theory](https://chrissanders.org/training/investigationtheory/)\
+  \ by Chris Sanders. We recommend you to have it as a mandatory training for every\
+  \ member of your Incident Response team\n1. [SANS Digital Forensics & Incident Response](https://digital-forensics.sans.org/training/courses)\
+  \ trainings\n  * SEC450: Blue Team Fundamentals: Security Operations and Analysis\
+  \ - https://www.sans.org/course/blue-team-fundamentals-security-operations-analysis.\
+  \ We recommend you to have it as a mandatory training for every member of your Incident\
+  \ Response team\n  * SEC504: Hacker Tools, Techniques, Exploits, and Incident Handling\
+  \ - https://www.sans.org/course/hacker-techniques-exploits-incident-handling We\
+  \ recommend you to have it as a mandatory training for every member of your Incident\
+  \ Response team\n  * FOR500: Windows Forensic Analysis - https://www.sans.org/course/windows-forensic-analysis\n\
+  \  * FOR508: Advanced Incident Response, Threat Hunting, and Digital Forensics -\
+  \ https://www.sans.org/course/advanced-incident-response-threat-hunting-training\n\
+  \  * SEC503: Intrusion Detection In-Depth - https://www.sans.org/course/intrusion-detection-in-depth\n\
+  \  * FOR572: Advanced Network Forensics: Threat Hunting, Analysis, and Incident\
+  \ Response - https://www.sans.org/course/advanced-network-forensics-threat-hunting-incident-response\n\
+  \  * SEC560: Network Penetration Testing and Ethical Hacking - https://www.sans.org/course/network-penetration-testing-ethical-hacking\
+  \ OR Offensive Security trainings mentioned below\n  * SEC599: Defeating Advanced\
+  \ Adversaries - Purple Team Tactics & Kill Chain Defenses - https://www.sans.org/course/defeating-advanced-adversaries-kill-chain-defenses\n\
+  1. [Offensive Security](https://www.offensive-security.com/courses-and-certifications/)\
+  \ trainings. We recommend [PWK](https://www.offensive-security.com/pwk-oscp/) by\
+  \ Offensive Security trainings are in the list because to fight a threat, you need\
+  \ to understand their motivation, tactics, and techniques.\nThe training above is\
+  \ a recommendation and certainly the size of the organization will depend on the\
+  \ amount of training possible.\n"
diff --git a/response_tasks/playbooks/confirm_incident.yml b/response_tasks/playbooks/confirm_incident.yml
index 3f578f7229..e6229ae4c6 100644
--- a/response_tasks/playbooks/confirm_incident.yml
+++ b/response_tasks/playbooks/confirm_incident.yml
@@ -1,27 +1,38 @@
-name: Confirm incident
+author: ButterCup
+automation:
+  actions:
+  - update event
+  is_note_required: false
+  playbooks:
+  - playook: Update TLP, attack vector, disposition and category of event
+    scm: local
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Upon analysis, determination of incident status is either confirmation,
+  suspicious, false positive or authorized exception. Update the event metadata and
+  process event as determined.
+
+  '
 id: 994298f0-75fc-4c14-b044-9b81944d3a03
+name: Confirm incident
+references:
+- 3.2.4 Incident Analysis - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+- 3.2.5 Incident Documentation - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
 tags:
   nist: RS.RP
-description: |
-  Upon analysis, determination of incident status is either confirmation, suspicious, false positive or authorized exception. Update the event metadata and process event as determined.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - update event
-  playbooks:
-    - scm: local
-      playook: Update TLP, attack vector, disposition and category of event
-references:
-  - 3.2.4 Incident Analysis - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-  - 3.2.5 Incident Documentation - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. Determine if confirmed, suspicious, a false positive, or authorized exception event
-  2. Update TLP, attack vector, disposition and category of event
-  3. Proceed on the next task of prioritizing incident or lessons learned or escalate for additional investigation
-date: '2020-07-30'
+  product:
+  - Splunk Phantom
+type: response
 version: 1
-author: ButterCup
+workflow: '1. Determine if confirmed, suspicious, a false positive, or authorized
+  exception event
+
+  2. Update TLP, attack vector, disposition and category of event
+
+  3. Proceed on the next task of prioritizing incident or lessons learned or escalate
+  for additional investigation
+
+  '
diff --git a/response_tasks/playbooks/contain_incident.yml b/response_tasks/playbooks/contain_incident.yml
index 9806a94f02..57d2e605c5 100644
--- a/response_tasks/playbooks/contain_incident.yml
+++ b/response_tasks/playbooks/contain_incident.yml
@@ -1,31 +1,38 @@
-name: Contain Incident
-id: 735335a5-7ac0-4bdf-b1d3-6f4a6767d02f
-tags:
-  nist:
-    RS.RP
-description: |
-  Use atomic indicators to block and contain malicious activity. Use host and network protection tools to block, pause, drop, or quarantine affected machines.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - block ip
-    - block domain
-    - block url
-    - block process
-    - terminate process
-    - quarantine host
-    - quarantine device
-  playbooks:
-    - scm: community
-      playook: quarantine device and block external network access
-references:
-  - 3.3.1 Choosing a Containment Strategy - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. Determine the appropriate containment technique either with soft block (localized blocking of a specific indictors) or hard block (regionalized blocking, quarantine whole hosts, net blocks, etc) techniques
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - block ip
+  - block domain
+  - block url
+  - block process
+  - terminate process
+  - quarantine host
+  - quarantine device
+  is_note_required: false
+  playbooks:
+  - playook: quarantine device and block external network access
+    scm: community
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Use atomic indicators to block and contain malicious activity. Use host
+  and network protection tools to block, pause, drop, or quarantine affected machines.
+
+  '
+id: 735335a5-7ac0-4bdf-b1d3-6f4a6767d02f
+name: Contain Incident
+references:
+- 3.3.1 Choosing a Containment Strategy - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Determine the appropriate containment technique either with soft block
+  (localized blocking of a specific indictors) or hard block (regionalized blocking,
+  quarantine whole hosts, net blocks, etc) techniques
+
+  '
diff --git a/response_tasks/playbooks/create_follow-up_report.yml b/response_tasks/playbooks/create_follow-up_report.yml
index 60a501708f..0fdd40eda1 100644
--- a/response_tasks/playbooks/create_follow-up_report.yml
+++ b/response_tasks/playbooks/create_follow-up_report.yml
@@ -1,35 +1,43 @@
-name: Create follow-up report
-id: 69d25415-408f-462a-899f-9bc8eef8c299
-tags:
-  nist:
-    RS.RP
-description: |
-  Build an after actions report that discusses about what happened and a timeline. Provide what went well and what improvements can be made, information timeliness, steps or actions that might have delayed recovery, what information was shared or could have been shared, any corrective actions that would have prevent the incident, identify precursors and indicators should be watched for in the future or tools that could be used to mitigate future incidents. Conduct an Incident Review Meeting.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - update ticket
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.4.1 Lessons Learned - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  1. Create a follow up report
-    a. Exactly what happened, and at what times?
-    b. How well did staff and management perform in dealing with the incident?
-    c. Were the documented procedures followed? Were they adequate?
-    d. What information was needed sooner?
-    e. Were any steps or actions taken that might have inhibited the recovery?
-    f. What would the staff and management do differently the next time a similar incident occurs?
-    g. How could information sharing with other organizations have been improved?
-    h. What corrective actions can prevent similar incidents in the future?
-    i. What precursors or indicators should be watched for in the future to detect similar incidents?
-    j. What additional tools or resources are needed to detect, analyze, and mitigate future incidents?
-  2. Schedule and conduct a Incident Review Meeting with necessary leadership and incident response team
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - update ticket
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Build an after actions report that discusses about what happened and
+  a timeline. Provide what went well and what improvements can be made, information
+  timeliness, steps or actions that might have delayed recovery, what information
+  was shared or could have been shared, any corrective actions that would have prevent
+  the incident, identify precursors and indicators should be watched for in the future
+  or tools that could be used to mitigate future incidents. Conduct an Incident Review
+  Meeting.
+
+  '
+id: 69d25415-408f-462a-899f-9bc8eef8c299
+name: Create follow-up report
+references:
+- 3.4.1 Lessons Learned - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "1. Create a follow up report\n  a. Exactly what happened, and at what times?\n\
+  \  b. How well did staff and management perform in dealing with the incident?\n\
+  \  c. Were the documented procedures followed? Were they adequate?\n  d. What information\
+  \ was needed sooner?\n  e. Were any steps or actions taken that might have inhibited\
+  \ the recovery?\n  f. What would the staff and management do differently the next\
+  \ time a similar incident occurs?\n  g. How could information sharing with other\
+  \ organizations have been improved?\n  h. What corrective actions can prevent similar\
+  \ incidents in the future?\n  i. What precursors or indicators should be watched\
+  \ for in the future to detect similar incidents?\n  j. What additional tools or\
+  \ resources are needed to detect, analyze, and mitigate future incidents?\n2. Schedule\
+  \ and conduct a Incident Review Meeting with necessary leadership and incident response\
+  \ team\n"
diff --git a/response_tasks/playbooks/determine_if_an_incident_has_occurred.yml b/response_tasks/playbooks/determine_if_an_incident_has_occurred.yml
index 0abdc1d20c..697367d838 100644
--- a/response_tasks/playbooks/determine_if_an_incident_has_occurred.yml
+++ b/response_tasks/playbooks/determine_if_an_incident_has_occurred.yml
@@ -1,25 +1,35 @@
-name: Determine if an incident has occurred
-id: 92ba5c50-717d-44e7-bb88-72bf6907ec83
-tags:
-  nist:
-    RS.RP
-description: |
-  Review precursor and indicator data and try to prove the data observed is normal activity. Knowledge of false positives detractors will support this assessment. The object of this step is to remove confirmation bias and validate the detection as a true positive and anomalous behavior.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - set status
-  playbooks:
-    - scm: local
-      playook: Accept event and assign owner
-references:
-  - 3.2.2 Signs of an Incident - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  Incident handlers are responsible for analyzing ambiguous, contradictory, and incomplete symptoms to determine what has happened.
-  The process here is to remove bias and support investigation based on indicators that validate compromise or violdation for continued investigation
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - set status
+  is_note_required: false
+  playbooks:
+  - playook: Accept event and assign owner
+    scm: local
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Review precursor and indicator data and try to prove the data observed
+  is normal activity. Knowledge of false positives detractors will support this assessment.
+  The object of this step is to remove confirmation bias and validate the detection
+  as a true positive and anomalous behavior.
+
+  '
+id: 92ba5c50-717d-44e7-bb88-72bf6907ec83
+name: Determine if an incident has occurred
+references:
+- 3.2.2 Signs of an Incident - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: 'Incident handlers are responsible for analyzing ambiguous, contradictory,
+  and incomplete symptoms to determine what has happened.
+
+  The process here is to remove bias and support investigation based on indicators
+  that validate compromise or violdation for continued investigation
+
+  '
diff --git a/response_tasks/playbooks/determine_incident_prioritization.yml b/response_tasks/playbooks/determine_incident_prioritization.yml
index 58ff118934..03a3f6361e 100644
--- a/response_tasks/playbooks/determine_incident_prioritization.yml
+++ b/response_tasks/playbooks/determine_incident_prioritization.yml
@@ -1,40 +1,57 @@
-name: Determine Incident Prioritization
-id: 91f1c863-c080-4b3c-921c-e1ca1c0e7ae1
-tags:
-  nist:
-    RS.RP
-description: |
-  Determine Functional Impact of the Incident. Incidents targeting IT systems typically impact the business functionality that those systems provide, resulting in some type of negative impact to the users of those systems.
-  Determine Information Impact of the Incident. Incidents may affect the confidentiality, integrity, and availability of the organizations information.
-  Determine Recoverability from the Incident. The size of the incident and the type of resources it affects will determine the amount of time and resources that must be spent on recovering from that incident.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - update container
-  playbooks:
-    - scm: local
-      playook: Determine impact and effort
-references:
-  - 3.2.6 Incident Prioritization - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  1. Determine Functional Impact of the Incident. By determining,
-      a. None   | No effect to the organization’s ability to provide all services to all users
-      b. Low    | Minimal effect; the organization can still provide all critical services to all users but has lost efficiency
-      c. Medium | Organization has lost the ability to provide a critical service to a subset of system users
-      d. High   | Organization is no longer able to provide some critical services to any users
-  2. Determine Information Impact of the Incident. Incidents may affect the confidentiality, integrity, and availability of the organization’s information.
-      a. None               | No information was exfiltrated, changed, deleted, or otherwise compromised
-      b. Privacy Breach     | Sensitive personally identifiable information (PII) of taxpayers, employees, beneficiaries, etc. was accessed or exfiltrated
-      c. Proprietary Breach | Unclassified proprietary information, such as protected critical infrastructure information (PCII), was accessed or exfiltrated
-      d. Integrity Loss     | Sensitive or proprietary information was changed or deleted
-  3. Determine Recoverability from the Incident. The size of the incident and the type of resources it affects will determine the amount of time and resources that must be spent on recovering from that incident.
-      a. Regular          | Time to recovery is predictable with existing resources
-      b. Supplemented     | Time to recovery is predictable with additional resources
-      c. Extended         | Time to recovery is unpredictable; additional resources and outside help are needed
-      d. Not Recoverable  | Recovery from the incident is not possible (e.g., sensitive data exfiltrated and posted publicly); launch investigation
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - update container
+  is_note_required: false
+  playbooks:
+  - playook: Determine impact and effort
+    scm: local
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Determine Functional Impact of the Incident. Incidents targeting IT
+  systems typically impact the business functionality that those systems provide,
+  resulting in some type of negative impact to the users of those systems.
+
+  Determine Information Impact of the Incident. Incidents may affect the confidentiality,
+  integrity, and availability of the organizations information.
+
+  Determine Recoverability from the Incident. The size of the incident and the type
+  of resources it affects will determine the amount of time and resources that must
+  be spent on recovering from that incident.
+
+  '
+id: 91f1c863-c080-4b3c-921c-e1ca1c0e7ae1
+name: Determine Incident Prioritization
+references:
+- 3.2.6 Incident Prioritization - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "1. Determine Functional Impact of the Incident. By determining,\n    a.\
+  \ None   | No effect to the organization\u2019s ability to provide all services\
+  \ to all users\n    b. Low    | Minimal effect; the organization can still provide\
+  \ all critical services to all users but has lost efficiency\n    c. Medium | Organization\
+  \ has lost the ability to provide a critical service to a subset of system users\n\
+  \    d. High   | Organization is no longer able to provide some critical services\
+  \ to any users\n2. Determine Information Impact of the Incident. Incidents may affect\
+  \ the confidentiality, integrity, and availability of the organization\u2019s information.\n\
+  \    a. None               | No information was exfiltrated, changed, deleted, or\
+  \ otherwise compromised\n    b. Privacy Breach     | Sensitive personally identifiable\
+  \ information (PII) of taxpayers, employees, beneficiaries, etc. was accessed or\
+  \ exfiltrated\n    c. Proprietary Breach | Unclassified proprietary information,\
+  \ such as protected critical infrastructure information (PCII), was accessed or\
+  \ exfiltrated\n    d. Integrity Loss     | Sensitive or proprietary information\
+  \ was changed or deleted\n3. Determine Recoverability from the Incident. The size\
+  \ of the incident and the type of resources it affects will determine the amount\
+  \ of time and resources that must be spent on recovering from that incident.\n \
+  \   a. Regular          | Time to recovery is predictable with existing resources\n\
+  \    b. Supplemented     | Time to recovery is predictable with additional resources\n\
+  \    c. Extended         | Time to recovery is unpredictable; additional resources\
+  \ and outside help are needed\n    d. Not Recoverable  | Recovery from the incident\
+  \ is not possible (e.g., sensitive data exfiltrated and posted publicly); launch\
+  \ investigation\n"
diff --git a/response_tasks/playbooks/document_and_notify_of_incident.yml b/response_tasks/playbooks/document_and_notify_of_incident.yml
index 6058e1d971..7383cc660d 100644
--- a/response_tasks/playbooks/document_and_notify_of_incident.yml
+++ b/response_tasks/playbooks/document_and_notify_of_incident.yml
@@ -1,39 +1,59 @@
-name: Document and notify of incident
-id: 3890e0b3-bb46-4b9b-8134-184dbe644a8a
-tags:
-  nist:
-    RS.RP
-description: |
-  At this point, incident responder should acquire, preserve, secure, and document all evidence to the incident. This process will be continual through the IR process. The incident responder should perform notification pursuant to the organizational incident response policy and outlined procedures.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - create ticket
-    - create incident
-    - create case
-    - send email
-  playbooks:
-      - scm: local
-        playook: Create case or merge with known case
-references:
-  - 3.2.5 Document Incident - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-  - 3.2.7 Incident Notification - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  The reasond for reordering of the documentation of the incident was to align documentation and incident notification into the same process flow.
-  1. The current status of the incident (new, in progress, forwarded for investigation, resolved, etc.)
-  2. A summary of the incident
-  3. Indicators related to the incident
-  4. Other incidents related to this incident
-  5. Actions taken by all incident handlers on this incident
-  6. Chain of custody, if applicable
-  7. Impact assessments related to the incident
-  8. Contact information for other involved parties (e.g., system owners, system administrators)
-  9. list of evidence gathered during the incident investigation
-  10. Comments from incident handlers
-  11. Next steps to be taken (e.g., rebuild the host, upgrade an application).
-date: '2020-04-21'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - create ticket
+  - create incident
+  - create case
+  - send email
+  is_note_required: false
+  playbooks:
+  - playook: Create case or merge with known case
+    scm: local
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-04-21'
+description: 'At this point, incident responder should acquire, preserve, secure,
+  and document all evidence to the incident. This process will be continual through
+  the IR process. The incident responder should perform notification pursuant to the
+  organizational incident response policy and outlined procedures.
+
+  '
+id: 3890e0b3-bb46-4b9b-8134-184dbe644a8a
+name: Document and notify of incident
+references:
+- 3.2.5 Document Incident - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+- 3.2.7 Incident Notification - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: 'The reasond for reordering of the documentation of the incident was to
+  align documentation and incident notification into the same process flow.
+
+  1. The current status of the incident (new, in progress, forwarded for investigation,
+  resolved, etc.)
+
+  2. A summary of the incident
+
+  3. Indicators related to the incident
+
+  4. Other incidents related to this incident
+
+  5. Actions taken by all incident handlers on this incident
+
+  6. Chain of custody, if applicable
+
+  7. Impact assessments related to the incident
+
+  8. Contact information for other involved parties (e.g., system owners, system administrators)
+
+  9. list of evidence gathered during the incident investigation
+
+  10. Comments from incident handlers
+
+  11. Next steps to be taken (e.g., rebuild the host, upgrade an application).
+
+  '
diff --git a/response_tasks/playbooks/identify_additional_affected_hosts.yml b/response_tasks/playbooks/identify_additional_affected_hosts.yml
index 6fb5958c73..b39873f017 100644
--- a/response_tasks/playbooks/identify_additional_affected_hosts.yml
+++ b/response_tasks/playbooks/identify_additional_affected_hosts.yml
@@ -1,31 +1,50 @@
-name: Identify additional affected hosts
-id: 3d481dd1-4f30-4262-a846-78af6bdce11c
-tags:
-  nist:
-    RS.RP
-description: |
-  Use indictors developed from the detection and analysis to find additional hosts or potentially infected hosts.  If found perform additional analysis to determine root cause analysis and any additional indictors. If this is mass infection event, immediate containment must be delayed until full understanding of the infection is achieved.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-    - merge event
-  playbooks:
-    - scm: community
-      playook: Merge event with case
-references:
-  - 3.3.1 Choosing a Containment Strategy - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-  - 3.3.2 Evidence Gathering and Handling - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-  - NIST SP 800-86, Guide to Integrating Forensic Techniques into Incident Response, for additional information on preserving evidence
-workflow: |
-  1. For Host infections, use persistence mechanism, dropped filenames, paths, hashs, accessed or collected files, links, network communications, network ports and protocols, netwrok flow data points, etc
-  2. For phishing infections, use email addresses, domains, urls, file hashes, x-origin-ip address, subject, etc
-  3. For Command and Control, use host communication, ports and protocols, application, url patterns, dns queries, ping data, etc
-  4. Escalate to incident management team, if a massive incident to support incident response.
-  5. Ensure all known event information and root cause of the event is known before proceeding with containment
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - run query
+  - merge event
+  is_note_required: false
+  playbooks:
+  - playook: Merge event with case
+    scm: community
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Use indictors developed from the detection and analysis to find additional
+  hosts or potentially infected hosts.  If found perform additional analysis to determine
+  root cause analysis and any additional indictors. If this is mass infection event,
+  immediate containment must be delayed until full understanding of the infection
+  is achieved.
+
+  '
+id: 3d481dd1-4f30-4262-a846-78af6bdce11c
+name: Identify additional affected hosts
+references:
+- 3.3.1 Choosing a Containment Strategy - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+- 3.3.2 Evidence Gathering and Handling - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+- NIST SP 800-86, Guide to Integrating Forensic Techniques into Incident Response,
+  for additional information on preserving evidence
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. For Host infections, use persistence mechanism, dropped filenames, paths,
+  hashs, accessed or collected files, links, network communications, network ports
+  and protocols, netwrok flow data points, etc
+
+  2. For phishing infections, use email addresses, domains, urls, file hashes, x-origin-ip
+  address, subject, etc
+
+  3. For Command and Control, use host communication, ports and protocols, application,
+  url patterns, dns queries, ping data, etc
+
+  4. Escalate to incident management team, if a massive incident to support incident
+  response.
+
+  5. Ensure all known event information and root cause of the event is known before
+  proceeding with containment
+
+  '
diff --git a/response_tasks/playbooks/identify_vunlerabilities.yml b/response_tasks/playbooks/identify_vunlerabilities.yml
index 7818395e30..c895361fb4 100644
--- a/response_tasks/playbooks/identify_vunlerabilities.yml
+++ b/response_tasks/playbooks/identify_vunlerabilities.yml
@@ -1,28 +1,38 @@
-name: Identify vunlerabilities
-id: f28177ae-78de-43c9-8692-e972e8a0aa62
-tags:
-  nist:
-    RS.RP
-description: |
-  Scan host(s) for vulnerabilities. Reverse engineer malware thru static and dynamic means to determine any zero day vulnerabilities.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - scan host
-    - scan hosts
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.3.3 Identifying the Attacking Hosts - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-    - NIST SP 800-86, Guide to Integrating Forensic Techniques into Incident Response, for additional information on preserving evidence
-workflow: |
-  1. Scan host(s) with infection and determine any vulnerabilities that can remediate infection. (e.g. finding a SMB MS17-010 - Windows SMB Remote Code Execution Vulnerability)
-  2. Identify any common vulnerabilities among the hosts infected
-  3. Reverse engineer malware for any zero day vulnerabilities.
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - scan host
+  - scan hosts
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Scan host(s) for vulnerabilities. Reverse engineer malware thru static
+  and dynamic means to determine any zero day vulnerabilities.
+
+  '
+id: f28177ae-78de-43c9-8692-e972e8a0aa62
+name: Identify vunlerabilities
+references:
+- 3.3.3 Identifying the Attacking Hosts - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+  - NIST SP 800-86, Guide to Integrating Forensic Techniques into Incident Response,
+  for additional information on preserving evidence
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Scan host(s) with infection and determine any vulnerabilities that can
+  remediate infection. (e.g. finding a SMB MS17-010 - Windows SMB Remote Code Execution
+  Vulnerability)
+
+  2. Identify any common vulnerabilities among the hosts infected
+
+  3. Reverse engineer malware for any zero day vulnerabilities.
+
+  '
diff --git a/response_tasks/playbooks/implement_additional_monitoring.yml b/response_tasks/playbooks/implement_additional_monitoring.yml
index 3a61431028..38eea56d8d 100644
--- a/response_tasks/playbooks/implement_additional_monitoring.yml
+++ b/response_tasks/playbooks/implement_additional_monitoring.yml
@@ -1,26 +1,35 @@
-name: Implement additional monitoring
-id: edb7867c-2e81-4356-a422-92781f4fa34c
-tags:
-  nist:
-    RS.RP
-description: |
-  Implement additional monitoring that reviews not only host/network containment success. Monitor network blocks for additional hosts that might not have been identified. Reassess containment as needed depending on any new information. If this is a mass infection, it's advised that a 24 no change process be implemented.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-  playbooks:
-    - scm:
-      playook: ""
-references:
-  - 3.2.4 Incident Analysis - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. Create additional monitoring for network and host detection for abnormal activity to ensure containment is effective.
-  2. Re-investigate new hosts found but not on the containment list
-date: '2020-04-21'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - run query
+  is_note_required: false
+  playbooks:
+  - playook: ''
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-04-21'
+description: 'Implement additional monitoring that reviews not only host/network containment
+  success. Monitor network blocks for additional hosts that might not have been identified.
+  Reassess containment as needed depending on any new information. If this is a mass
+  infection, it''s advised that a 24 no change process be implemented.
+
+  '
+id: edb7867c-2e81-4356-a422-92781f4fa34c
+name: Implement additional monitoring
+references:
+- 3.2.4 Incident Analysis - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Create additional monitoring for network and host detection for abnormal
+  activity to ensure containment is effective.
+
+  2. Re-investigate new hosts found but not on the containment list
+
+  '
diff --git a/response_tasks/playbooks/implement_recovery_monitoring.yml b/response_tasks/playbooks/implement_recovery_monitoring.yml
index 453f458dec..dbe078be18 100644
--- a/response_tasks/playbooks/implement_recovery_monitoring.yml
+++ b/response_tasks/playbooks/implement_recovery_monitoring.yml
@@ -1,33 +1,45 @@
-name: Implement recovery monitoring
-id: ecf89e9b-106a-46d1-b236-a2716f71d7ae
-tags:
-  nist:
-    RS.RP
-description: |
-  Newly recovered systems should have additional monitoring for any anomalies or newly created incidents.  Adding single host to a previously infected list for a specified period will allow the incident responder to quickly assess a re-infection or subsequent new infection. At this stage in recovery, you should be unquarantining devices and removing any host blocks.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-    - unblock ip
-    - unblock domain
-    - unblock url
-    - unblock hash
-    - unblock process
-    - unquarantine device
-    - unquarantine host
-    - remove tag
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  1. Monitor restored systems for any new anomalies and ensure that operations have been returned to normal
-  2. Begin to remove emergency blocks to permanent block and remediation alerts for infected systems returning the network (e.g. laptops that were unreachable during the incident)
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - run query
+  - unblock ip
+  - unblock domain
+  - unblock url
+  - unblock hash
+  - unblock process
+  - unquarantine device
+  - unquarantine host
+  - remove tag
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Newly recovered systems should have additional monitoring for any anomalies
+  or newly created incidents.  Adding single host to a previously infected list for
+  a specified period will allow the incident responder to quickly assess a re-infection
+  or subsequent new infection. At this stage in recovery, you should be unquarantining
+  devices and removing any host blocks.
+
+  '
+id: ecf89e9b-106a-46d1-b236-a2716f71d7ae
+name: Implement recovery monitoring
+references:
+- 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Monitor restored systems for any new anomalies and ensure that operations
+  have been returned to normal
+
+  2. Begin to remove emergency blocks to permanent block and remediation alerts for
+  infected systems returning the network (e.g. laptops that were unreachable during
+  the incident)
+
+  '
diff --git a/response_tasks/playbooks/make_personnel_report_suspicious_activity.yml b/response_tasks/playbooks/make_personnel_report_suspicious_activity.yml
index f9c8bbac91..5b02738b99 100644
--- a/response_tasks/playbooks/make_personnel_report_suspicious_activity.yml
+++ b/response_tasks/playbooks/make_personnel_report_suspicious_activity.yml
@@ -1,22 +1,28 @@
-name: Make personnel report suspicious activity
-id: f83abcae-3734-45ff-99ef-b17eb937c057
-tags:
-  nist:
-    RS.RP
-description: |
-  c
-automation:
-  is_note_required: false
-  role:
-  sla_type: minutes
-  sla:
-  action:
-  playbook:
-references:
-  - Organizational Acceptable Use Policy
-workflow: |
-  Develop a simplified, company wide-known way to contact IR team in case of suspicious activity on the user system.
-  Make sure that the personnel is aware of it, can and will use it.
-date: '2020-04-21'
-version: 1
 author: ButterCup
+automation:
+  action: null
+  is_note_required: false
+  playbook: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-04-21'
+description: 'c
+
+  '
+id: f83abcae-3734-45ff-99ef-b17eb937c057
+name: Make personnel report suspicious activity
+references:
+- Organizational Acceptable Use Policy
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: 'Develop a simplified, company wide-known way to contact IR team in case
+  of suspicious activity on the user system.
+
+  Make sure that the personnel is aware of it, can and will use it.
+
+  '
diff --git a/response_tasks/playbooks/malware_hunt_and_contain.yml b/response_tasks/playbooks/malware_hunt_and_contain.yml
index 4a287f788e..954d9eb8c8 100644
--- a/response_tasks/playbooks/malware_hunt_and_contain.yml
+++ b/response_tasks/playbooks/malware_hunt_and_contain.yml
@@ -1,44 +1,46 @@
-name: Malware Hunt and Contain
-id: 1d7b437a-5114-4b94-a585-04c3362ba08f
-tags:
-  nist:
-    RS.RP
-description: Uses any presented filehash artifact sent to phantom and conducts a reputation check, hunts for additional systems, blocks file hash with <=10 positive detections and creates a ticket for follow up.  Any hashes found with >10 positive hits, automatically blockes the hashes, disables user accounts, logs off the user, shuts down the system and finally creates a urgent ticket.
 author: Patrick Bareiss, Splunk
 automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
   actions:
-    - file reputation
-    - hunt file
-    - get file
-    - block hash
-    - disable user
-    - logoff user
-    - shutdown system
-    - create ticket
+  - file reputation
+  - hunt file
+  - get file
+  - block hash
+  - disable user
+  - logoff user
+  - shutdown system
+  - create ticket
+  is_note_required: false
   playbooks:
-    - scm: community
-      playook: malware_hunt_and_contain
-references:
-  - https://github.com/phantomcyber/playbooks/blob/4.9/malware_hunt_and_contain.json
-  - https://github.com/phantomcyber/playbooks/blob/4.9/malware_hunt_and_contain.py
-  - https://github.com/phantomcyber/playbooks/blob/4.9/malware_hunt_and_contain.png
-workflow: |
-  1. Gets file reputation for every hash presented and filters hashes with >10 OR <=10 and >5 for positive hits
-  2. Get the files that match and hunt the file hashes and return the system(s)/user(s) that have these files present
-  3. If >10 then block hash, disable users, logoff user, shutdown system and create a ticket
-  4. IF <=10 but >5 block hash, and create a ticket
-  Ticket template:
-    Virus Detected on # devices
-    Hashes submitted with detections: (list hashes)
-    File was found on # of devices (list devices)
-    This impacts at least # users: (list users)
-    # of hashes were submitted for blocking: (list hashes)
-    # of users were forced to logoff: (list users)
-    # of user accounts were disabled: (list users)
-    # of systems were shutdown: (list systems)
-version: 2
+  - playook: malware_hunt_and_contain
+    scm: community
+  role: null
+  sla: null
+  sla_type: minutes
 date: '2020-08-05'
+description: Uses any presented filehash artifact sent to phantom and conducts a reputation
+  check, hunts for additional systems, blocks file hash with <=10 positive detections
+  and creates a ticket for follow up.  Any hashes found with >10 positive hits, automatically
+  blockes the hashes, disables user accounts, logs off the user, shuts down the system
+  and finally creates a urgent ticket.
+id: 1d7b437a-5114-4b94-a585-04c3362ba08f
+name: Malware Hunt and Contain
+references:
+- https://github.com/phantomcyber/playbooks/blob/4.9/malware_hunt_and_contain.json
+- https://github.com/phantomcyber/playbooks/blob/4.9/malware_hunt_and_contain.py
+- https://github.com/phantomcyber/playbooks/blob/4.9/malware_hunt_and_contain.png
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 2
+workflow: "1. Gets file reputation for every hash presented and filters hashes with\
+  \ >10 OR <=10 and >5 for positive hits\n2. Get the files that match and hunt the\
+  \ file hashes and return the system(s)/user(s) that have these files present\n3.\
+  \ If >10 then block hash, disable users, logoff user, shutdown system and create\
+  \ a ticket\n4. IF <=10 but >5 block hash, and create a ticket\nTicket template:\n\
+  \  Virus Detected on # devices\n  Hashes submitted with detections: (list hashes)\n\
+  \  File was found on # of devices (list devices)\n  This impacts at least # users:\
+  \ (list users)\n  # of hashes were submitted for blocking: (list hashes)\n  # of\
+  \ users were forced to logoff: (list users)\n  # of user accounts were disabled:\
+  \ (list users)\n  # of systems were shutdown: (list systems)\n"
diff --git a/response_tasks/playbooks/mitigate_or_remediate_any_vulnerabilities.yml b/response_tasks/playbooks/mitigate_or_remediate_any_vulnerabilities.yml
index 222be388f5..99987c19a5 100644
--- a/response_tasks/playbooks/mitigate_or_remediate_any_vulnerabilities.yml
+++ b/response_tasks/playbooks/mitigate_or_remediate_any_vulnerabilities.yml
@@ -1,33 +1,55 @@
-name: Mitigate or remediate any vulnerabilities
-id: 70362de1-bfef-4a0f-893f-3e0d605ed9b7
-tags:
-  nist:
-    RS.RP
-description: |
-  Apply mitigations or remediate any known affecting vulnerabilities that linked to the incidents.  (e.g. SMB MS17-010 - Windows SMB Remote Code Execution Vulnerability). Mitigations are controls that block and lower the risk, but don't remove the vulnerability. Remediation is patching and removing the risk known vulnerability from being exploited.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - patch host
-    - deploy patch
-    - run job
-    - execute program
-    - run script
-    - execute action
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. Apply remediations (e.g. patches) to infected and not infected hosts that pertain to the vulnerability found being used by the incident (remove the risk). Identify and fix any systems not accepting or validating the patch (e.g. needing reboots) as soon as possible.
-  2. This may require scheduling due to business needs. In a mass incident, emergency change requests can be used to support patching.  In single instance events, the risk to outage vs the risk to mitigate may be acceptable. If acceptable, then move remediations to lessons learned processing before closing out the request.  Put in place, mitigations for at least detections and if possible protection rules to minimize impact while remediation is being scheduled.
-  3. Apply mitigations such as IPS and host based firewall rules to mitigate (reduce the risk) of the vulnerability being exploited for at least detection to notify when occurring and if containment and eradication has failed.
-  4. Monitor detections to ensure containment is working and determine when eradication is beginning to be effective.
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - patch host
+  - deploy patch
+  - run job
+  - execute program
+  - run script
+  - execute action
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Apply mitigations or remediate any known affecting vulnerabilities that
+  linked to the incidents.  (e.g. SMB MS17-010 - Windows SMB Remote Code Execution
+  Vulnerability). Mitigations are controls that block and lower the risk, but don''t
+  remove the vulnerability. Remediation is patching and removing the risk known vulnerability
+  from being exploited.
+
+  '
+id: 70362de1-bfef-4a0f-893f-3e0d605ed9b7
+name: Mitigate or remediate any vulnerabilities
+references:
+- 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Apply remediations (e.g. patches) to infected and not infected hosts
+  that pertain to the vulnerability found being used by the incident (remove the risk).
+  Identify and fix any systems not accepting or validating the patch (e.g. needing
+  reboots) as soon as possible.
+
+  2. This may require scheduling due to business needs. In a mass incident, emergency
+  change requests can be used to support patching.  In single instance events, the
+  risk to outage vs the risk to mitigate may be acceptable. If acceptable, then move
+  remediations to lessons learned processing before closing out the request.  Put
+  in place, mitigations for at least detections and if possible protection rules to
+  minimize impact while remediation is being scheduled.
+
+  3. Apply mitigations such as IPS and host based firewall rules to mitigate (reduce
+  the risk) of the vulnerability being exploited for at least detection to notify
+  when occurring and if containment and eradication has failed.
+
+  4. Monitor detections to ensure containment is working and determine when eradication
+  is beginning to be effective.
+
+  '
diff --git a/response_tasks/playbooks/practice_real_world_events.yml b/response_tasks/playbooks/practice_real_world_events.yml
index 7f382ed2c9..67e8b52f6b 100644
--- a/response_tasks/playbooks/practice_real_world_events.yml
+++ b/response_tasks/playbooks/practice_real_world_events.yml
@@ -1,19 +1,26 @@
-name: Practice Real World Events
+author: ButterCup
+automation:
+  action: null
+  is_note_required: false
+  playbook: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-17'
+description: Practice in the real environment. Sharpen Response skills within your
+  organization by simulating real world with training exercises within your organization.
 id: 97d00b14-dd01-47e4-b7eb-0a82f4998c4e
+name: Practice Real World Events
+references:
+- 3.1 Preparation - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
 tags:
   nist: RS.RP
-description: Practice in the real environment. Sharpen Response skills within your organization by simulating real world with training exercises within your organization.
-automation:
-    is_note_required: false
-    role:
-    sla_type: minutes
-    sla:
-    action:
-    playbook:
-references:
-  - 3.1 Preparation - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: Make sure that the response actions have been performed during an internal exercise by your Incident Response Team.
-  You need to make sure that when an Incident happens, the team understands the how and this is not the first time they have seen the tasks. This is best done by being able to execute the actual steps in **your environment**, i.e. blocking an IP address or a domain name.
-date: '2020-07-17'
+  product:
+  - Splunk Phantom
+type: response
 version: 1
-author: ButterCup
+workflow: Make sure that the response actions have been performed during an internal
+  exercise by your Incident Response Team. You need to make sure that when an Incident
+  happens, the team understands the how and this is not the first time they have seen
+  the tasks. This is best done by being able to execute the actual steps in **your
+  environment**, i.e. blocking an IP address or a domain name.
diff --git a/response_tasks/playbooks/prepare_for_incident_handling.yml b/response_tasks/playbooks/prepare_for_incident_handling.yml
index 17b6628d51..4120e4926c 100644
--- a/response_tasks/playbooks/prepare_for_incident_handling.yml
+++ b/response_tasks/playbooks/prepare_for_incident_handling.yml
@@ -1,46 +1,98 @@
-name: Prepare for incident handling
-id: 91d4566e-a292-4f0a-b894-dde23bde3f08
-tags:
-  nist:
-    RS.RP
-description: |
-  The lists in the workflow below provide examples of tools and resources available that may be of value during incident handling. These lists are intended to be a starting point for discussions about which tools and resources an organizations incident handlers need.
-automation:
-  is_note_required: false
-  role:
-  sla_type: minutes
-  sla:
-  action:
-  playbook: ""
-references:
-  - 3.1.1 Preparing to Handle Incidents - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-    ## Incident Handler Communications and Facilities:
-    * Contact information for team members and others within and outside the organization (primary and backup contacts), such as law enforcement and other incident response teams; information may include phone numbers, email addresses, public encryption keys (in accordance with the encryption software described below), and instructions for verifying the contacts identity
-    * On-call information for other teams within the organization, including escalation information
-    * Incident reporting mechanisms, such as phone numbers, email addresses, online forms, and secure instant messaging systems that users can use to report suspected incidents; at least one mechanism should permit people to report incidents anonymously
-    * Issue tracking system for tracking incident information, status, etc.
-    * Smartphones to be carried by team members for off-hour support and onsite communications
-    * Encryption software to be used for communications among team members, within the organization and with external parties; for Federal agencies, software must use a FIPS-validated encryption algorithm20
-    * War room for central communication and coordination; if a permanent war room is not necessary or practical, the team should create a procedure for procuring a temporary war room when needed
-    * Secure storage facility for securing evidence and other sensitive materials
-    ## Incident Analysis Hardware and Software
-    * Digital forensic workstations21 and/or backup devices to create disk images, preserve log files, and save other relevant incident data
-    * Laptops for activities such as analyzing data, sniffing packets, and writing reports
-    * Spare workstations, servers, and networking equipment, or the virtualized equivalents, which may be used for many purposes, such as restoring backups and trying out malware
-    * Blank removable media
-    * Portable printer to print copies of log files and other evidence from non-networked systems
-    * Packet sniffers and protocol analyzers to capture and analyze network traffic
-    * Digital forensic software to analyze disk images
-    * Removable media with trusted versions of programs to be used to gather evidence from systems
-    * Evidence gathering accessories, including hard-bound notebooks, digital cameras, audio recorders, chain of custody forms, evidence storage bags and tags, and evidence tape, to preserve evidence for possible legal actions
-    ## Incident Analysis Resources:
-    * Port lists, including commonly used ports and Trojan horse ports
-    * Documentation for OSs, applications, protocols, and intrusion detection and antivirus products Network diagrams and lists of critical assets, such as database servers
-    * Current baselines of expected network, system, and application activity
-    * Cryptographic hashes of critical files22 to speed incident analysis, verification, and eradication
-    ## Incident Mitigation Software:
-    * Access to images of clean OS and application installations for restoration and recovery purposes
-date: '2020-07-17'
-version: 1
 author: ButterCup, Splunk
+automation:
+  action: null
+  is_note_required: false
+  playbook: ''
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-17'
+description: 'The lists in the workflow below provide examples of tools and resources
+  available that may be of value during incident handling. These lists are intended
+  to be a starting point for discussions about which tools and resources an organizations
+  incident handlers need.
+
+  '
+id: 91d4566e-a292-4f0a-b894-dde23bde3f08
+name: Prepare for incident handling
+references:
+- 3.1.1 Preparing to Handle Incidents - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '## Incident Handler Communications and Facilities:
+
+  * Contact information for team members and others within and outside the organization
+  (primary and backup contacts), such as law enforcement and other incident response
+  teams; information may include phone numbers, email addresses, public encryption
+  keys (in accordance with the encryption software described below), and instructions
+  for verifying the contacts identity
+
+  * On-call information for other teams within the organization, including escalation
+  information
+
+  * Incident reporting mechanisms, such as phone numbers, email addresses, online
+  forms, and secure instant messaging systems that users can use to report suspected
+  incidents; at least one mechanism should permit people to report incidents anonymously
+
+  * Issue tracking system for tracking incident information, status, etc.
+
+  * Smartphones to be carried by team members for off-hour support and onsite communications
+
+  * Encryption software to be used for communications among team members, within the
+  organization and with external parties; for Federal agencies, software must use
+  a FIPS-validated encryption algorithm20
+
+  * War room for central communication and coordination; if a permanent war room is
+  not necessary or practical, the team should create a procedure for procuring a temporary
+  war room when needed
+
+  * Secure storage facility for securing evidence and other sensitive materials
+
+  ## Incident Analysis Hardware and Software
+
+  * Digital forensic workstations21 and/or backup devices to create disk images, preserve
+  log files, and save other relevant incident data
+
+  * Laptops for activities such as analyzing data, sniffing packets, and writing reports
+
+  * Spare workstations, servers, and networking equipment, or the virtualized equivalents,
+  which may be used for many purposes, such as restoring backups and trying out malware
+
+  * Blank removable media
+
+  * Portable printer to print copies of log files and other evidence from non-networked
+  systems
+
+  * Packet sniffers and protocol analyzers to capture and analyze network traffic
+
+  * Digital forensic software to analyze disk images
+
+  * Removable media with trusted versions of programs to be used to gather evidence
+  from systems
+
+  * Evidence gathering accessories, including hard-bound notebooks, digital cameras,
+  audio recorders, chain of custody forms, evidence storage bags and tags, and evidence
+  tape, to preserve evidence for possible legal actions
+
+  ## Incident Analysis Resources:
+
+  * Port lists, including commonly used ports and Trojan horse ports
+
+  * Documentation for OSs, applications, protocols, and intrusion detection and antivirus
+  products Network diagrams and lists of critical assets, such as database servers
+
+  * Current baselines of expected network, system, and application activity
+
+  * Cryptographic hashes of critical files22 to speed incident analysis, verification,
+  and eradication
+
+  ## Incident Mitigation Software:
+
+  * Access to images of clean OS and application installations for restoration and
+  recovery purposes
+
+  '
diff --git a/response_tasks/playbooks/preventing_incidents.yml b/response_tasks/playbooks/preventing_incidents.yml
index 6724d6d5d4..6d2f2a32eb 100644
--- a/response_tasks/playbooks/preventing_incidents.yml
+++ b/response_tasks/playbooks/preventing_incidents.yml
@@ -1,26 +1,54 @@
-name: Preventing Incidents
-id: 5b7c5d18-6598-412b-a4f1-e66e92890503
-tags:
-  nist:
-    RS.RP
-description: Keeping the number of incidents reasonably low is very important to protect the business processes of the organization. It is outside the scope of this document to provide specific advice on securing networks, systems, and applications. Although incident response teams are generally not responsible for securing resources, they are advocates of sound security practices. An incident response team should identify problems that the organization is otherwise not aware of and play a key role in risk assessment and training by identifying gaps.
-automation:
-  is_note_required: false
-  role:
-  sla_type: minutes
-  sla:
-  action:
-  playbook: ""
-references:
-  - 3.1.2 Prevent Incidents - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  The following:
-  * Risk Assessments. Periodic risk assessments of systems and applications should determine what allowing staff to emphasize monitoring and response activities for those resources.
-  * Host Security. All hosts should be hardened appropriately using standard configurations. In addition to keeping each host properly patched, hosts should be configured to follow the principle of least privilege—granting users only the privileges necessary for performing their authorized tasks. Hosts should have auditing enabled and should log significant security-related events. The security of hosts risks are posed by combinations of threats and vulnerabilities.
-  * Applicable threats, including organization-specific threats. Each risk should be prioritized, and the risks can be mitigated, transferred, or accepted until a reasonable overall level of risk is reached. Another benefit of conducting risk assessments regularly is that critical resources are identified, Content Automation Protocol (SCAP) expressed operating system and application configuration and their configurations should be continuously monitored. checklists to assist in securing hosts consistently and effectively.
-  * Network Security. The network perimeter should be configured to deny all activity that is not expressly permitted. This includes securing all connection points, such as virtual private networks (VPNs) and dedicated connections to other organizations.
-  * Malware Prevention. Software to detect and stop malware should be deployed throughout the organization. Malware protection should be deployed at the host level (e.g., server and workstation operating systems), the application server level (e.g., email server, web proxies), and the application
-  * User Awareness and Training. Users should be made aware of policies and procedures regarding appropriate use of networks, systems, and applications. Applicable lessons learned from previous incidents should also be shared with users so they can see how their actions could affect the organization. Improving user awareness regarding incidents should reduce the frequency of incidents. IT staff should be trained so that they can maintain their networks, systems, and applications in accordance with the organization’s security standards.
-date: '2020-07-17'
-version: 1
 author: ButterCup, Splunk
+automation:
+  action: null
+  is_note_required: false
+  playbook: ''
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-17'
+description: Keeping the number of incidents reasonably low is very important to protect
+  the business processes of the organization. It is outside the scope of this document
+  to provide specific advice on securing networks, systems, and applications. Although
+  incident response teams are generally not responsible for securing resources, they
+  are advocates of sound security practices. An incident response team should identify
+  problems that the organization is otherwise not aware of and play a key role in
+  risk assessment and training by identifying gaps.
+id: 5b7c5d18-6598-412b-a4f1-e66e92890503
+name: Preventing Incidents
+references:
+- 3.1.2 Prevent Incidents - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "The following:\n* Risk Assessments. Periodic risk assessments of systems\
+  \ and applications should determine what allowing staff to emphasize monitoring\
+  \ and response activities for those resources.\n* Host Security. All hosts should\
+  \ be hardened appropriately using standard configurations. In addition to keeping\
+  \ each host properly patched, hosts should be configured to follow the principle\
+  \ of least privilege\u2014granting users only the privileges necessary for performing\
+  \ their authorized tasks. Hosts should have auditing enabled and should log significant\
+  \ security-related events. The security of hosts risks are posed by combinations\
+  \ of threats and vulnerabilities.\n* Applicable threats, including organization-specific\
+  \ threats. Each risk should be prioritized, and the risks can be mitigated, transferred,\
+  \ or accepted until a reasonable overall level of risk is reached. Another benefit\
+  \ of conducting risk assessments regularly is that critical resources are identified,\
+  \ Content Automation Protocol (SCAP) expressed operating system and application\
+  \ configuration and their configurations should be continuously monitored. checklists\
+  \ to assist in securing hosts consistently and effectively.\n* Network Security.\
+  \ The network perimeter should be configured to deny all activity that is not expressly\
+  \ permitted. This includes securing all connection points, such as virtual private\
+  \ networks (VPNs) and dedicated connections to other organizations.\n* Malware Prevention.\
+  \ Software to detect and stop malware should be deployed throughout the organization.\
+  \ Malware protection should be deployed at the host level (e.g., server and workstation\
+  \ operating systems), the application server level (e.g., email server, web proxies),\
+  \ and the application\n* User Awareness and Training. Users should be made aware\
+  \ of policies and procedures regarding appropriate use of networks, systems, and\
+  \ applications. Applicable lessons learned from previous incidents should also be\
+  \ shared with users so they can see how their actions could affect the organization.\
+  \ Improving user awareness regarding incidents should reduce the frequency of incidents.\
+  \ IT staff should be trained so that they can maintain their networks, systems,\
+  \ and applications in accordance with the organization\u2019s security standards.\n"
diff --git a/response_tasks/playbooks/provide_lessons_learned_tasks_or_changes.yml b/response_tasks/playbooks/provide_lessons_learned_tasks_or_changes.yml
index c77dca51b4..4b5b0b72b0 100644
--- a/response_tasks/playbooks/provide_lessons_learned_tasks_or_changes.yml
+++ b/response_tasks/playbooks/provide_lessons_learned_tasks_or_changes.yml
@@ -1,26 +1,35 @@
-name: Provide lessons learned tasks or changes
-id: 43fc5e87-d819-460a-a740-de2066b18a29
-tags:
-  nist:
-    RS.RP
-description: |
-  Create any tasks or service requests based on the recommendations of the review of the incident or with the process flow from alert detection to provide feedback from false positives.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - create ticket
-  playbooks:
-    - scm: local
-      playook: Create false-positve reduction service request
-references:
-  - 3.4.1 Lessons Learned - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. Create service requests or documentation updates from the results of the review of the followup report and incident review meeting
-  2. Create service request for updating alert detection from feedback from false positives
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - create ticket
+  is_note_required: false
+  playbooks:
+  - playook: Create false-positve reduction service request
+    scm: local
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Create any tasks or service requests based on the recommendations of
+  the review of the incident or with the process flow from alert detection to provide
+  feedback from false positives.
+
+  '
+id: 43fc5e87-d819-460a-a740-de2066b18a29
+name: Provide lessons learned tasks or changes
+references:
+- 3.4.1 Lessons Learned - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Create service requests or documentation updates from the results of
+  the review of the followup report and incident review meeting
+
+  2. Create service request for updating alert detection from feedback from false
+  positives
+
+  '
diff --git a/response_tasks/playbooks/raise_personnel_awareness.yml b/response_tasks/playbooks/raise_personnel_awareness.yml
index 5d51e24e37..f5723d6ffa 100644
--- a/response_tasks/playbooks/raise_personnel_awareness.yml
+++ b/response_tasks/playbooks/raise_personnel_awareness.yml
@@ -1,19 +1,24 @@
-name: Raise personnel awareness
-id: 145a82b5-cafd-468e-b487-737fdf13d6a4
-tags:
-  nist:
-    RS.RP
-description: Raise personnel awareness regarding phishing, ransomware, social engineering, and other attacks that involve user interaction
-automation:
-    is_note_required: false
-    role:
-    sla_type: minutes
-    sla:
-    action:
-    playbook:
-references:
-  - https://attack.mitre.org/mitigations/M1017/
-workflow: Train users to be aware of access or manipulation attempts by an adversary to reduce the risk of successful spearphishing, social engineering, and other techniques that involve user interaction.
-date: '2020-07-17'
-version: 1
 author: ButterCup, Splunk, @atc_react
+automation:
+  action: null
+  is_note_required: false
+  playbook: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-17'
+description: Raise personnel awareness regarding phishing, ransomware, social engineering,
+  and other attacks that involve user interaction
+id: 145a82b5-cafd-468e-b487-737fdf13d6a4
+name: Raise personnel awareness
+references:
+- https://attack.mitre.org/mitigations/M1017/
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: Train users to be aware of access or manipulation attempts by an adversary
+  to reduce the risk of successful spearphishing, social engineering, and other techniques
+  that involve user interaction.
diff --git a/response_tasks/playbooks/remove_malicious_content.yml b/response_tasks/playbooks/remove_malicious_content.yml
index 2ac775f2cd..c5397b59ff 100644
--- a/response_tasks/playbooks/remove_malicious_content.yml
+++ b/response_tasks/playbooks/remove_malicious_content.yml
@@ -1,32 +1,46 @@
-name: Remove malicious content
-id: 26cd22c6-4b67-4dc5-b8d1-f5ef9b5d8226
-tags:
-  nist:
-    RS.RP
-description: |
-  Remove malicious content. There are multiple ways to accomplish this. Depending on the maturity and size of the incident, this can be as simple as re-imaging a single system to full remediation via a deployable package from your antivirus vendor or removal of offending file via your enterprise detection and response (EDR) tool. Network attacks external to your environment will need to be managed with the support of your ISP. Internal attacks should be segmented for containment and then removing offending systems or malicious content from those offending systems.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - create ticket
-    - deploy patch
-    - run script
-    - add tag
-    - execute action
-    - execute program
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. Determine the correct approach based on maturity and size of the incident
-  2. Employ eradication and monitor the process to ensure the system does not get re-infected. Reinfection is an indication that your containment measures are inadequate to stop the incident.
-  3. Apply any new gold image with up to date patches on re-imaged systems.
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - create ticket
+  - deploy patch
+  - run script
+  - add tag
+  - execute action
+  - execute program
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Remove malicious content. There are multiple ways to accomplish this.
+  Depending on the maturity and size of the incident, this can be as simple as re-imaging
+  a single system to full remediation via a deployable package from your antivirus
+  vendor or removal of offending file via your enterprise detection and response (EDR)
+  tool. Network attacks external to your environment will need to be managed with
+  the support of your ISP. Internal attacks should be segmented for containment and
+  then removing offending systems or malicious content from those offending systems.
+
+  '
+id: 26cd22c6-4b67-4dc5-b8d1-f5ef9b5d8226
+name: Remove malicious content
+references:
+- 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Determine the correct approach based on maturity and size of the incident
+
+  2. Employ eradication and monitor the process to ensure the system does not get
+  re-infected. Reinfection is an indication that your containment measures are inadequate
+  to stop the incident.
+
+  3. Apply any new gold image with up to date patches on re-imaged systems.
+
+  '
diff --git a/response_tasks/playbooks/restore_systems_to_operational_status.yml b/response_tasks/playbooks/restore_systems_to_operational_status.yml
index 020147319c..8c01b2ab6e 100644
--- a/response_tasks/playbooks/restore_systems_to_operational_status.yml
+++ b/response_tasks/playbooks/restore_systems_to_operational_status.yml
@@ -1,28 +1,44 @@
-name: Restore systems to operational status
-id: bb515cf6-40b5-4005-af04-6f63439df7b4
-tags:
-  nist:
-    RS.RP
-description: |
-  Depending on the sized of the incident, reimaging systems maybe a viable eradication and recovery process combined. Once restored to gold image (standardized corporate image) with immediate patching and updating of all known vulnerabilities. Create a service request for the Help Desk to re-image the host. Server or network equipment should be baselined and restored by the owning team.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - create ticket
-  playbooks:
-    - scm:
-      playook: Create a service request for re-image
-references:
-  - 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. Create a request for re-imaging system for eradication and recovery. Re-imaging will not be possible in a mass incident.
-  2. For mass incidents, provide immediate patching and reporting of non-compliant patching or antivirus removal tools.  Thorough forensic and reverse malware engineering will provide the necessary details to minimize complete recovery processes.
-  3. Do not restore localized customer files (/home/user, or /User/<username>), but only on a case by case basis and thorough review of the files being restored.
-  4. If the eradication process isn't reducing the number of infected hosts, eradication is missing a persistence mechanism or containment has failed.
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - create ticket
+  is_note_required: false
+  playbooks:
+  - playook: Create a service request for re-image
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Depending on the sized of the incident, reimaging systems maybe a viable
+  eradication and recovery process combined. Once restored to gold image (standardized
+  corporate image) with immediate patching and updating of all known vulnerabilities.
+  Create a service request for the Help Desk to re-image the host. Server or network
+  equipment should be baselined and restored by the owning team.
+
+  '
+id: bb515cf6-40b5-4005-af04-6f63439df7b4
+name: Restore systems to operational status
+references:
+- 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: '1. Create a request for re-imaging system for eradication and recovery.
+  Re-imaging will not be possible in a mass incident.
+
+  2. For mass incidents, provide immediate patching and reporting of non-compliant
+  patching or antivirus removal tools.  Thorough forensic and reverse malware engineering
+  will provide the necessary details to minimize complete recovery processes.
+
+  3. Do not restore localized customer files (/home/user, or /User/<username>), but
+  only on a case by case basis and thorough review of the files being restored.
+
+  4. If the eradication process isn''t reducing the number of infected hosts, eradication
+  is missing a persistence mechanism or containment has failed.
+
+  '
diff --git a/response_tasks/playbooks/suspicious_email_attachment_investigate_and_delete.yml b/response_tasks/playbooks/suspicious_email_attachment_investigate_and_delete.yml
index 4834b07433..f7b6135117 100644
--- a/response_tasks/playbooks/suspicious_email_attachment_investigate_and_delete.yml
+++ b/response_tasks/playbooks/suspicious_email_attachment_investigate_and_delete.yml
@@ -1,24 +1,34 @@
-name: Suspicious Email Attachment Investigate and Delete
-id: 3096f721-8842-42ce-2fc7-742d8372b712
-tags:
-  nist:
-    RS.RP
-description: |
-  Investigate an email with a suspicious file attachment detected by Splunk Enterprise Security. Detonate the file attachment in a sandbox, gather network behavior from the sandbox results, and pivot on those network indicators with both external reputation queries and internal Splunk Common Information Model searches. After confirming the results with an analyst prompt, delete the email from the user's inbox, hopefully before they have opened it.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - set status
-  playbooks:
-    - scm: community
-      playook: suspicious_email_attachment_investigate_and_delete
-references:
-  - ""
-workflow: |
-  Synchronize the community playbook repository in Phantom, then open the playbook and follow the deployment notes to configure it for your environment.
-date: '2019-06-03'
-version: 1
 author: Philip Royer, Splunk
+automation:
+  actions:
+  - set status
+  is_note_required: false
+  playbooks:
+  - playook: suspicious_email_attachment_investigate_and_delete
+    scm: community
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2019-06-03'
+description: 'Investigate an email with a suspicious file attachment detected by Splunk
+  Enterprise Security. Detonate the file attachment in a sandbox, gather network behavior
+  from the sandbox results, and pivot on those network indicators with both external
+  reputation queries and internal Splunk Common Information Model searches. After
+  confirming the results with an analyst prompt, delete the email from the user''s
+  inbox, hopefully before they have opened it.
+
+  '
+id: 3096f721-8842-42ce-2fc7-742d8372b712
+name: Suspicious Email Attachment Investigate and Delete
+references:
+- ''
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: 'Synchronize the community playbook repository in Phantom, then open the
+  playbook and follow the deployment notes to configure it for your environment.
+
+  '
diff --git a/response_tasks/playbooks/validate_hosts_eradicated.yml b/response_tasks/playbooks/validate_hosts_eradicated.yml
index ce6ae40138..eddd244606 100644
--- a/response_tasks/playbooks/validate_hosts_eradicated.yml
+++ b/response_tasks/playbooks/validate_hosts_eradicated.yml
@@ -1,33 +1,44 @@
-name: Validate hosts eradicated
-id: b678705c-12a6-428b-a631-ed579332bc99
-tags:
-  nist:
-    RS.RP
-description: |
-  Confirm and acknowledge eradication steps are working as expected and number of infected host(s) is dropping.  Validation here will allow the incident responder to move to recovery phase. If single instance, validate the reimage process was completed by validating new image creation date. (win - 'systeminfo | find Original'', linux - 'ls -ld /var/log/installer', macOS - /var/log/install.log.# (oldest))
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-requirements:
-workflow: |
-  1. If single instance, validate the reimage process was completed by validating new image creation date.
-    a. win - 'systeminfo | find Original''
-    b. linux - 'ls -ld /var/log/installer'
-    c. macOS - /var/log/install.log.# (oldest))
-  2. If this host has been reinfected more than once, conduct a formal forensic review to ensure all malicious content has been removed.
-  2. If mass incident follows steps 2-5, determine if the number of infected host is reducing
-  3. Determine if the number of (re)infected host is increasing
-  4. If the eradication process isn't reducing the number of infected hosts, eradication is missing an persistence mechanism or containment has failed
-  5. If the eradication process shows new hosts are being infected, your containment process is failing and/or the incident has mutated. (e.g. polymorphic malware with a active vulnerability or actor changed tactics and is now using ping for command and control and uploaded new malware). If this occurs return to Detection and Analysis Phase and continue forensic analysis.
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - run query
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Confirm and acknowledge eradication steps are working as expected and
+  number of infected host(s) is dropping.  Validation here will allow the incident
+  responder to move to recovery phase. If single instance, validate the reimage process
+  was completed by validating new image creation date. (win - ''systeminfo | find
+  Original'''', linux - ''ls -ld /var/log/installer'', macOS - /var/log/install.log.#
+  (oldest))
+
+  '
+id: b678705c-12a6-428b-a631-ed579332bc99
+name: Validate hosts eradicated
+references:
+- 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+requirements: null
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "1. If single instance, validate the reimage process was completed by validating\
+  \ new image creation date.\n  a. win - 'systeminfo | find Original''\n  b. linux\
+  \ - 'ls -ld /var/log/installer'\n  c. macOS - /var/log/install.log.# (oldest))\n\
+  2. If this host has been reinfected more than once, conduct a formal forensic review\
+  \ to ensure all malicious content has been removed.\n2. If mass incident follows\
+  \ steps 2-5, determine if the number of infected host is reducing\n3. Determine\
+  \ if the number of (re)infected host is increasing\n4. If the eradication process\
+  \ isn't reducing the number of infected hosts, eradication is missing an persistence\
+  \ mechanism or containment has failed\n5. If the eradication process shows new hosts\
+  \ are being infected, your containment process is failing and/or the incident has\
+  \ mutated. (e.g. polymorphic malware with a active vulnerability or actor changed\
+  \ tactics and is now using ping for command and control and uploaded new malware).\
+  \ If this occurs return to Detection and Analysis Phase and continue forensic analysis.\n"
diff --git a/response_tasks/playbooks/validate_restored_hosts.yml b/response_tasks/playbooks/validate_restored_hosts.yml
index 8d09fd3da0..cf9b89a26a 100644
--- a/response_tasks/playbooks/validate_restored_hosts.yml
+++ b/response_tasks/playbooks/validate_restored_hosts.yml
@@ -1,28 +1,34 @@
-name: Validate restored hosts
-id: 8218bcf6-739b-4f76-8952-eb133480ad8d
-tags:
-  nist:
-    RS.RP
-description: |
-   Validate each reimaged system was completed by validating new image creation date. ** win: systeminfo | find Original ** linux: ls -ld /var/log/installer ** macOS: /var/log/install.log.# (oldest date of entry). If more surgical, antivirus package or enterprise detection and response removal process has successfully completed and system is showing no signs of indicators of the incident.
-automation:
-  role:
-  sla_type: minutes
-  sla:
-  is_note_required: false
-  actions:
-    - run query
-  playbooks:
-    - scm:
-      playook:
-references:
-  - 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-workflow: |
-  1. If single instance, validate the reimage process was completed by validating new image creation date
-    a. win - 'systeminfo | find Original''
-    b. linux - 'ls -ld /var/log/installer'
-    c. macOS - /var/log/install.log.# (oldest))
-  2. Review existing monitoring to ensure host is working as expected and within normal parameters
-date: '2020-07-30'
-version: 1
 author: ButterCup, Splunk
+automation:
+  actions:
+  - run query
+  is_note_required: false
+  playbooks:
+  - playook: null
+    scm: null
+  role: null
+  sla: null
+  sla_type: minutes
+date: '2020-07-30'
+description: 'Validate each reimaged system was completed by validating new image
+  creation date. ** win: systeminfo | find Original ** linux: ls -ld /var/log/installer
+  ** macOS: /var/log/install.log.# (oldest date of entry). If more surgical, antivirus
+  package or enterprise detection and response removal process has successfully completed
+  and system is showing no signs of indicators of the incident.
+
+  '
+id: 8218bcf6-739b-4f76-8952-eb133480ad8d
+name: Validate restored hosts
+references:
+- 3.3.4 Eradication and Recovery - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+tags:
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: "1. If single instance, validate the reimage process was completed by validating\
+  \ new image creation date\n  a. win - 'systeminfo | find Original''\n  b. linux\
+  \ - 'ls -ld /var/log/installer'\n  c. macOS - /var/log/install.log.# (oldest))\n\
+  2. Review existing monitoring to ensure host is working as expected and within normal\
+  \ parameters\n"
diff --git a/responses/NIST_800-61r2_response_plan.yml b/responses/NIST_800-61r2_response_plan.yml
index 0700970e6f..ab0bd59274 100644
--- a/responses/NIST_800-61r2_response_plan.yml
+++ b/responses/NIST_800-61r2_response_plan.yml
@@ -1,29 +1,37 @@
-name: NIST 800-61r2 Response Plan
-id: b974f8bb-2999-4480-94ef-8a90029b8759
-description: Response plan built for the NIST framework described in NIST 800-61r2 (https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf)
-is_note_required: false
-tags:
-    analytics_story: all
-    nist: RS.RP
-references:
-  - https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
-response_phase:
-  - preparation_nist:
-     - id: d360707d-9214-4449-b15d-9d3cf134209a
-       name: Preparation
-  - detection_analysis:
-    - id: a6eec2aa-3ec8-4f16-9c09-b8537873047d
-      name:  Detection and Analysis
-  - contain_eradicate_recover:
-      - id: 838ad8e8-1701-4829-be89-51a997fd9852
-        name: Contain Eradicate Recover
-  - post_incident:
-      - id: 001209bc-2f94-4dc7-b21e-9598c41eaa80
-        name: Post Incident
-workflow: Organizations should use this response plan as template to define the processes for their security operations teams.
-    With this as the template and then customizing the response plan around attack vectors. Organizations can fashion a framework for thier response procedures.
-    Response plans and response procedures are different in the setting that procedures should have the actual step-by-step actions vs an response plan that discuses and deomonstrates actions without specifcs and aligns to the policy.
-    > Preparation should not be imported into response tools that support ingestion (hive, phantom, xsoar, etc).
-date: '2020-04-21'
-version: 1
 author: ButterCup
+date: '2020-04-21'
+description: Response plan built for the NIST framework described in NIST 800-61r2
+  (https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf)
+id: b974f8bb-2999-4480-94ef-8a90029b8759
+is_note_required: false
+name: NIST 800-61r2 Response Plan
+references:
+- https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+response_phase:
+- preparation_nist:
+  - id: d360707d-9214-4449-b15d-9d3cf134209a
+    name: Preparation
+- detection_analysis:
+  - id: a6eec2aa-3ec8-4f16-9c09-b8537873047d
+    name: Detection and Analysis
+- contain_eradicate_recover:
+  - id: 838ad8e8-1701-4829-be89-51a997fd9852
+    name: Contain Eradicate Recover
+- post_incident:
+  - id: 001209bc-2f94-4dc7-b21e-9598c41eaa80
+    name: Post Incident
+tags:
+  analytics_story: all
+  nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
+version: 1
+workflow: Organizations should use this response plan as template to define the processes
+  for their security operations teams. With this as the template and then customizing
+  the response plan around attack vectors. Organizations can fashion a framework for
+  thier response procedures. Response plans and response procedures are different
+  in the setting that procedures should have the actual step-by-step actions vs an
+  response plan that discuses and deomonstrates actions without specifcs and aligns
+  to the policy. > Preparation should not be imported into response tools that support
+  ingestion (hive, phantom, xsoar, etc).
diff --git a/responses/NIST_PICERL_response_plan.yml b/responses/NIST_PICERL_response_plan.yml
index 5af4051e9d..c1a4e69b75 100644
--- a/responses/NIST_PICERL_response_plan.yml
+++ b/responses/NIST_PICERL_response_plan.yml
@@ -1,35 +1,45 @@
-author: "ButterCup, Splunk"
-date: "2020-04-21"
+author: ButterCup, Splunk
+date: '2020-04-21'
 description: NIST incident response process that follows PICERL (https://www.sans.org/media/score/504-incident-response-cycle.pdf)
 id: 8a7ea67a-dd53-468e-aeef-b75aed0a877c
 is_note_required: false
-name: "NIST PICERL Response Plan"
+name: NIST PICERL Response Plan
 references:
-  - "https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf"
-  - "https://www.sans.org/reading-room/whitepapers/incident/incident-handlers-handbook-33901"
+- https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf
+- https://www.sans.org/reading-room/whitepapers/incident/incident-handlers-handbook-33901
 response_phase:
-  - preparation:
-      - id: d360707d-9214-4449-b15d-9d3cf134209a
-        name: Preparation
-  - identification:
-      - id: 6cdd56ba-5ffd-46a9-9dde-d25ce755c100
-        name: Identification
-  - containment:
-      - id: 5d790fae-8ba6-4fc9-b288-78b67ef8370c
-        name: Containment
-  - eradication:
-      - id: d3b80e0e-4e85-4259-a13c-69ef20987e1c
-        name: Eradication
-  - recovery:
-      - id: cae4dcdb-f81b-45ec-b0d6-a00cec468e9a
-        name: Recovery
-  - lessons_learned:
-      - id: 001209bc-2f94-4dc7-b21e-9598c41eaa80
-        name: "Post-Incident Activities"
+- preparation:
+  - id: d360707d-9214-4449-b15d-9d3cf134209a
+    name: Preparation
+- identification:
+  - id: 6cdd56ba-5ffd-46a9-9dde-d25ce755c100
+    name: Identification
+- containment:
+  - id: 5d790fae-8ba6-4fc9-b288-78b67ef8370c
+    name: Containment
+- eradication:
+  - id: d3b80e0e-4e85-4259-a13c-69ef20987e1c
+    name: Eradication
+- recovery:
+  - id: cae4dcdb-f81b-45ec-b0d6-a00cec468e9a
+    name: Recovery
+- lessons_learned:
+  - id: 001209bc-2f94-4dc7-b21e-9598c41eaa80
+    name: Post-Incident Activities
 tags:
   analytics_story:
-    - "Credential Dumping"
-    - Ransomeware
+  - Credential Dumping
+  - Ransomeware
   nist: RS.RP
+  product:
+  - Splunk Phantom
+type: response
 version: 1
-workflow: "Organizations should use this response plan as template to define the processes for their security operations teams. With this as the template and then customizing the response plan around attack vectors. Organizations can fashion a framework for thier response procedures. Response plans and response procedures are different in the setting that procedures should have the actual step-by-step actions vs an response plan that discuses and deomonstrates actions without specifcs and aligns to the policy. Preparation should not be imported into response tools that support ingestion (hive, phantom, xsoar, etc)."
+workflow: Organizations should use this response plan as template to define the processes
+  for their security operations teams. With this as the template and then customizing
+  the response plan around attack vectors. Organizations can fashion a framework for
+  thier response procedures. Response plans and response procedures are different
+  in the setting that procedures should have the actual step-by-step actions vs an
+  response plan that discuses and deomonstrates actions without specifcs and aligns
+  to the policy. Preparation should not be imported into response tools that support
+  ingestion (hive, phantom, xsoar, etc).
diff --git a/responses/credential_dumping_attack.yml b/responses/credential_dumping_attack.yml
index d16e285c4e..74516d18e5 100644
--- a/responses/credential_dumping_attack.yml
+++ b/responses/credential_dumping_attack.yml
@@ -1,20 +1,25 @@
-name: Credential Dumping Attack
-id: 570dd98e-6cab-443c-bdd8-3dbb5fe4188d
-version: 2
-date: '2020-07-16'
-description: This response workflow guide you through the investigation of a credential dumping attack.
 author: Patrick Bareiss, Splunk
+date: '2020-07-16'
+description: This response workflow guide you through the investigation of a credential
+  dumping attack.
+id: 570dd98e-6cab-443c-bdd8-3dbb5fe4188d
+name: Credential Dumping Attack
 response_phase:
-  - identification:
-      - id: c5506139-ef86-4cd9-8535-0512aa732e79
-        name: Process Chain Analysis
-      - id: 6ee5c067-8228-4926-abb2-54f2c59d726e
-        name: Analyze Malicious File
-      - id: 1d7b437a-5114-4b94-a585-04c3362ba08f
-        name: Malware Hunt and Contain
-  - containment:
-      - id: 60c4cfa5-81b7-44e2-9ad4-71524e4a3e78
-        name: Quarantaine Infected Host
+- identification:
+  - id: c5506139-ef86-4cd9-8535-0512aa732e79
+    name: Process Chain Analysis
+  - id: 6ee5c067-8228-4926-abb2-54f2c59d726e
+    name: Analyze Malicious File
+  - id: 1d7b437a-5114-4b94-a585-04c3362ba08f
+    name: Malware Hunt and Contain
+- containment:
+  - id: 60c4cfa5-81b7-44e2-9ad4-71524e4a3e78
+    name: Quarantaine Infected Host
 tags:
   analytics_story: Credential Dumping
-workflow: This workbook provides you a guide on how to investigate credential dumping with some automation to make data collection easier.
+  product:
+  - Splunk Phantom
+type: response
+version: 2
+workflow: This workbook provides you a guide on how to investigate credential dumping
+  with some automation to make data collection easier.
diff --git a/spec/baselines.spec.json b/spec/baselines.spec.json
index c13fc47497..d072456c6b 100644
--- a/spec/baselines.spec.json
+++ b/spec/baselines.spec.json
@@ -74,7 +74,7 @@
       "description": "An array of key value pairs for tagging",
       "examples": [
         {
-          "analytics_story": "suspicious_aws_ec2_activities",
+          "analytic_story": "suspicious_aws_ec2_activities",
           "custom_key": "custom_value"
         }
       ],
@@ -82,6 +82,34 @@
       "type": "object",
       "uniqueItems": true
     },
+    "datamodel": {
+      "$id": "#/properties/datamodel",
+      "default": "",
+      "description": "datamodel used in the search",
+      "examples": [
+        "Endpoint"
+      ],
+      "items": {
+        "enum": [
+          "Endpoint",
+          "Network_Traffic",
+          "Authentication",
+          "Change",
+          "Change_Analysis",
+          "Email",
+          "Endpoint",
+          "Network_Resolution",
+          "Network_Sessions",
+          "Network_Traffic",
+          "UEBA",
+          "Updates",
+          "Vulnerabilities",
+          "Web"
+        ],
+        "type": "string"
+      },
+      "type": "array"
+    },
     "version": {
       "$id": "#/properties/version",
       "default": 0,
diff --git a/spec/deployments.spec.json b/spec/deployments.spec.json
index 7014cfd201..389c0bba89 100644
--- a/spec/deployments.spec.json
+++ b/spec/deployments.spec.json
@@ -242,7 +242,7 @@
       "description": "An array of key value pairs for tagging",
       "examples": [
         {
-          "analytics_story": "credential_dumping"
+          "analytic_story": "credential_dumping"
         }
       ],
       "minItems": 1,
diff --git a/spec/detections.spec.json b/spec/detections.spec.json
index 9fe66a1f61..14b619fcf2 100644
--- a/spec/detections.spec.json
+++ b/spec/detections.spec.json
@@ -105,7 +105,7 @@
       "description": "An array of key value pairs for tagging",
       "examples": [
         {
-          "analytics_story": "credential_dumping",
+          "analytic_story": "credential_dumping",
           "kill_chain_phases": "Action on Objectives",
           "mitre_attack_id": "T1078.004",
           "cis20": "CIS 13",
@@ -127,18 +127,45 @@
       "default": "",
       "description": "type of detection",
       "examples": [
-        "ESCU"
+        "streaming"
       ],
       "items": {
         "enum": [
-          "ESCU",
-          "SSE",
-          "RBA"
+          "batch",
+          "streaming"
         ],
         "type": "string"
       },
       "type": "string"
     },
+    "datamodel": {
+      "$id": "#/properties/datamodel",
+      "default": "",
+      "description": "datamodel used in the search",
+      "examples": [
+        "Endpoint"
+      ],
+      "items": {
+        "enum": [
+          "Endpoint",
+          "Network_Traffic",
+          "Authentication",
+          "Change",
+          "Change_Analysis",
+          "Email",
+          "Endpoint",
+          "Network_Resolution",
+          "Network_Sessions",
+          "Network_Traffic",
+          "UEBA",
+          "Updates",
+          "Vulnerabilities",
+          "Web"
+        ],
+        "type": "string"
+      },
+      "type": "array"
+    },
     "version": {
       "$id": "#/properties/version",
       "default": 0,
diff --git a/spec/response_tasks.spec.json b/spec/response_tasks.spec.json
index 0ee36596b3..dfe84471cd 100644
--- a/spec/response_tasks.spec.json
+++ b/spec/response_tasks.spec.json
@@ -114,7 +114,7 @@
       "description": "An array of key value pairs for tagging",
       "examples": [
         {
-          "analytics_story": "credential_dumping"
+          "analytic_story": "credential_dumping"
         }
       ],
       "minItems": 1,
diff --git a/spec/responses.spec.json b/spec/responses.spec.json
index 65cf69cb36..4beb6cf9a4 100644
--- a/spec/responses.spec.json
+++ b/spec/responses.spec.json
@@ -85,7 +85,7 @@
       "description": "An array of key value pairs for tagging",
       "examples": [
         {
-          "analytics_story": "credential_dumping"
+          "analytic_story": "credential_dumping"
         }
       ],
       "minItems": 1,
diff --git a/spec/responses_phase.spec.json b/spec/responses_phase.spec.json
index cc57305c5c..4c46e46803 100644
--- a/spec/responses_phase.spec.json
+++ b/spec/responses_phase.spec.json
@@ -10,7 +10,7 @@
       "default": "",
       "description": "Author of the phase",
       "examples": [
-        "Rico Valdez, Patrick Barei\u00df, Splunk"
+        "Rico Valdez, Patrick Bareiß, Splunk"
       ],
       "type": "string"
     },
@@ -57,18 +57,16 @@
       "description": "Response phase is divided into task(s) to be completed. These will used to referenced known response_task parameters. Order is as positioned and with unique name.",
       "examples": [
         {
-            {
-              "id": "7c72d944-3995-4485-8e57-67b4c353989b",
-              "name": "Prepare for Incident Handling"
-            },
-            {
-              "id": "c36f3f48-e0bb-4c20-a62a-cdc8f6418892",
-              "name": "Preventing Incidents"
-            },
-            {
-              "id": "0dc849b2-2eb4-4fd2-add1-b6cc475765f0",
-              "name": "Practice"
-            }
+          "id": "7c72d944-3995-4485-8e57-67b4c353989b",
+          "name": "Prepare for Incident Handling"
+        },
+        {
+          "id": "c36f3f48-e0bb-4c20-a62a-cdc8f6418892",
+          "name": "Preventing Incidents"
+        },
+        {
+          "id": "0dc849b2-2eb4-4fd2-add1-b6cc475765f0",
+          "name": "Practice"
         }
       ],
       "minItems": 1,
@@ -81,7 +79,7 @@
       "description": "An array of key value pairs for tagging",
       "examples": [
         {
-          "analytics_story": "credential_dumping"
+          "analytic_story": "credential_dumping"
         }
       ],
       "minItems": 1,
@@ -97,7 +95,7 @@
       ],
       "type": "integer"
     },
-    "sla":{
+    "sla": {
       "$id": "#/properties/sla",
       "default": null,
       "description": "Measured integer for Service Level Agreement for completion of the phase",
@@ -107,7 +105,7 @@
       ],
       "type": "integer"
     },
-    "sla_type":{
+    "sla_type": {
       "$id": "#/properties/sla_type",
       "default": "minutes",
       "description": "Duration for measured integer for Service Level Agreement for completion of the phase (e.g. minutes, or hours, etc)",
@@ -124,9 +122,7 @@
       "default": [],
       "description": "A list of references for this response, phase or task (e.g. web or printed citation)",
       "examples": [
-        [
-          "https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf"
-        ]
+        "https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf"
       ],
       "items": {
         "$id": "#/properties/references/items",
@@ -139,7 +135,7 @@
         "type": "string"
       },
       "type": "array"
-    },
+    }
   },
   "required": [
     "name",
@@ -149,7 +145,7 @@
     "description",
     "author",
     "response_task",
-    "tags",
+    "tags"
   ],
   "title": "Response Schema",
   "type": "object"
diff --git a/spec/stories.spec.json b/spec/stories.spec.json
index 3eda58b394..f2384cbb81 100644
--- a/spec/stories.spec.json
+++ b/spec/stories.spec.json
@@ -75,7 +75,7 @@
       "description": "An explanation about the purpose of this instance.",
       "examples": [
         {
-          "analytics_story": "credential_dumping"
+          "analytic_story": "credential_dumping"
         }
       ],
       "minItems": 1,
diff --git a/stories/apache_struts_vulnerability.yml b/stories/apache_struts_vulnerability.yml
index 47bc1383c7..844472655f 100644
--- a/stories/apache_struts_vulnerability.yml
+++ b/stories/apache_struts_vulnerability.yml
@@ -1,10 +1,12 @@
-author: Rico Valdez, Splunk
+name: Apache Struts Vulnerability
+id: 2dcfd6a2-e7d2-4873-b6ba-adaf819d2a1e
+version: 1
 date: '2018-12-06'
+author: Rico Valdez, Splunk
+type: batch
 description: Detect and investigate activities--such as unusually long `Content-Type`
   length, suspicious java classes and web servers executing suspicious processes--consistent
   with attempts to exploit Apache Struts vulnerabilities.
-id: 2dcfd6a2-e7d2-4873-b6ba-adaf819d2a1e
-name: Apache Struts Vulnerability
 narrative: 'In March of 2017, a remote code-execution vulnerability in the Jakarta
   Multipart parser in Apache Struts, a widely used open-source framework for creating
   Java web applications, was disclosed and assigned to CVE-2017-5638. About two months
@@ -100,7 +102,7 @@ narrative: 'In March of 2017, a remote code-execution vulnerability in the Jakar
 references:
 - https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf
 tags:
-  analytics_story: Apache Struts Vulnerability
+  analytic_story: Apache Struts Vulnerability
   category:
   - Vulnerability
   product:
@@ -108,5 +110,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/asset_tracking.yml b/stories/asset_tracking.yml
index 7cc4be14fe..29b5fa430d 100644
--- a/stories/asset_tracking.yml
+++ b/stories/asset_tracking.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Asset Tracking
+id: 91c676cf-0b23-438d-abee-f6335e1fce77
+version: 1
 date: '2017-09-13'
+author: Bhavin Patel, Splunk
+type: batch
 description: Keep a careful inventory of every asset on your network to make it easier
   to detect rogue devices. Unauthorized/unmanaged devices could be an indication of
   malicious behavior that should be investigated further.
-id: 91c676cf-0b23-438d-abee-f6335e1fce77
-name: Asset Tracking
 narrative: This Analytic Story is designed to help you develop a better understanding
   of what authorized and unauthorized devices are part of your enterprise. This story
   can help you better categorize and classify assets, providing critical business
@@ -15,7 +17,7 @@ narrative: This Analytic Story is designed to help you develop a better understa
 references:
 - https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/
 tags:
-  analytics_story: Asset Tracking
+  analytic_story: Asset Tracking
   category:
   - Best Practices
   product:
@@ -23,5 +25,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/aws_cross_account_activity.yml b/stories/aws_cross_account_activity.yml
index f451386399..330ee1e9d3 100644
--- a/stories/aws_cross_account_activity.yml
+++ b/stories/aws_cross_account_activity.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: AWS Cross Account Activity
+id: 2f2f610a-d64d-48c2-b57c-967a2b49ab5a
+version: 1
 date: '2018-06-04'
+author: David Dorsey, Splunk
+type: batch
 description: Track when a user assumes an IAM role in another AWS account to obtain
   cross-account access to services and resources in that account. Accessing new roles
   could be an indication of malicious activity.
-id: 2f2f610a-d64d-48c2-b57c-967a2b49ab5a
-name: AWS Cross Account Activity
 narrative: 'Amazon Web Services (AWS) admins manage access to AWS resources and services
   across the enterprise using AWS''s Identity and Access Management (IAM) functionality.
   IAM provides the ability to create and manage AWS users, groups, and roles-each
@@ -28,7 +30,7 @@ narrative: 'Amazon Web Services (AWS) admins manage access to AWS resources and
 references:
 - https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/
 tags:
-  analytics_story: AWS Cross Account Activity
+  analytic_story: AWS Cross Account Activity
   category:
   - Cloud Security
   product:
@@ -36,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/aws_cryptomining.yml b/stories/aws_cryptomining.yml
index 3e003d7fb8..9918332ce9 100644
--- a/stories/aws_cryptomining.yml
+++ b/stories/aws_cryptomining.yml
@@ -1,11 +1,13 @@
-author: David Dorsey, Splunk
+name: AWS Cryptomining
+id: ced74200-8465-4bc3-bd2c-9a782eec6750
+version: 1
 date: '2018-03-08'
+author: David Dorsey, Splunk
+type: batch
 description: Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining.
   New instances that originate from previously unseen regions, users who launch abnormally
   high numbers of instances, or EC2 instances started by previously unseen users are
   just a few examples of potentially malicious behavior.
-id: ced74200-8465-4bc3-bd2c-9a782eec6750
-name: AWS Cryptomining
 narrative: 'Cryptomining is an intentionally difficult, resource-intensive business.
   Its complexity was designed into the process to ensure that the number of blocks
   mined each day would remain steady. So, it''s par for the course that ambitious,
@@ -33,7 +35,7 @@ narrative: 'Cryptomining is an intentionally difficult, resource-intensive busin
 references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 tags:
-  analytics_story: AWS Cryptomining
+  analytic_story: AWS Cryptomining
   category:
   - Cloud Security
   product:
@@ -41,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/aws_network_acl_activity.yml b/stories/aws_network_acl_activity.yml
index 9bb0c00104..3c57640b70 100644
--- a/stories/aws_network_acl_activity.yml
+++ b/stories/aws_network_acl_activity.yml
@@ -1,9 +1,11 @@
-author: Bhavin Patel, Splunk
+name: AWS Network ACL Activity
+id: 2e8948a5-5239-406b-b56b-6c50ff268af4
+version: 2
 date: '2018-05-21'
+author: Bhavin Patel, Splunk
+type: batch
 description: Monitor your AWS network infrastructure for bad configurations and malicious
   activity. Investigative searches help you probe deeper, when the facts warrant it.
-id: 2e8948a5-5239-406b-b56b-6c50ff268af4
-name: AWS Network ACL Activity
 narrative: AWS CloudTrail is an AWS service that helps you enable governance, compliance,
   and operational/risk auditing of your AWS account. Actions taken by a user, role,
   or an AWS service are recorded as events in CloudTrail. It is crucial for a company
@@ -16,7 +18,7 @@ references:
 - https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html
 - https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/
 tags:
-  analytics_story: AWS Network ACL Activity
+  analytic_story: AWS Network ACL Activity
   category:
   - Cloud Security
   product:
@@ -24,5 +26,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 2
diff --git a/stories/aws_security_hub_alerts.yml b/stories/aws_security_hub_alerts.yml
index 683c138643..bb176264b0 100644
--- a/stories/aws_security_hub_alerts.yml
+++ b/stories/aws_security_hub_alerts.yml
@@ -1,9 +1,11 @@
-author: Bhavin Patel, Splunk
+name: AWS Security Hub Alerts
+id: 2f2f610a-d64d-48c2-b57c-96722b49ab5a
+version: 1
 date: '2020-08-04'
+author: Bhavin Patel, Splunk
+type: batch
 description: This story is focused around detecting Security Hub alerts generated
   from AWS
-id: 2f2f610a-d64d-48c2-b57c-96722b49ab5a
-name: AWS Security Hub Alerts
 narrative: AWS Security Hub collects and consolidates findings from AWS security services
   enabled in your environment, such as intrusion detection findings from Amazon GuardDuty,
   vulnerability scans from Amazon Inspector, S3 bucket policy findings from Amazon
@@ -12,7 +14,7 @@ narrative: AWS Security Hub collects and consolidates findings from AWS security
 references:
 - https://aws.amazon.com/security-hub/features/
 tags:
-  analytics_story: AWS Security Hub Alerts
+  analytic_story: AWS Security Hub Alerts
   category:
   - Cloud Security
   product:
@@ -20,5 +22,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/aws_suspicious_provisioning_activities.yml b/stories/aws_suspicious_provisioning_activities.yml
index 336f5e705f..509a1fe352 100644
--- a/stories/aws_suspicious_provisioning_activities.yml
+++ b/stories/aws_suspicious_provisioning_activities.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: AWS Suspicious Provisioning Activities
+id: 3338b567-3804-4261-9889-cf0ca4753c7f
+version: 1
 date: '2018-03-16'
+author: David Dorsey, Splunk
+type: batch
 description: Monitor your AWS provisioning activities for behaviors originating from
   unfamiliar or unusual locations. These behaviors may indicate that malicious activities
   are occurring somewhere within your network.
-id: 3338b567-3804-4261-9889-cf0ca4753c7f
-name: AWS Suspicious Provisioning Activities
 narrative: 'Because most enterprise AWS activities originate from familiar geographic
   locations, monitoring for activity from unknown or unusual regions is an important
   security measure. This indicator can be especially useful in environments where
@@ -22,7 +24,7 @@ narrative: 'Because most enterprise AWS activities originate from familiar geogr
 references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 tags:
-  analytics_story: AWS Suspicious Provisioning Activities
+  analytic_story: AWS Suspicious Provisioning Activities
   category:
   - Cloud Security
   product:
@@ -30,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/aws_user_monitoring.yml b/stories/aws_user_monitoring.yml
index da49aa0e41..b5ca874a7a 100644
--- a/stories/aws_user_monitoring.yml
+++ b/stories/aws_user_monitoring.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: AWS User Monitoring
+id: 2e8948a5-5239-406b-b56b-6c50f1269af3
+version: 1
 date: '2018-03-12'
+author: Bhavin Patel, Splunk
+type: batch
 description: Detect and investigate dormant user accounts for your AWS environment
   that have become active again. Because inactive and ad-hoc accounts are common attack
   targets, it's critical to enable governance within your environment.
-id: 2e8948a5-5239-406b-b56b-6c50f1269af3
-name: AWS User Monitoring
 narrative: 'It seems obvious that it is critical to monitor and control the users
   who have access to your cloud infrastructure. Nevertheless, it''s all too common
   for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable
@@ -29,7 +31,7 @@ references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 - https://redlock.io/blog/cryptojacking-tesla
 tags:
-  analytics_story: AWS User Monitoring
+  analytic_story: AWS User Monitoring
   category:
   - Cloud Security
   product:
@@ -37,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/baron_samedit_cve_2021_3156.yml b/stories/baron_samedit_cve_2021_3156.yml
index 5c0e985202..d66edc4d0e 100644
--- a/stories/baron_samedit_cve_2021_3156.yml
+++ b/stories/baron_samedit_cve_2021_3156.yml
@@ -1,13 +1,15 @@
-author: Shannon Davis, Splunk
+name: Baron Samedit CVE-2021-3156
+id: 817b0dfc-23ba-4bcc-96cc-2cb77e428fbe
+version: 1
 date: '2021-01-27'
+author: Shannon Davis, Splunk
+type: batch
 description: Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys
   Research Team, this vulnerability has been found to affect sudo across multiple
   Linux distributions (Ubuntu 20.04 and prior, Debian 10 and prior, Fedora 33 and
   prior). As this vulnerability was committed to code in July 2011, there will be
   many distributions affected. Successful exploitation of this vulnerability allows
   any unprivileged user to gain root privileges on the vulnerable host.
-id: 817b0dfc-23ba-4bcc-96cc-2cb77e428fbe
-name: Baron Samedit CVE-2021-3156
 narrative: A non-privledged user is able to execute the sudoedit command to trigger
   a buffer overflow. After the successful buffer overflow, they are then able to gain
   root privileges on the affected host. The conditions needed to be run are a trailing
@@ -17,7 +19,7 @@ narrative: A non-privledged user is able to execute the sudoedit command to trig
 references:
 - https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit
 tags:
-  analytics_story: Baron Samedit CVE-2021-3156
+  analytic_story: Baron Samedit CVE-2021-3156
   category:
   - Adversary Tactics
   product:
@@ -25,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/brand_monitoring.yml b/stories/brand_monitoring.yml
index 299d55e2e6..e6554d0edf 100644
--- a/stories/brand_monitoring.yml
+++ b/stories/brand_monitoring.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Brand Monitoring
+id: 91c676cf-0b23-438d-abee-f6335e1fce78
+version: 1
 date: '2017-12-19'
+author: David Dorsey, Splunk
+type: batch
 description: Detect and investigate activity that may indicate that an adversary is
   using faux domains to mislead users into interacting with malicious infrastructure.
   Monitor DNS, email, and web traffic for permutations of your brand name.
-id: 91c676cf-0b23-438d-abee-f6335e1fce78
-name: Brand Monitoring
 narrative: 'While you can educate your users and customers about the risks and threats
   posed by typosquatting, phishing, and corporate espionage, human error is a persistent
   fact of life. Of course, your adversaries are all too aware of this reality and
@@ -28,7 +30,7 @@ references:
 - https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/
 - https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/
 tags:
-  analytics_story: Brand Monitoring
+  analytic_story: Brand Monitoring
   category:
   - Abuse
   product:
@@ -36,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/cloud_cryptomining.yml b/stories/cloud_cryptomining.yml
index 77ea0cdce1..9c1c197a1d 100644
--- a/stories/cloud_cryptomining.yml
+++ b/stories/cloud_cryptomining.yml
@@ -1,11 +1,13 @@
-author: David Dorsey, Splunk
+name: Cloud Cryptomining
+id: 3b96d13c-fdc7-45dd-b3ad-c132b31cdd2a
+version: 1
 date: '2019-10-02'
+author: David Dorsey, Splunk
+type: batch
 description: Monitor your cloud compute instances for activities related to cryptojacking/cryptomining.
   New instances that originate from previously unseen regions, users who launch abnormally
   high numbers of instances, or compute instances started by previously unseen users
   are just a few examples of potentially malicious behavior.
-id: 3b96d13c-fdc7-45dd-b3ad-c132b31cdd2a
-name: Cloud Cryptomining
 narrative: 'Cryptomining is an intentionally difficult, resource-intensive business.
   Its complexity was designed into the process to ensure that the number of blocks
   mined each day would remain steady. So, it''s par for the course that ambitious,
@@ -33,7 +35,7 @@ narrative: 'Cryptomining is an intentionally difficult, resource-intensive busin
 references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 tags:
-  analytics_story: Cloud Cryptomining
+  analytic_story: Cloud Cryptomining
   category:
   - Cloud Security
   product:
@@ -41,5 +43,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/cloud_federated_credential_abuse.yml b/stories/cloud_federated_credential_abuse.yml
index 97ccec4b9d..73cff1e571 100644
--- a/stories/cloud_federated_credential_abuse.yml
+++ b/stories/cloud_federated_credential_abuse.yml
@@ -2,24 +2,30 @@ name: Cloud Federated Credential Abuse
 id: cecdc1e7-0af2-4a55-8967-b9ea62c0317d
 version: 1
 date: '2021-01-26'
-description: This analytical story addresses events that indicate abuse of cloud federated credentials.
- These credentials are usually extracted from endpoint desktop or servers specially those
- servers that provide federation services such as Windows Active Directory Federation Services.
- Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order
- to provide seamless access between cloud and perimeter environments. If these objects are either
- hijacked or forged then attackers will be able to pivot into victim's cloud environements.
-narrative: This story is composed of detection searches based on endpoint that addresses the use of
- Mimikatz, Escalation of Privileges and Abnormal processes that may indicate the extraction of Federated
- directory objects such as passwords, Oauth2 tokens, certificates and keys. Cloud environment (AWS, Azure)
- related events are also addressed in specific cloud environment detection searches.
 author: Rod Soto, Splunk
-type: ESCU
+type: batch
+description: This analytical story addresses events that indicate abuse of cloud federated
+  credentials. These credentials are usually extracted from endpoint desktop or servers
+  specially those servers that provide federation services such as Windows Active
+  Directory Federation Services. Identity Federation relies on objects such as Oauth2
+  tokens, cookies or SAML assertions in order to provide seamless access between cloud
+  and perimeter environments. If these objects are either hijacked or forged then
+  attackers will be able to pivot into victim's cloud environements.
+narrative: This story is composed of detection searches based on endpoint that addresses
+  the use of Mimikatz, Escalation of Privileges and Abnormal processes that may indicate
+  the extraction of Federated directory objects such as passwords, Oauth2 tokens,
+  certificates and keys. Cloud environment (AWS, Azure) related events are also addressed
+  in specific cloud environment detection searches.
 references:
 - https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps
 - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
 - https://us-cert.cisa.gov/ncas/alerts/aa21-008a
 tags:
   analytics_story: Cloud Federated Credential Abuse
-  usecase: Security Monitoring
   category:
   - Cloud Security
+  usecase: Security Monitoring
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/stories/coldroot_macos_rat.yml b/stories/coldroot_macos_rat.yml
index 2eb09048ad..b1ea322c59 100644
--- a/stories/coldroot_macos_rat.yml
+++ b/stories/coldroot_macos_rat.yml
@@ -1,12 +1,14 @@
-author: Jose Hernandez, Splunk
+name: ColdRoot MacOS RAT
+id: bd91a2bc-d20b-4f44-a982-1bea98e86390
+version: 1
 date: '2019-01-09'
+author: Jose Hernandez, Splunk
+type: batch
 description: Leverage searches that allow you to detect and investigate unusual activities
   that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example
   of some of these activities are changing sensative binaries in the MacOS sub-system,
   detecting process names and executables associated with the RAT, detecting when
   a keyboard tab is installed on a MacOS machine and more.
-id: bd91a2bc-d20b-4f44-a982-1bea98e86390
-name: ColdRoot MacOS RAT
 narrative: 'Conventional wisdom holds that Apple''s MacOS operating system is significantly
   less vulnerable to attack than Windows machines. While that point is debatable,
   it is true that attacks against MacOS systems are much less common. However, this
@@ -36,7 +38,7 @@ references:
 - https://objective-see.com/blog/blog_0x2A.html
 - https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/
 tags:
-  analytics_story: ColdRoot MacOS RAT
+  analytic_story: ColdRoot MacOS RAT
   category:
   - Malware
   product:
@@ -44,5 +46,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/collection_and_staging.yml b/stories/collection_and_staging.yml
index 3b3cdb374c..3557fd8bb6 100644
--- a/stories/collection_and_staging.yml
+++ b/stories/collection_and_staging.yml
@@ -1,11 +1,13 @@
-author: Rico Valdez, Splunk
+name: Collection and Staging
+id: 8e03c61e-13c4-4dcd-bfbe-5ce5a8dc031a
+version: 1
 date: '2020-02-03'
+author: Rico Valdez, Splunk
+type: batch
 description: 'Monitor for and investigate activities--such as suspicious writes to
   the Windows Recycling Bin or email servers sending high amounts of traffic to specific
   hosts, for example--that may indicate that an adversary is harvesting and exfiltrating
   sensitive data. '
-id: 8e03c61e-13c4-4dcd-bfbe-5ce5a8dc031a
-name: Collection and Staging
 narrative: "A common adversary goal is to identify and exfiltrate data of value from\
   \ a target organization. This data may include email conversations and addresses,\
   \ confidential company information, links to network design/infrastructure, important\
@@ -21,7 +23,7 @@ references:
 - https://attack.mitre.org/wiki/Collection
 - https://attack.mitre.org/wiki/Technique/T1074
 tags:
-  analytics_story: Collection and Staging
+  analytic_story: Collection and Staging
   category:
   - Adversary Tactics
   product:
@@ -29,5 +31,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/command_and_control.yml b/stories/command_and_control.yml
index f7669993e5..cf7241d118 100644
--- a/stories/command_and_control.yml
+++ b/stories/command_and_control.yml
@@ -1,11 +1,13 @@
-author: Rico Valdez, Splunk
+name: Command and Control
+id: 943773c6-c4de-4f38-89a8-0b92f98804d8
+version: 1
 date: '2018-06-01'
+author: Rico Valdez, Splunk
+type: batch
 description: Detect and investigate tactics, techniques, and procedures leveraged
   by attackers to establish and operate command and control channels. Implants installed
   by attackers on compromised endpoints use these channels to receive instructions
   and send data back to the malicious operators.
-id: 943773c6-c4de-4f38-89a8-0b92f98804d8
-name: Command and Control
 narrative: 'Threat actors typically architect and implement an infrastructure to use
   in various ways during the course of their attack campaigns. In some cases, they
   leverage this infrastructure for scanning and performing reconnaissance activities.
@@ -26,7 +28,7 @@ references:
 - https://attack.mitre.org/wiki/Command_and_Control
 - https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware
 tags:
-  analytics_story: Command and Control
+  analytic_story: Command and Control
   category:
   - Adversary Tactics
   product:
@@ -34,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/common_phishing_frameworks.yml b/stories/common_phishing_frameworks.yml
index 1244132f11..b103fa3d8e 100644
--- a/stories/common_phishing_frameworks.yml
+++ b/stories/common_phishing_frameworks.yml
@@ -1,10 +1,12 @@
-author: Splunk Research Team, Splunk
+name: Common Phishing Frameworks
+id: 9a64ab44-9214-4639-8163-7eaa2621bd61
+version: 1
 date: '2019-04-29'
+author: Splunk Research Team, Splunk
+type: batch
 description: 'Detect DNS and web requests to fake websites generated by the EvilGinx2
   toolkit. These websites are designed to fool unwitting users who have clicked on
   a malicious link in a phishing email. '
-id: 9a64ab44-9214-4639-8163-7eaa2621bd61
-name: Common Phishing Frameworks
 narrative: 'As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/),
   familiar contact names inserted as senders, and other tactics to lure targets into
   clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/),
@@ -27,7 +29,7 @@ references:
 - https://attack.mitre.org/techniques/T1192/
 - https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/
 tags:
-  analytics_story: Common Phishing Frameworks
+  analytic_story: Common Phishing Frameworks
   category:
   - Adversary Tactics
   product:
@@ -35,5 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/container_implantation_monitoring_and_investigation.yml b/stories/container_implantation_monitoring_and_investigation.yml
index a6f4e8d1aa..23abd5d6ae 100644
--- a/stories/container_implantation_monitoring_and_investigation.yml
+++ b/stories/container_implantation_monitoring_and_investigation.yml
@@ -1,5 +1,9 @@
-author: Rod Soto, Rico Valdez, Splunk
+name: Container Implantation Monitoring and Investigation
+id: aa0e28b1-0521-4b6f-9d2a-7b87e34af246
+version: 1
 date: '2020-02-20'
+author: Rod Soto, Rico Valdez, Splunk
+type: batch
 description: Use the searches in this story to monitor your Kubernetes registry repositories
   for upload, and deployment of potentially vulnerable, backdoor, or implanted containers.
   These searches provide information on source users, destination path, container
@@ -7,8 +11,6 @@ description: Use the searches in this story to monitor your Kubernetes registry
   which refers to container implantation upload to a company's repository either in
   Amazon Elastic Container Registry, Google Container Registry and Azure Container
   Registry.
-id: aa0e28b1-0521-4b6f-9d2a-7b87e34af246
-name: Container Implantation Monitoring and Investigation
 narrative: Container Registrys provide a way for organizations to keep customized
   images of their development and infrastructure environment in private. However if
   these repositories are misconfigured or priviledge users credentials are compromise,
@@ -18,7 +20,7 @@ narrative: Container Registrys provide a way for organizations to keep customize
 references:
 - https://github.com/splunk/cloud-datamodel-security-research
 tags:
-  analytics_story: Container Implantation Monitoring and Investigation
+  analytic_story: Container Implantation Monitoring and Investigation
   category:
   - Cloud Security
   product:
@@ -26,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/credential_dumping.yml b/stories/credential_dumping.yml
index 440710e2a2..a78c4523b4 100644
--- a/stories/credential_dumping.yml
+++ b/stories/credential_dumping.yml
@@ -1,12 +1,14 @@
-author: Rico Valdez, Splunk
+name: Credential Dumping
+id: 854d78bf-d0e2-4f4e-b05c-640905f86d7a
+version: 3
 date: '2020-02-04'
+author: Rico Valdez, Splunk
+type: batch
 description: Uncover activity consistent with credential dumping, a technique wherein
   attackers compromise systems and attempt to obtain and exfiltrate passwords. The
   threat actors use these pilfered credentials to further escalate privileges and
   spread throughout a target environment. The included searches in this Analytic Story
   are designed to identify attempts to credential dumping.
-id: 854d78bf-d0e2-4f4e-b05c-640905f86d7a
-name: Credential Dumping
 narrative: 'Credential dumping&#151;gathering credentials from a target system, often
   hashed or encrypted&#151;is a common attack technique. Even though the credentials
   may not be in plain text, an attacker can still exfiltrate the data and set to cracking
@@ -26,7 +28,7 @@ references:
 - https://attack.mitre.org/wiki/Technique/T1003
 - https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html
 tags:
-  analytics_story: Credential Dumping
+  analytic_story: Credential Dumping
   category:
   - Adversary Tactics
   product:
@@ -34,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 3
diff --git a/stories/data_exfiltration.yml b/stories/data_exfiltration.yml
index a6e7e50ef8..e97d584d9f 100644
--- a/stories/data_exfiltration.yml
+++ b/stories/data_exfiltration.yml
@@ -1,8 +1,10 @@
-author: Shannon Davis, Splunk
-date: '2020-10-21'
-description: The stealing of data by an adversary.
-id: 66b0fe0c-1351-11eb-adc1-0242ac120002
 name: Data Exfiltration
+id: 66b0fe0c-1351-11eb-adc1-0242ac120002
+version: 1
+date: '2020-10-21'
+author: Shannon Davis, Splunk
+type: batch
+description: The stealing of data by an adversary.
 narrative: Exfiltration comes in many flavors.  Adversaries can collect data over
   encrypted or non-encrypted channels.  They can utilise Command and Control channels
   that are already in place to exfiltrate data.  They can use both standard data transfer
@@ -12,7 +14,7 @@ narrative: Exfiltration comes in many flavors.  Adversaries can collect data ove
 references:
 - https://attack.mitre.org/tactics/TA0010/
 tags:
-  analytics_story: Data Exfiltration
+  analytic_story: Data Exfiltration
   category:
   - Adversary Tactics
   product:
@@ -20,5 +22,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/data_protection.yml b/stories/data_protection.yml
index e479cc4f14..fdac1f2174 100644
--- a/stories/data_protection.yml
+++ b/stories/data_protection.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Data Protection
+id: 91c676cf-0b23-438d-abee-f6335e1fce33
+version: 1
 date: '2017-09-14'
+author: Bhavin Patel, Splunk
+type: batch
 description: Fortify your data-protection arsenal--while continuing to ensure data
   confidentiality and integrity--with searches that monitor for and help you investigate
   possible signs of data exfiltration.
-id: 91c676cf-0b23-438d-abee-f6335e1fce33
-name: Data Protection
 narrative: Attackers can leverage a variety of resources to compromise or exfiltrate
   enterprise data. Common exfiltration techniques include remote-access channels via
   low-risk, high-payoff active-collections operations and close-access operations
@@ -16,7 +18,7 @@ references:
 - https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022
 - https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/
 tags:
-  analytics_story: Data Protection
+  analytic_story: Data Protection
   category:
   - Abuse
   product:
@@ -24,5 +26,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/detect_zerologon_attack.yml b/stories/detect_zerologon_attack.yml
index 8ad70b7360..0d4c72e47b 100644
--- a/stories/detect_zerologon_attack.yml
+++ b/stories/detect_zerologon_attack.yml
@@ -1,5 +1,9 @@
-author: Rod Soto, Jose Hernandez, Stan Miskowicz, David Dorsey, Shannon Davis Splunk
+name: Detect Zerologon Attack
+id: 5d14a962-569e-4578-939f-f386feb63ce4
+version: 1
 date: '2020-09-18'
+author: Rod Soto, Jose Hernandez, Stan Miskowicz, David Dorsey, Shannon Davis Splunk
+type: batch
 description: Uncover activity related to the execution of Zerologon CVE-2020-11472,
   a technique wherein attackers target a Microsoft Windows Domain Controller to reset
   its computer account password. The result from this attack is attackers can now
@@ -7,8 +11,6 @@ description: Uncover activity related to the execution of Zerologon CVE-2020-114
   searches in this Analytic Story are designed to identify attempts to reset Domain
   Controller Computer Account via exploit code remotely or via the use of tool Mimikatz
   as payload carrier.
-id: 5d14a962-569e-4578-939f-f386feb63ce4
-name: Detect Zerologon Attack
 narrative: This attack is a privilege escalation technique, where attacker targets
   a Netlogon secure channel connection to a domain controller, using Netlogon Remote
   Protocol (MS-NRPC). This vulnerability exposes vulnerable Windows Domain Controllers
@@ -26,7 +28,7 @@ references:
 - https://www.secura.com/blog/zero-logon
 - https://nvd.nist.gov/vuln/detail/CVE-2020-1472
 tags:
-  analytics_story: Detect Zerologon Attack
+  analytic_story: Detect Zerologon Attack
   category:
   - Adversary Tactics
   product:
@@ -34,5 +36,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/dhs_report_ta18_074a.yml b/stories/dhs_report_ta18_074a.yml
index 036555683d..012190d978 100644
--- a/stories/dhs_report_ta18_074a.yml
+++ b/stories/dhs_report_ta18_074a.yml
@@ -1,10 +1,12 @@
-author: Rico Valdez, Splunk
+name: DHS Report TA18-074A
+id: 0c016e5c-88be-4e2c-8c6c-c2b55b4fb4ef
+version: 2
 date: '2020-01-22'
+author: Rico Valdez, Splunk
+type: batch
 description: Monitor for suspicious activities associated with DHS Technical Alert
   US-CERT TA18-074A. Some of the activities that adversaries used in these compromises
   included spearfishing attacks, malware, watering-hole domains, many and more.
-id: 0c016e5c-88be-4e2c-8c6c-c2b55b4fb4ef
-name: DHS Report TA18-074A
 narrative: 'The frequency of nation-state cyber attacks has increased significantly
   over the last decade. Employing numerous tactics and techniques, these attacks continue
   to escalate in complexity. \
@@ -30,7 +32,7 @@ narrative: 'The frequency of nation-state cyber attacks has increased significan
 references:
 - https://www.us-cert.gov/ncas/alerts/TA18-074A
 tags:
-  analytics_story: DHS Report TA18-074A
+  analytic_story: DHS Report TA18-074A
   category:
   - Malware
   product:
@@ -38,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/disabling_security_tools.yml b/stories/disabling_security_tools.yml
index 3c10bfcf6f..aebcad8523 100644
--- a/stories/disabling_security_tools.yml
+++ b/stories/disabling_security_tools.yml
@@ -1,10 +1,12 @@
-author: Rico Valdez, Splunk
+name: Disabling Security Tools
+id: fcc27099-46a0-46b0-a271-5c7dab56b6f1
+version: 2
 date: '2020-02-04'
+author: Rico Valdez, Splunk
+type: batch
 description: Looks for activities and techniques associated with the disabling of
   security tools on a Windows system, such as suspicious `reg.exe` processes, processes
   launching netsh, and many others.
-id: fcc27099-46a0-46b0-a271-5c7dab56b6f1
-name: Disabling Security Tools
 narrative: Attackers employ a variety of tactics in order to avoid detection and operate
   without barriers. This often involves modifying the configuration of security tools
   to get around them or explicitly disabling them to prevent them from running. This
@@ -20,7 +22,7 @@ references:
 - https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/
 - https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf
 tags:
-  analytics_story: Disabling Security Tools
+  analytic_story: Disabling Security Tools
   category:
   - Adversary Tactics
   product:
@@ -28,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 2
diff --git a/stories/dns_amplification_attacks.yml b/stories/dns_amplification_attacks.yml
index 9172bbf597..23bdb70021 100644
--- a/stories/dns_amplification_attacks.yml
+++ b/stories/dns_amplification_attacks.yml
@@ -1,11 +1,13 @@
-author: Bhavin Patel, Splunk
+name: DNS Amplification Attacks
+id: e8afd39e-3294-11e6-b39d-a45e60c6700
+version: 1
 date: '2016-09-13'
+author: Bhavin Patel, Splunk
+type: batch
 description: DNS poses a serious threat as a Denial of Service (DOS) amplifier, if
   it responds to `ANY` queries. This Analytic Story can help you detect attackers
   who may be abusing your company's DNS infrastructure to launch amplification attacks,
   causing Denial of Service to other victims.
-id: e8afd39e-3294-11e6-b39d-a45e60c6700
-name: DNS Amplification Attacks
 narrative: 'The Domain Name System (DNS) is the protocol used to map domain names
   to IP addresses. It has been proven to work very well for its intended function.
   However if DNS is misconfigured, servers can be abused by attackers to levy amplification
@@ -27,7 +29,7 @@ references:
 - https://www.us-cert.gov/ncas/alerts/TA13-088A
 - https://www.imperva.com/learn/application-security/dns-amplification/
 tags:
-  analytics_story: DNS Amplification Attacks
+  analytic_story: DNS Amplification Attacks
   category:
   - Abuse
   product:
@@ -35,5 +37,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/dns_hijacking.yml b/stories/dns_hijacking.yml
index b42f767ec6..f40dcee71c 100644
--- a/stories/dns_hijacking.yml
+++ b/stories/dns_hijacking.yml
@@ -1,9 +1,11 @@
-author: Bhavin Patel, Splunk
+name: DNS Hijacking
+id: 8169f17b-ef68-4b59-aa28-586907301221
+version: 1
 date: '2020-02-04'
+author: Bhavin Patel, Splunk
+type: batch
 description: Secure your environment against DNS hijacks with searches that help you
   detect and investigate unauthorized changes to DNS records.
-id: 8169f17b-ef68-4b59-aa28-586907301221
-name: DNS Hijacking
 narrative: 'Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613),
   DNS plays a critical role in routing web traffic but is notoriously vulnerable to
   attack. One reason is its distributed nature. It relies on unstructured connections
@@ -59,7 +61,7 @@ references:
 - http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/
 - https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html
 tags:
-  analytics_story: DNS Hijacking
+  analytic_story: DNS Hijacking
   category:
   - Adversary Tactics
   product:
@@ -67,5 +69,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/dynamic_dns.yml b/stories/dynamic_dns.yml
index 24eacb4e18..f8e0211fda 100644
--- a/stories/dynamic_dns.yml
+++ b/stories/dynamic_dns.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Dynamic DNS
+id: 8169f17b-ef68-4b59-aae8-586907301221
+version: 2
 date: '2018-09-06'
+author: Bhavin Patel, Splunk
+type: batch
 description: Detect and investigate hosts in your environment that may be communicating
   with dynamic domain providers. Attackers may leverage these services to help them
   avoid firewall blocks and deny lists.
-id: 8169f17b-ef68-4b59-aae8-586907301221
-name: Dynamic DNS
 narrative: Dynamic DNS services (DDNS) are legitimate low-cost or free services that
   allow users to rapidly update domain resolutions to IP infrastructure. While their
   usage can be benign, malicious actors can abuse DDNS to host harmful payloads or
@@ -22,7 +24,7 @@ references:
 - http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/
 - https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html
 tags:
-  analytics_story: Dynamic DNS
+  analytic_story: Dynamic DNS
   category:
   - Malware
   product:
@@ -30,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 2
diff --git a/stories/emotet_malware__dhs_report_ta18_201a_.yml b/stories/emotet_malware__dhs_report_ta18_201a_.yml
index 77f91f5807..48163971c5 100644
--- a/stories/emotet_malware__dhs_report_ta18_201a_.yml
+++ b/stories/emotet_malware__dhs_report_ta18_201a_.yml
@@ -1,11 +1,13 @@
-author: Bhavin Patel, Splunk
+name: 'Emotet Malware  DHS Report TA18-201A '
+id: bb9f5ed2-916e-4364-bb6d-91c310efcf52
+version: 1
 date: '2020-01-27'
+author: Bhavin Patel, Splunk
+type: batch
 description: Detect rarely used executables, specific registry paths that may confer
   malware survivability and persistence, instances where cmd.exe is used to launch
   script interpreters, and other indicators that the Emotet financial malware has
   compromised your environment.
-id: bb9f5ed2-916e-4364-bb6d-91c310efcf52
-name: 'Emotet Malware  DHS Report TA18-201A '
 narrative: 'The trojan downloader known as Emotet first surfaced in 2014, when it
   was discovered targeting the banking industry to steal credentials. However, according
   to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A),
@@ -29,7 +31,7 @@ references:
 - https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf
 - https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html
 tags:
-  analytics_story: 'Emotet Malware  DHS Report TA18-201A '
+  analytic_story: 'Emotet Malware  DHS Report TA18-201A '
   category:
   - Malware
   product:
@@ -37,5 +39,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/f5_tmui_rce_cve_2020_5902.yml b/stories/f5_tmui_rce_cve_2020_5902.yml
index 9a44a5e87a..3aa77c4f47 100644
--- a/stories/f5_tmui_rce_cve_2020_5902.yml
+++ b/stories/f5_tmui_rce_cve_2020_5902.yml
@@ -1,13 +1,15 @@
-author: Shannon Davis, Splunk
+name: F5 TMUI RCE CVE-2020-5902
+id: 7678c968-d46e-11ea-87d0-0242ac130003
+version: 1
 date: '2020-08-02'
+author: Shannon Davis, Splunk
+type: batch
 description: Uncover activity consistent with CVE-2020-5902. Discovered by Positive
   Technologies researchers, this vulnerability affects F5 BIG-IP, BIG-IQ. and Traffix
   SDC devices (vulnerable versions in F5 support link below). This vulnerability allows
   unauthenticated users, along with authenticated users, who have access to the configuration
   utility to execute system commands, create/delete files, disable services, and/or
   execute Java code.  This vulnerability can result in full system compromise.
-id: 7678c968-d46e-11ea-87d0-0242ac130003
-name: F5 TMUI RCE CVE-2020-5902
 narrative: A client is able to perform a remote code execution on an exposed and vulnerable
   system. The detection search in this Analytic Story uses syslog to detect the malicious
   behavior. Syslog is going to be the best detection method, as any systems using
@@ -20,7 +22,7 @@ references:
 - https://support.f5.com/csp/article/K52145254
 - https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/
 tags:
-  analytics_story: F5 TMUI RCE CVE-2020-5902
+  analytic_story: F5 TMUI RCE CVE-2020-5902
   category:
   - Adversary Tactics
   product:
@@ -28,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/gcp_cross_account_activity.yml b/stories/gcp_cross_account_activity.yml
index 67ba626620..7e18c2dea7 100644
--- a/stories/gcp_cross_account_activity.yml
+++ b/stories/gcp_cross_account_activity.yml
@@ -1,10 +1,12 @@
-author: Rod Soto, Splunk
+name: GCP Cross Account Activity
+id: 0432039c-ef41-4b03-b157-450c25dad1e6
+version: 1
 date: '2020-09-01'
+author: Rod Soto, Splunk
+type: batch
 description: Track when a user assumes an IAM role in another GCP account to obtain
   cross-account access to services and resources in that account. Accessing new roles
   could be an indication of malicious activity.
-id: 0432039c-ef41-4b03-b157-450c25dad1e6
-name: GCP Cross Account Activity
 narrative: 'Google Cloud Platform (GCP) admins manage access to GCP resources and
   services across the enterprise using GCP Identity and Access Management (IAM) functionality.
   IAM provides the ability to create and manage GCP users, groups, and roles-each
@@ -28,7 +30,7 @@ narrative: 'Google Cloud Platform (GCP) admins manage access to GCP resources an
 references:
 - https://cloud.google.com/iam/docs/understanding-service-accounts
 tags:
-  analytics_story: GCP Cross Account Activity
+  analytic_story: GCP Cross Account Activity
   category:
   - Cloud Security
   product:
@@ -36,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/hidden_cobra_malware.yml b/stories/hidden_cobra_malware.yml
index b746f6a286..89c203de60 100644
--- a/stories/hidden_cobra_malware.yml
+++ b/stories/hidden_cobra_malware.yml
@@ -1,11 +1,13 @@
-author: Rico Valdez, Splunk
+name: Hidden Cobra Malware
+id: baf7580b-d4b4-4774-8173-7d198e9da335
+version: 2
 date: '2020-01-22'
+author: Rico Valdez, Splunk
+type: batch
 description: Monitor for and investigate activities, including the creation or deletion
   of hidden shares and file writes, that may be evidence of infiltration by North
   Korean government-sponsored cybercriminals. Details of this activity were reported
   in DHS Report TA-18-149A.
-id: baf7580b-d4b4-4774-8173-7d198e9da335
-name: Hidden Cobra Malware
 narrative: 'North Korea''s government-sponsored "cyber army" has been slowly building
   momentum and gaining sophistication over the last 15 years or so. As a result, the
   group''s activity, which the US government refers to as "Hidden Cobra," has surreptitiously
@@ -37,7 +39,7 @@ references:
 - https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity
 - https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf
 tags:
-  analytics_story: Hidden Cobra Malware
+  analytic_story: Hidden Cobra Malware
   category:
   - Malware
   product:
@@ -45,5 +47,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/host_redirection.yml b/stories/host_redirection.yml
index e36f237f53..60001712fc 100644
--- a/stories/host_redirection.yml
+++ b/stories/host_redirection.yml
@@ -1,11 +1,13 @@
-author: Rico Valdez, Splunk
+name: Host Redirection
+id: 2e8948a5-5239-406b-b56b-6c50fe268af4
+version: 1
 date: '2017-09-14'
+author: Rico Valdez, Splunk
+type: batch
 description: Detect evidence of tactics used to redirect traffic from a host to a
   destination other than the one intended--potentially one that is part of an adversary's
   attack infrastructure. An example is redirecting communications regarding patches
   and updates or misleading users into visiting a malicious website.
-id: 2e8948a5-5239-406b-b56b-6c50fe268af4
-name: Host Redirection
 narrative: Attackers will often attempt to manipulate client communications for nefarious
   purposes. In some cases, an attacker may endeavor to modify a local host file to
   redirect communications with resources (such as antivirus or system-update services)
@@ -17,7 +19,7 @@ narrative: Attackers will often attempt to manipulate client communications for
 references:
 - https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/
 tags:
-  analytics_story: Host Redirection
+  analytic_story: Host Redirection
   category:
   - Abuse
   product:
@@ -25,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/jboss_vulnerability.yml b/stories/jboss_vulnerability.yml
index 1d48039efb..e68a18c5cb 100644
--- a/stories/jboss_vulnerability.yml
+++ b/stories/jboss_vulnerability.yml
@@ -1,11 +1,13 @@
-author: Bhavin Patel, Splunk
+name: JBoss Vulnerability
+id: 1f5294cb-b85f-4c2d-9c58-ffcf248f52bd
+version: 1
 date: '2017-09-14'
+author: Bhavin Patel, Splunk
+type: batch
 description: In March of 2016, adversaries were seen using JexBoss--an open-source
   utility used for testing and exploiting JBoss application servers. These searches
   help detect evidence of these attacks, such as network connections to external resources
   or web services spawning atypical child processes, among others.
-id: 1f5294cb-b85f-4c2d-9c58-ffcf248f52bd
-name: JBoss Vulnerability
 narrative: 'This Analytic Story looks for probing and exploitation attempts targeting
   JBoss application servers. While the vulnerabilities associated with this story
   are rather dated, they were leveraged in a spring 2016 campaign in connection with
@@ -90,7 +92,7 @@ narrative: 'This Analytic Story looks for probing and exploitation attempts targ
 references:
 - http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html
 tags:
-  analytics_story: JBoss Vulnerability
+  analytic_story: JBoss Vulnerability
   category:
   - Vulnerability
   product:
@@ -98,5 +100,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/kubernetes_scanning_activity.yml b/stories/kubernetes_scanning_activity.yml
index da179646ea..0476024cf5 100644
--- a/stories/kubernetes_scanning_activity.yml
+++ b/stories/kubernetes_scanning_activity.yml
@@ -1,10 +1,12 @@
-author: Rod Soto, Splunk
+name: Kubernetes Scanning Activity
+id: a9ef59cf-e981-4e66-9eef-bb049f695c09
+version: 1
 date: '2020-04-15'
+author: Rod Soto, Splunk
+type: batch
 description: This story addresses detection against Kubernetes cluster fingerprint
   scan and attack by providing information on items such as source ip, user agent,
   cluster names.
-id: a9ef59cf-e981-4e66-9eef-bb049f695c09
-name: Kubernetes Scanning Activity
 narrative: Kubernetes is the most used container orchestration platform, this orchestration
   platform contains sensitve information and management priviledges of production
   workloads, microservices and applications. These searches allow operator to detect
@@ -12,7 +14,7 @@ narrative: Kubernetes is the most used container orchestration platform, this or
 references:
 - https://github.com/splunk/cloud-datamodel-security-research
 tags:
-  analytics_story: Kubernetes Scanning Activity
+  analytic_story: Kubernetes Scanning Activity
   category:
   - Cloud Security
   product:
@@ -20,5 +22,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/kubernetes_sensitive_object_access_activity.yml b/stories/kubernetes_sensitive_object_access_activity.yml
index eb435445b1..911a57fa06 100644
--- a/stories/kubernetes_sensitive_object_access_activity.yml
+++ b/stories/kubernetes_sensitive_object_access_activity.yml
@@ -1,10 +1,12 @@
-author: Rod Soto, Splunk
+name: Kubernetes Sensitive Object Access Activity
+id: 2574e6d9-7254-4751-8925-0447deeec8ea
+version: 1
 date: '2020-05-20'
+author: Rod Soto, Splunk
+type: batch
 description: This story addresses detection and response of accounts acccesing Kubernetes
   cluster sensitive objects such as configmaps or secrets providing information on
   items such as user user, group. object, namespace and authorization reason.
-id: 2574e6d9-7254-4751-8925-0447deeec8ea
-name: Kubernetes Sensitive Object Access Activity
 narrative: Kubernetes is the most used container orchestration platform, this orchestration
   platform contains sensitive objects within its architecture, specifically configmaps
   and secrets, if accessed by an attacker can lead to further compromise. These searches
@@ -12,7 +14,7 @@ narrative: Kubernetes is the most used container orchestration platform, this or
 references:
 - https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html
 tags:
-  analytics_story: Kubernetes Sensitive Object Access Activity
+  analytic_story: Kubernetes Sensitive Object Access Activity
   category:
   - Cloud Security
   product:
@@ -20,5 +22,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/kubernetes_sensitive_role_activity.yml b/stories/kubernetes_sensitive_role_activity.yml
index 9d2d8071cc..57ccb792f3 100644
--- a/stories/kubernetes_sensitive_role_activity.yml
+++ b/stories/kubernetes_sensitive_role_activity.yml
@@ -1,9 +1,11 @@
-author: Rod Soto, Splunk
+name: Kubernetes Sensitive Role Activity
+id: 2574e6d9-7254-4751-8925-0447deeec8ew
+version: 1
 date: '2020-05-20'
+author: Rod Soto, Splunk
+type: batch
 description: This story addresses detection and response around Sensitive Role usage
   within a Kubernetes clusters against cluster resources and namespaces.
-id: 2574e6d9-7254-4751-8925-0447deeec8ew
-name: Kubernetes Sensitive Role Activity
 narrative: Kubernetes is the most used container orchestration platform, this orchestration
   platform contains sensitive roles within its architecture, specifically configmaps
   and secrets, if accessed by an attacker can lead to further compromise. These searches
@@ -11,7 +13,7 @@ narrative: Kubernetes is the most used container orchestration platform, this or
 references:
 - https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html
 tags:
-  analytics_story: Kubernetes Sensitive Role Activity
+  analytic_story: Kubernetes Sensitive Role Activity
   category:
   - Cloud Security
   product:
@@ -19,5 +21,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/lateral_movement.yml b/stories/lateral_movement.yml
index ace138283a..2cc361ff75 100644
--- a/stories/lateral_movement.yml
+++ b/stories/lateral_movement.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Lateral Movement
+id: 399d65dc-1f08-499b-a259-aad9051f38ad
+version: 2
 date: '2020-02-04'
+author: David Dorsey, Splunk
+type: batch
 description: Detect and investigate tactics, techniques, and procedures around how
   attackers move laterally within the enterprise. Because lateral movement can expose
   the adversary to detection, it should be an important focus for security analysts.
-id: 399d65dc-1f08-499b-a259-aad9051f38ad
-name: Lateral Movement
 narrative: "Once attackers gain a foothold within an enterprise, they will seek to\
   \ expand their accesses and leverage techniques that facilitate lateral movement.\
   \ Attackers will often spend quite a bit of time and effort moving laterally. Because\
@@ -32,7 +34,7 @@ narrative: "Once attackers gain a foothold within an enterprise, they will seek
 references:
 - https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html
 tags:
-  analytics_story: Lateral Movement
+  analytic_story: Lateral Movement
   category:
   - Adversary Tactics
   product:
@@ -40,5 +42,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/malicious_powershell.yml b/stories/malicious_powershell.yml
index 7fd4fa18c4..f84830edcf 100644
--- a/stories/malicious_powershell.yml
+++ b/stories/malicious_powershell.yml
@@ -1,11 +1,13 @@
-author: David Dorsey, Splunk
+name: Malicious PowerShell
+id: 2c8ff66e-0b57-42af-8ad7-912438a403fc
+version: 4
 date: '2017-08-23'
+author: David Dorsey, Splunk
+type: batch
 description: Attackers are finding stealthy ways "live off the land," leveraging utilities
   and tools that come standard on the endpoint--such as PowerShell--to achieve their
   goals without downloading binary files. These searches can help you detect and investigate
   PowerShell command-line options that may be indicative of malicious intent.
-id: 2c8ff66e-0b57-42af-8ad7-912438a403fc
-name: Malicious PowerShell
 narrative: 'The searches in this Analytic Story monitor for parameters often used
   for malicious purposes. It is helpful to understand how often the notable events
   generated by this story occur, as well as the commonalities between some of these
@@ -64,7 +66,7 @@ references:
 - https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/
 - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/
 tags:
-  analytics_story: Malicious PowerShell
+  analytic_story: Malicious PowerShell
   category:
   - Adversary Tactics
   product:
@@ -72,5 +74,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 4
diff --git a/stories/monitor_backup_solution.yml b/stories/monitor_backup_solution.yml
index ab533eb352..73dac99808 100644
--- a/stories/monitor_backup_solution.yml
+++ b/stories/monitor_backup_solution.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Monitor Backup Solution
+id: abe807c7-1eb6-4304-ac32-6e7aacdb891d
+version: 1
 date: '2017-09-12'
+author: David Dorsey, Splunk
+type: batch
 description: Address common concerns when monitoring your backup processes. These
   searches can help you reduce risks from ransomware, device theft, or denial of physical
   access to a host by backing up data on endpoints.
-id: abe807c7-1eb6-4304-ac32-6e7aacdb891d
-name: Monitor Backup Solution
 narrative: Having backups is a standard best practice that helps ensure continuity
   of business operations.  Having mature backup processes can also help you reduce
   the risks of many security-related incidents and streamline your response processes.
@@ -15,7 +17,7 @@ narrative: Having backups is a standard best practice that helps ensure continui
 references:
 - https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/
 tags:
-  analytics_story: Monitor Backup Solution
+  analytic_story: Monitor Backup Solution
   category:
   - Best Practices
   product:
@@ -23,5 +25,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Compliance
-type: ESCU
-version: 1
diff --git a/stories/monitor_for_unauthorized_software.yml b/stories/monitor_for_unauthorized_software.yml
index 3bd6d65ea1..a458d94c24 100644
--- a/stories/monitor_for_unauthorized_software.yml
+++ b/stories/monitor_for_unauthorized_software.yml
@@ -1,9 +1,11 @@
-author: David Dorsey, Splunk
+name: Monitor for Unauthorized Software
+id: 8892a655-6205-43f7-abba-06460e38c8ae
+version: 1
 date: '2017-09-15'
+author: David Dorsey, Splunk
+type: batch
 description: 'Identify and investigate prohibited/unauthorized software or processes
   that may be concealing malicious behavior within your environment. '
-id: 8892a655-6205-43f7-abba-06460e38c8ae
-name: Monitor for Unauthorized Software
 narrative: 'It is critical to identify unauthorized software and processes running
   on enterprise endpoints and determine whether they are likely to be malicious. This
   Analytic Story requires the user to populate the Interesting Processes table within
@@ -20,7 +22,7 @@ narrative: 'It is critical to identify unauthorized software and processes runni
 references:
 - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/
 tags:
-  analytics_story: Monitor for Unauthorized Software
+  analytic_story: Monitor for Unauthorized Software
   category:
   - Best Practices
   product:
@@ -28,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Compliance
-type: ESCU
-version: 1
diff --git a/stories/monitor_for_updates.yml b/stories/monitor_for_updates.yml
index 94b622ba33..ba145b98e1 100644
--- a/stories/monitor_for_updates.yml
+++ b/stories/monitor_for_updates.yml
@@ -1,10 +1,12 @@
-author: Rico Valdez, Splunk
+name: Monitor for Updates
+id: 9ef8d677-7b52-4213-a038-99cfc7acc2d8
+version: 1
 date: '2017-09-15'
+author: Rico Valdez, Splunk
+type: batch
 description: Monitor your enterprise to ensure that your endpoints are being patched
   and updated. Adversaries notoriously exploit known vulnerabilities that could be
   mitigated by applying routine security patches.
-id: 9ef8d677-7b52-4213-a038-99cfc7acc2d8
-name: Monitor for Updates
 narrative: 'It is a common best practice to ensure that endpoints are being patched
   and updated in a timely manner, in order to reduce the risk of compromise via a
   publicly disclosed vulnerability. Timely application of updates/patches is important
@@ -21,7 +23,7 @@ narrative: 'It is a common best practice to ensure that endpoints are being patc
 references:
 - https://learn.cisecurity.org/20-controls-download
 tags:
-  analytics_story: Monitor for Updates
+  analytic_story: Monitor for Updates
   category:
   - Best Practices
   product:
@@ -29,5 +31,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Compliance
-type: ESCU
-version: 1
diff --git a/stories/netsh_abuse.yml b/stories/netsh_abuse.yml
index 034fa39c75..316cb9eb52 100644
--- a/stories/netsh_abuse.yml
+++ b/stories/netsh_abuse.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Netsh Abuse
+id: 2b1800dd-92f9-47ec-a981-fdf1351e5f65
+version: 1
 date: '2017-01-05'
+author: Bhavin Patel, Splunk
+type: batch
 description: Detect activities and various techniques associated with the abuse of
   `netsh.exe`, which can disable local firewall settings or set up a remote connection
   to a host from an infected system.
-id: 2b1800dd-92f9-47ec-a981-fdf1351e5f65
-name: Netsh Abuse
 narrative: 'It is a common practice for attackers of all types to leverage native
   Windows tools and functionality to execute commands for malicious reasons. One such
   tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you
@@ -19,7 +21,7 @@ references:
 - https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html
 - http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html
 tags:
-  analytics_story: Netsh Abuse
+  analytic_story: Netsh Abuse
   category:
   - Abuse
   product:
@@ -27,5 +29,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/office_365_detections.yml b/stories/office_365_detections.yml
index 6deb322a70..9b3ab16118 100644
--- a/stories/office_365_detections.yml
+++ b/stories/office_365_detections.yml
@@ -1,15 +1,17 @@
-author: Patrick Bareiss, Splunk
-date: '2020-12-16'
-description: This story is focused around detecting Office 365 Attacks.
-id: 1a51dd71-effc-48b2-abc4-3e9cdb61e5b9
 name: Office 365 Detections
+id: 1a51dd71-effc-48b2-abc4-3e9cdb61e5b9
+version: 1
+date: '2020-12-16'
+author: Patrick Bareiss, Splunk
+type: batch
+description: This story is focused around detecting Office 365 Attacks.
 narrative: More and more companies are using Microsofts Office 365 cloud offering.
   Therefore, we see more and more attacks against Office 365. This story provides
   various detections for Office 365 attacks.
 references:
 - https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf
 tags:
-  analytics_story: Office 365 Detections
+  analytic_story: Office 365 Detections
   category:
   - Cloud Security
   product:
@@ -17,5 +19,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/orangeworm_attack_group.yml b/stories/orangeworm_attack_group.yml
index 7fc6899bdb..2caa4a5fa2 100644
--- a/stories/orangeworm_attack_group.yml
+++ b/stories/orangeworm_attack_group.yml
@@ -1,9 +1,11 @@
-author: David Dorsey, Splunk
+name: Orangeworm Attack Group
+id: bb9f5ed2-916e-4364-bb6d-97c370efcf52
+version: 2
 date: '2020-01-22'
+author: David Dorsey, Splunk
+type: batch
 description: Detect activities and various techniques associated with the Orangeworm
   Attack Group, a group that frequently targets the healthcare industry.
-id: bb9f5ed2-916e-4364-bb6d-97c370efcf52
-name: Orangeworm Attack Group
 narrative: 'In May of 2018, the attack group Orangeworm was implicated for installing
   a custom backdoor called Trojan.Kwampirs within large international healthcare corporations
   in the United States, Europe, and Asia. This malware provides the attackers with
@@ -31,7 +33,7 @@ references:
 - https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia
 - https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/
 tags:
-  analytics_story: Orangeworm Attack Group
+  analytic_story: Orangeworm Attack Group
   category:
   - Malware
   product:
@@ -39,5 +41,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/phishing_payloads.yml b/stories/phishing_payloads.yml
index 5fbf82eb69..bb02015adf 100644
--- a/stories/phishing_payloads.yml
+++ b/stories/phishing_payloads.yml
@@ -1,9 +1,11 @@
-author: Splunk Research Team, Splunk
+name: Phishing Payloads
+id: 57226b40-94f3-4ce5-b101-a75f67759c27
+version: 1
 date: '2019-04-29'
+author: Splunk Research Team, Splunk
+type: batch
 description: Detect signs of malicious payloads that may indicate that your environment
   has been breached via a phishing attack.
-id: 57226b40-94f3-4ce5-b101-a75f67759c27
-name: Phishing Payloads
 narrative: 'Despite its simplicity, phishing remains the most pervasive and dangerous
   cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing)
   are initiated via a phishing email. \
@@ -44,7 +46,7 @@ narrative: 'Despite its simplicity, phishing remains the most pervasive and dang
 references:
 - https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html
 tags:
-  analytics_story: Phishing Payloads
+  analytic_story: Phishing Payloads
   category:
   - Adversary Tactics
   product:
@@ -52,5 +54,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/possible_backdoor_activity_associated_with_mudcarp_espionage_campaigns.yml b/stories/possible_backdoor_activity_associated_with_mudcarp_espionage_campaigns.yml
index b17dfbd3c2..fc38055e4b 100644
--- a/stories/possible_backdoor_activity_associated_with_mudcarp_espionage_campaigns.yml
+++ b/stories/possible_backdoor_activity_associated_with_mudcarp_espionage_campaigns.yml
@@ -1,9 +1,11 @@
-author: iDefense Cyber Espionage Team, iDefense
+name: Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
+id: 988C59C5-0A1C-45B6-A555-0C62276E327E
+version: 1
 date: '2020-01-22'
+author: iDefense Cyber Espionage Team, iDefense
+type: batch
 description: Monitor your environment for suspicious behaviors that resemble the techniques
   employed by the MUDCARP threat group.
-id: 988C59C5-0A1C-45B6-A555-0C62276E327E
-name: Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
 narrative: 'This story was created as a joint effort between iDefense and Splunk.\
 
   iDefense analysts have recently discovered a Windows executable file that, upon
@@ -78,7 +80,7 @@ references:
 - https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/
 - http://blog.amossys.fr/badflick-is-not-so-bad.html
 tags:
-  analytics_story: Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
+  analytic_story: Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
   category:
   - Adversary Tactics
   product:
@@ -86,5 +88,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/prohibited_traffic_allowed_or_protocol_mismatch.yml b/stories/prohibited_traffic_allowed_or_protocol_mismatch.yml
index 9672ffec6d..88c140a3ba 100644
--- a/stories/prohibited_traffic_allowed_or_protocol_mismatch.yml
+++ b/stories/prohibited_traffic_allowed_or_protocol_mismatch.yml
@@ -1,10 +1,12 @@
-author: Rico Valdez, Splunk
+name: Prohibited Traffic Allowed or Protocol Mismatch
+id: 6d13121c-90f3-446d-8ac3-27efbbc65218
+version: 1
 date: '2017-09-11'
+author: Rico Valdez, Splunk
+type: batch
 description: Detect instances of prohibited network traffic allowed in the environment,
   as well as protocols running on non-standard ports. Both of these types of behaviors
   typically violate policy and can be leveraged by attackers.
-id: 6d13121c-90f3-446d-8ac3-27efbbc65218
-name: Prohibited Traffic Allowed or Protocol Mismatch
 narrative: A traditional security best practice is to control the ports, protocols,
   and services allowed within your environment. By limiting the services and protocols
   to those explicitly approved by policy, administrators can minimize the attack surface.
@@ -16,7 +18,7 @@ narrative: A traditional security best practice is to control the ports, protoco
 references:
 - http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/
 tags:
-  analytics_story: Prohibited Traffic Allowed or Protocol Mismatch
+  analytic_story: Prohibited Traffic Allowed or Protocol Mismatch
   category:
   - Best Practices
   product:
@@ -24,5 +26,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/ransomware.yml b/stories/ransomware.yml
index 8f21d68773..d2d5ad8bb4 100644
--- a/stories/ransomware.yml
+++ b/stories/ransomware.yml
@@ -1,11 +1,13 @@
-author: David Dorsey, Splunk
+name: Ransomware
+id: cf309d0d-d4aa-4fbb-963d-1e79febd3756
+version: 1
 date: '2020-02-04'
+author: David Dorsey, Splunk
+type: batch
 description: Leverage searches that allow you to detect and investigate unusual activities
   that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage,
   the presence of common ransomware extensions, and system processes run from unexpected
   locations, and many others.
-id: cf309d0d-d4aa-4fbb-963d-1e79febd3756
-name: Ransomware
 narrative: Ransomware is an ever-present risk to the enterprise, wherein an infected
   host encrypts business-critical data, holding it hostage until the victim pays the
   attacker a ransom. There are many types and varieties of ransomware that can affect
@@ -19,7 +21,7 @@ references:
 - https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/
 - https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html
 tags:
-  analytics_story: Ransomware
+  analytic_story: Ransomware
   category:
   - Malware
   product:
@@ -27,5 +29,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/ransomware_cloud.yml b/stories/ransomware_cloud.yml
index b57a9a660e..e9a2af5f6b 100644
--- a/stories/ransomware_cloud.yml
+++ b/stories/ransomware_cloud.yml
@@ -1,10 +1,12 @@
-author: Rod Soto, David Dorsey, Splunk
+name: Ransomware Cloud
+id: f52f6c43-05f8-4b19-a9d3-5b8c56da91c2
+version: 1
 date: '2020-10-27'
+author: Rod Soto, David Dorsey, Splunk
+type: batch
 description: Leverage searches that allow you to detect and investigate unusual activities
   that might relate to ransomware. These searches include cloud related objects that
   may be targeted by malicious actors via cloud providers own encryption features.
-id: f52f6c43-05f8-4b19-a9d3-5b8c56da91c2
-name: Ransomware Cloud
 narrative: Ransomware is an ever-present risk to the enterprise, wherein an infected
   host encrypts business-critical data, holding it hostage until the victim pays the
   attacker a ransom. There are many types and varieties of ransomware that can affect
@@ -15,7 +17,7 @@ references:
 - https://github.com/d1vious/git-wild-hunt
 - https://www.youtube.com/watch?v=PgzNib37g0M
 tags:
-  analytics_story: Ransomware Cloud
+  analytic_story: Ransomware Cloud
   category:
   - Malware
   product:
@@ -23,5 +25,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/ransomware_ryuk.yml b/stories/ransomware_ryuk.yml
index d76e0cdba0..5fe0d2eb11 100644
--- a/stories/ransomware_ryuk.yml
+++ b/stories/ransomware_ryuk.yml
@@ -1,11 +1,13 @@
-author: Jose Hernandez, Splunk
+name: Ryuk Ransomware
+id: 507edc74-13d5-4339-878e-b9744ded1f35
+version: 1
 date: '2020-11-06'
+author: Jose Hernandez, Splunk
+type: batch
 description: Leverage searches that allow you to detect and investigate unusual activities
   that might relate to the Ryuk ransomware, including looking for file writes associated
   with Ryuk, Stopping Security Access Manager, DisableAntiSpyware registry key modification,
   suspicious psexec use, and more.
-id: 507edc74-13d5-4339-878e-b9744ded1f35
-name: Ryuk Ransomware
 narrative: "Cybersecurity Infrastructure Security Agency (CISA) released Alert (AA20-302A)\
   \ on October 28th called \u201CRansomware Activity Targeting the Healthcare and\
   \ Public Health Sector.\u201D This alert details TTPs associated with ongoing and\
@@ -25,7 +27,7 @@ references:
 - https://www.crowdstrike.com/blog/big-game-hunting-with-ryuk-another-lucrative-targeted-ransomware/
 - https://us-cert.cisa.gov/ncas/alerts/aa20-302a
 tags:
-  analytics_story: Ryuk Ransomware
+  analytic_story: Ryuk Ransomware
   category:
   - Malware
   product:
@@ -33,5 +35,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/ransomware_samsam.yml b/stories/ransomware_samsam.yml
index e80ec4bbc8..bd3542f193 100644
--- a/stories/ransomware_samsam.yml
+++ b/stories/ransomware_samsam.yml
@@ -1,11 +1,13 @@
-author: Rico Valdez, Splunk
+name: SamSam Ransomware
+id: c4b89506-fbcf-4cb7-bfd6-527e54789604
+version: 1
 date: '2018-12-13'
+author: Rico Valdez, Splunk
+type: batch
 description: Leverage searches that allow you to detect and investigate unusual activities
   that might relate to the SamSam ransomware, including looking for file writes associated
   with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware
   extensions, suspicious psexec use, and more.
-id: c4b89506-fbcf-4cb7-bfd6-527e54789604
-name: SamSam Ransomware
 narrative: 'The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt)
   was launched in 2015 by a group of Iranian threat actors. The malicious software
   has affected and continues to affect thousands of victims and has raised almost
@@ -45,7 +47,7 @@ references:
 - https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/
 - https://thehackernews.com/2018/07/samsam-ransomware-attacks.html
 tags:
-  analytics_story: SamSam Ransomware
+  analytic_story: SamSam Ransomware
   category:
   - Malware
   product:
@@ -53,5 +55,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/router_and_infrastructure_security.yml b/stories/router_and_infrastructure_security.yml
index 3feb9cd99a..346b45a98f 100644
--- a/stories/router_and_infrastructure_security.yml
+++ b/stories/router_and_infrastructure_security.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Router and Infrastructure Security
+id: 91c676cf-0b23-438d-abee-f6335e177e77
+version: 1
 date: '2017-09-12'
+author: Bhavin Patel, Splunk
+type: batch
 description: Validate the security configuration of network infrastructure and verify
   that only authorized users and systems are accessing critical assets. Core routing
   and switching infrastructure are common strategic targets for attackers.
-id: 91c676cf-0b23-438d-abee-f6335e177e77
-name: Router and Infrastructure Security
 narrative: 'Networking devices, such as routers and switches, are often overlooked
   as resources that attackers will leverage to subvert an enterprise. Advanced threats
   actors have shown a proclivity to target these critical assets as a means to siphon
@@ -19,7 +21,7 @@ references:
 - https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html
 - https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html
 tags:
-  analytics_story: Router and Infrastructure Security
+  analytic_story: Router and Infrastructure Security
   category:
   - Best Practices
   product:
@@ -27,5 +29,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/spectre_and_meltdown_vulnerabilities.yml b/stories/spectre_and_meltdown_vulnerabilities.yml
index f465066a3e..4ec8887f14 100644
--- a/stories/spectre_and_meltdown_vulnerabilities.yml
+++ b/stories/spectre_and_meltdown_vulnerabilities.yml
@@ -1,9 +1,11 @@
-author: David Dorsey, Splunk
+name: Spectre And Meltdown Vulnerabilities
+id: 6d3306f6-bb2b-4219-8609-8efad64032f2
+version: 1
 date: '2018-01-08'
+author: David Dorsey, Splunk
+type: batch
 description: Assess and mitigate your systems' vulnerability to Spectre and Meltdown
   exploitation with the searches in this Analytic Story.
-id: 6d3306f6-bb2b-4219-8609-8efad64032f2
-name: Spectre And Meltdown Vulnerabilities
 narrative: Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that
   allow unintended access to data in memory. This Analytic Story will help you identify
   the systems can be patched for these vulnerabilities, as well as those that still
@@ -11,7 +13,7 @@ narrative: Meltdown and Spectre exploit critical vulnerabilities in modern CPUs
 references:
 - https://meltdownattack.com/
 tags:
-  analytics_story: Spectre And Meltdown Vulnerabilities
+  analytic_story: Spectre And Meltdown Vulnerabilities
   category:
   - Vulnerability
   product:
@@ -19,5 +21,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/splunk_enterprise_vulnerability.yml b/stories/splunk_enterprise_vulnerability.yml
index afc3d3c769..0ff703097d 100644
--- a/stories/splunk_enterprise_vulnerability.yml
+++ b/stories/splunk_enterprise_vulnerability.yml
@@ -1,11 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Splunk Enterprise Vulnerability
+id: 4e692b96-de2d-4bd1-9105-37e2368a8db1
+version: 1
 date: '2017-09-19'
+author: Bhavin Patel, Splunk
+type: batch
 description: Keeping your Splunk deployment up to date is critical and may help you
   reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some
   older versions of Splunk Enterprise. The detection search will help ensure that
   users are being properly authenticated and not being redirected to malicious domains.
-id: 4e692b96-de2d-4bd1-9105-37e2368a8db1
-name: Splunk Enterprise Vulnerability
 narrative: 'This Analytic Story is associated with CVE-2016-4859, an open-redirect
   vulnerability in the following versions of Splunk Enterprise:\
 
@@ -35,7 +37,7 @@ references:
 - http://www.splunk.com/view/SP-CAAAPQ6#announce
 - https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859
 tags:
-  analytics_story: Splunk Enterprise Vulnerability
+  analytic_story: Splunk Enterprise Vulnerability
   category:
   - Vulnerability
   product:
@@ -43,5 +45,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/splunk_enterprise_vulnerability_cve_2018_11409.yml b/stories/splunk_enterprise_vulnerability_cve_2018_11409.yml
index 5396c9337e..29aa754be9 100644
--- a/stories/splunk_enterprise_vulnerability_cve_2018_11409.yml
+++ b/stories/splunk_enterprise_vulnerability_cve_2018_11409.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Splunk Enterprise Vulnerability CVE-2018-11409
+id: 1fc34cbc-34e9-43ba-87ab-6811c9e95400
+version: 1
 date: '2018-06-14'
+author: David Dorsey, Splunk
+type: batch
 description: Reduce the risk of CVE-2018-11409, an information disclosure vulnerability
   within some older versions of Splunk Enterprise, with searches designed to help
   ensure that your Splunk system does not leak information to authenticated users.
-id: 1fc34cbc-34e9-43ba-87ab-6811c9e95400
-name: Splunk Enterprise Vulnerability CVE-2018-11409
 narrative: 'Although there have been no reports of it being exploited, Splunk Enterprise
   versions through 7.0.1 reportedly have a vulnerability that may expose information
   through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings).
@@ -30,7 +32,7 @@ references:
 - https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings
 - https://www.exploit-db.com/exploits/44865/
 tags:
-  analytics_story: Splunk Enterprise Vulnerability CVE-2018-11409
+  analytic_story: Splunk Enterprise Vulnerability CVE-2018-11409
   category:
   - Vulnerability
   product:
@@ -38,5 +40,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/sql_injection.yml b/stories/sql_injection.yml
index 2b11009165..25b2df4022 100644
--- a/stories/sql_injection.yml
+++ b/stories/sql_injection.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: SQL Injection
+id: 4f6632f5-449c-4686-80df-57625f59bab3
+version: 1
 date: '2017-09-19'
+author: Bhavin Patel, Splunk
+type: batch
 description: Use the searches in this Analytic Story to help you detect structured
   query language (SQL) injection attempts characterized by long URLs that contain
   malicious parameters.
-id: 4f6632f5-449c-4686-80df-57625f59bab3
-name: SQL Injection
 narrative: 'It is very common for attackers to inject SQL parameters into vulnerable
   web applications, which then interpret the malicious SQL statements.\
 
@@ -15,7 +17,7 @@ references:
 - https://capec.mitre.org/data/definitions/66.html
 - https://www.incapsula.com/web-application-security/sql-injection.html
 tags:
-  analytics_story: SQL Injection
+  analytic_story: SQL Injection
   category:
   - Adversary Tactics
   product:
@@ -23,5 +25,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/sunburst_malware.yml b/stories/sunburst_malware.yml
index 3132c2c1ad..69bc44d62b 100644
--- a/stories/sunburst_malware.yml
+++ b/stories/sunburst_malware.yml
@@ -1,11 +1,13 @@
-author: Patrick Bareiss, Splunk
+name: Sunburst Malware
+id: 758196b5-2e21-424f-a50c-6e421ce926c2
+version: 1
 date: '2020-12-14'
+author: Patrick Bareiss, Splunk
+type: batch
 description: Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and
   management software. It was discovered by FireEye in December 2020. The actors behind
   this campaign gained access to numerous public and private organizations around
   the world.
-id: 758196b5-2e21-424f-a50c-6e421ce926c2
-name: Sunburst Malware
 narrative: This Analytic Story supports you to detect Tactics, Techniques and Procedures
   (TTPs) from the Sunburst malware. The threat actor behind sunburst compromised the
   SolarWinds.Orion.Core.BusinessLayer.dll, is a SolarWinds digitally-signed component
@@ -16,7 +18,7 @@ references:
 - https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html
 - https://msrc-blog.microsoft.com/2020/12/13/customer-guidance-on-recent-nation-state-cyber-attacks/
 tags:
-  analytics_story: Sunburst Malware
+  analytic_story: Sunburst Malware
   category:
   - Adversary Tactics
   product:
@@ -24,5 +26,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/suspicious_aws_ec2_activities.yml b/stories/suspicious_aws_ec2_activities.yml
index f41d8fd1d2..2b03ecafab 100644
--- a/stories/suspicious_aws_ec2_activities.yml
+++ b/stories/suspicious_aws_ec2_activities.yml
@@ -1,12 +1,14 @@
-author: Bhavin Patel, Splunk
+name: Suspicious AWS EC2 Activities
+id: 2e8948a5-5239-406b-b56b-6c50f1268af3
+version: 1
 date: '2018-02-09'
+author: Bhavin Patel, Splunk
+type: batch
 description: Use the searches in this Analytic Story to monitor your AWS EC2 instances
   for evidence of anomalous activity and suspicious behaviors, such as EC2 instances
   that originate from unusual locations or those launched by previously unseen users
   (among others). Included investigative searches will help you probe more deeply,
   when the information warrants it.
-id: 2e8948a5-5239-406b-b56b-6c50f1268af3
-name: Suspicious AWS EC2 Activities
 narrative: AWS CloudTrail is an AWS service that helps you enable governance, compliance,
   and risk auditing within your AWS account. Actions taken by a user, role, or an
   AWS service are recorded as events in CloudTrail. It is crucial for a company to
@@ -17,7 +19,7 @@ narrative: AWS CloudTrail is an AWS service that helps you enable governance, co
 references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 tags:
-  analytics_story: Suspicious AWS EC2 Activities
+  analytic_story: Suspicious AWS EC2 Activities
   category:
   - Cloud Security
   product:
@@ -25,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_aws_login_activities.yml b/stories/suspicious_aws_login_activities.yml
index 669eaf96d4..0c66b7088f 100644
--- a/stories/suspicious_aws_login_activities.yml
+++ b/stories/suspicious_aws_login_activities.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Suspicious AWS Login Activities
+id: 2e8948a5-5239-406b-b56b-6c59f1268af3
+version: 1
 date: '2019-05-01'
+author: Bhavin Patel, Splunk
+type: batch
 description: 'Monitor your AWS authentication events using your CloudTrail logs. Searches
   within this Analytic Story will help you stay aware of and investigate suspicious
   logins. '
-id: 2e8948a5-5239-406b-b56b-6c59f1268af3
-name: Suspicious AWS Login Activities
 narrative: It is important to monitor and control who has access to your AWS infrastructure.
   Detecting suspicious logins to your AWS infrastructure will provide good starting
   points for investigations. Abusive behaviors caused by compromised credentials can
@@ -13,7 +15,7 @@ narrative: It is important to monitor and control who has access to your AWS inf
 references:
 - https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html
 tags:
-  analytics_story: Suspicious AWS Login Activities
+  analytic_story: Suspicious AWS Login Activities
   category:
   - Cloud Security
   product:
@@ -21,5 +23,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_aws_s3_activities.yml b/stories/suspicious_aws_s3_activities.yml
index 482bdeec65..535981278a 100644
--- a/stories/suspicious_aws_s3_activities.yml
+++ b/stories/suspicious_aws_s3_activities.yml
@@ -1,11 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Suspicious AWS S3 Activities
+id: 2e8948a5-5239-406b-b56b-6c50w3168af3
+version: 2
 date: '2018-07-24'
+author: Bhavin Patel, Splunk
+type: batch
 description: Use the searches in this Analytic Story to monitor your AWS S3 buckets
   for evidence of anomalous activity and suspicious behaviors, such as detecting open
   S3 buckets and buckets being accessed from a new IP. The contextual and investigative
   searches will give you more information, when required.
-id: 2e8948a5-5239-406b-b56b-6c50w3168af3
-name: Suspicious AWS S3 Activities
 narrative: 'As cloud computing has exploded, so has the number of creative attacks
   on virtual environments. And as the number-two cloud-service provider, Amazon Web
   Services (AWS) has certainly had its share.\
@@ -22,7 +24,7 @@ references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 - https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/
 tags:
-  analytics_story: Suspicious AWS S3 Activities
+  analytic_story: Suspicious AWS S3 Activities
   category:
   - Cloud Security
   product:
@@ -30,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 2
diff --git a/stories/suspicious_aws_traffic.yml b/stories/suspicious_aws_traffic.yml
index db23133eff..d415be8ab2 100644
--- a/stories/suspicious_aws_traffic.yml
+++ b/stories/suspicious_aws_traffic.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Suspicious AWS Traffic
+id: 2e8948a5-5239-406b-b56b-6c50f2168af3
+version: 1
 date: '2018-05-07'
+author: Bhavin Patel, Splunk
+type: batch
 description: Leverage these searches to monitor your AWS network traffic for evidence
   of anomalous activity and suspicious behaviors, such as a spike in blocked outbound
   traffic in your virtual private cloud (VPC).
-id: 2e8948a5-5239-406b-b56b-6c50f2168af3
-name: Suspicious AWS Traffic
 narrative: "A virtual private cloud (VPC) is an on-demand managed cloud-computing\
   \ service that isolates computing resources for each client. Inside the VPC container,\
   \ the environment resembles a physical network. \\\nAmazon's VPC service enables\
@@ -23,7 +25,7 @@ narrative: "A virtual private cloud (VPC) is an on-demand managed cloud-computin
 references:
 - https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/
 tags:
-  analytics_story: Suspicious AWS Traffic
+  analytic_story: Suspicious AWS Traffic
   category:
   - Cloud Security
   product:
@@ -31,5 +33,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_cloud_authentication_activities.yml b/stories/suspicious_cloud_authentication_activities.yml
index b1c2c73da3..a9c61c3c41 100644
--- a/stories/suspicious_cloud_authentication_activities.yml
+++ b/stories/suspicious_cloud_authentication_activities.yml
@@ -1,10 +1,12 @@
-author: Rico Valdez, Splunk
+name: Suspicious Cloud Authentication Activities
+id: 6380ebbb-55c5-4fce-b754-01fd565fb73c
+version: 1
 date: '2020-06-04'
+author: Rico Valdez, Splunk
+type: batch
 description: 'Monitor your cloud authentication events. Searches within this Analytic
   Story leverage the recent cloud updates to the Authentication data model to help
   you stay aware of and investigate suspicious login activity. '
-id: 6380ebbb-55c5-4fce-b754-01fd565fb73c
-name: Suspicious Cloud Authentication Activities
 narrative: 'It is important to monitor and control who has access to your cloud infrastructure.
   Detecting suspicious logins will provide good starting points for investigations.
   Abusive behaviors caused by compromised credentials can lead to direct monetary
@@ -17,7 +19,7 @@ references:
 - https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/
 - https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html
 tags:
-  analytics_story: Suspicious Cloud Authentication Activities
+  analytic_story: Suspicious Cloud Authentication Activities
   category:
   - Cloud Security
   product:
@@ -25,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_cloud_instance_activities.yml b/stories/suspicious_cloud_instance_activities.yml
index 372f7195ee..d766a1f889 100644
--- a/stories/suspicious_cloud_instance_activities.yml
+++ b/stories/suspicious_cloud_instance_activities.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Suspicious Cloud Instance Activities
+id: 8168ca88-392e-42f4-85a2-767579c660ce
+version: 1
 date: '2020-08-25'
+author: David Dorsey, Splunk
+type: batch
 description: Monitor your cloud infrastructure provisioning activities for behaviors
   originating from unfamiliar or unusual locations. These behaviors may indicate that
   malicious activities are occurring somewhere within your cloud environment.
-id: 8168ca88-392e-42f4-85a2-767579c660ce
-name: Suspicious Cloud Instance Activities
 narrative: Monitoring your cloud infrastructure logs allows you enable governance,
   compliance, and risk auditing. It is crucial for a company to monitor events and
   actions taken in the their cloud environments to ensure that your instances are
@@ -13,7 +15,7 @@ narrative: Monitoring your cloud infrastructure logs allows you enable governanc
 references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 tags:
-  analytics_story: Suspicious Cloud Instance Activities
+  analytic_story: Suspicious Cloud Instance Activities
   category:
   - Cloud Security
   product:
@@ -21,5 +23,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_cloud_provisioning_activities.yml b/stories/suspicious_cloud_provisioning_activities.yml
index 8b3f34ab31..90f7a30d6a 100644
--- a/stories/suspicious_cloud_provisioning_activities.yml
+++ b/stories/suspicious_cloud_provisioning_activities.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Suspicious Cloud Provisioning Activities
+id: 51045ded-1575-4ba6-aef7-af6c73cffd86
+version: 1
 date: '2018-08-20'
+author: David Dorsey, Splunk
+type: batch
 description: Monitor your cloud infrastructure provisioning activities for behaviors
   originating from unfamiliar or unusual locations. These behaviors may indicate that
   malicious activities are occurring somewhere within your cloud environment.
-id: 51045ded-1575-4ba6-aef7-af6c73cffd86
-name: Suspicious Cloud Provisioning Activities
 narrative: 'Because most enterprise cloud infrastructure activities originate from
   familiar geographic locations, monitoring for activity from unknown or unusual regions
   is an important security measure. This indicator can be especially useful in environments
@@ -22,7 +24,7 @@ narrative: 'Because most enterprise cloud infrastructure activities originate fr
 references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 tags:
-  analytics_story: Suspicious Cloud Provisioning Activities
+  analytic_story: Suspicious Cloud Provisioning Activities
   category:
   - Cloud Security
   product:
@@ -30,5 +32,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_cloud_user_activities.yml b/stories/suspicious_cloud_user_activities.yml
index ea09741066..98bbade3b0 100644
--- a/stories/suspicious_cloud_user_activities.yml
+++ b/stories/suspicious_cloud_user_activities.yml
@@ -1,9 +1,11 @@
-author: David Dorsey, Splunk
+name: Suspicious Cloud User Activities
+id: 1ed5ce7d-5469-4232-92af-89d1a3595b39
+version: 1
 date: '2020-09-04'
+author: David Dorsey, Splunk
+type: batch
 description: Detect and investigate suspicious activities by users and roles in your
   cloud environments.
-id: 1ed5ce7d-5469-4232-92af-89d1a3595b39
-name: Suspicious Cloud User Activities
 narrative: 'It seems obvious that it is critical to monitor and control the users
   who have access to your cloud infrastructure. Nevertheless, it''s all too common
   for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable
@@ -17,7 +19,7 @@ references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 - https://redlock.io/blog/cryptojacking-tesla
 tags:
-  analytics_story: Suspicious Cloud User Activities
+  analytic_story: Suspicious Cloud User Activities
   category:
   - Cloud Security
   product:
@@ -25,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_command_line_executions.yml b/stories/suspicious_command_line_executions.yml
index 5e8795af33..1cd363a106 100644
--- a/stories/suspicious_command_line_executions.yml
+++ b/stories/suspicious_command_line_executions.yml
@@ -1,11 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Suspicious Command-Line Executions
+id: f4368ddf-d59f-4192-84f6-778ac5a3ffc7
+version: 2
 date: '2020-02-03'
+author: Bhavin Patel, Splunk
+type: batch
 description: Leveraging the Windows command-line interface (CLI) is one of the most
   common attack techniques--one that is also detailed in the MITRE ATT&CK framework.
   Use this Analytic Story to help you identify unusual or suspicious use of the CLI
   on Windows systems.
-id: f4368ddf-d59f-4192-84f6-778ac5a3ffc7
-name: Suspicious Command-Line Executions
 narrative: The ability to execute arbitrary commands via the Windows CLI is a primary
   goal for the adversary. With access to the shell, an attacker can easily run scripts
   and interact with the target system. Often, attackers may only have limited access
@@ -20,7 +22,7 @@ references:
 - https://www.microsoft.com/en-us/wdsi/threats/macro-malware
 - https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf
 tags:
-  analytics_story: Suspicious Command-Line Executions
+  analytic_story: Suspicious Command-Line Executions
   category:
   - Adversary Tactics
   product:
@@ -28,5 +30,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/suspicious_dns_traffic.yml b/stories/suspicious_dns_traffic.yml
index 9571ffb8bf..487bc98f77 100644
--- a/stories/suspicious_dns_traffic.yml
+++ b/stories/suspicious_dns_traffic.yml
@@ -1,10 +1,12 @@
-author: Rico Valdez, Splunk
+name: Suspicious DNS Traffic
+id: 3c3835c0-255d-4f9e-ab84-e29ec9ec9b56
+version: 1
 date: '2017-09-18'
+author: Rico Valdez, Splunk
+type: batch
 description: Attackers often attempt to hide within or otherwise abuse the domain
   name system (DNS). You can thwart attempts to manipulate this omnipresent protocol
   by monitoring for these types of abuses.
-id: 3c3835c0-255d-4f9e-ab84-e29ec9ec9b56
-name: Suspicious DNS Traffic
 narrative: Although DNS is one of the fundamental underlying protocols that make the
   Internet work, it is often ignored (perhaps because of its complexity and effectiveness).  However,
   attackers have discovered ways to abuse the protocol to meet their objectives. One
@@ -19,7 +21,7 @@ references:
 - http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680
 - https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454
 tags:
-  analytics_story: Suspicious DNS Traffic
+  analytic_story: Suspicious DNS Traffic
   category:
   - Adversary Tactics
   product:
@@ -27,5 +29,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/suspicious_emails.yml b/stories/suspicious_emails.yml
index 6dbb822469..e8dc840e5c 100644
--- a/stories/suspicious_emails.yml
+++ b/stories/suspicious_emails.yml
@@ -1,10 +1,12 @@
-author: Bhavin Patel, Splunk
+name: Suspicious Emails
+id: 2b1800dd-92f9-47ec-a981-fdf1351e5d55
+version: 1
 date: '2020-01-27'
+author: Bhavin Patel, Splunk
+type: batch
 description: Email remains one of the primary means for attackers to gain an initial
   foothold within the modern enterprise. Detect and investigate suspicious emails
   in your environment with the help of the searches in this Analytic Story.
-id: 2b1800dd-92f9-47ec-a981-fdf1351e5d55
-name: Suspicious Emails
 narrative: 'It is a common practice for attackers of all types to leverage targeted
   spearphishing campaigns and mass mailers to deliver weaponized email messages and
   attachments. Fortunately, there are a number of ways to monitor email data in Splunk
@@ -24,7 +26,7 @@ narrative: 'It is a common practice for attackers of all types to leverage targe
 references:
 - https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/
 tags:
-  analytics_story: Suspicious Emails
+  analytic_story: Suspicious Emails
   category:
   - Adversary Tactics
   product:
@@ -32,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/suspicious_gcp_storage_activities.yml b/stories/suspicious_gcp_storage_activities.yml
index 7db5cea4ca..ab82ae33e8 100644
--- a/stories/suspicious_gcp_storage_activities.yml
+++ b/stories/suspicious_gcp_storage_activities.yml
@@ -1,21 +1,23 @@
-author: Shannon Davis, Splunk
+name: Suspicious GCP Storage Activities
+id: 4d656b2e-d6be-11ea-87d0-0242ac130003
+version: 1
 date: '2020-08-05'
+author: Shannon Davis, Splunk
+type: batch
 description: Use the searches in this Analytic Story to monitor your GCP Storage buckets
   for evidence of anomalous activity and suspicious behaviors, such as detecting open
   storage buckets and buckets being accessed from a new IP. The contextual and investigative
   searches will give you more information, when required.
-id: 4d656b2e-d6be-11ea-87d0-0242ac130003
-name: Suspicious GCP Storage Activities
 narrative: Similar to other cloud providers, GCP operates on a shared responsibility
   model. This means the end user, you, are responsible for setting appropriate access
   control lists and permissions on your GCP resources.\ This Analytics Story concentrates
   on detecting things like open storage buckets (both read and write) along with storage
   bucket access from unfamiliar users and IP addresses.
 references:
-- https://cloud.google.com/blog/products/gcp/4-steps-for-hardening-your-cloud-storage-buckets-taking-charge-of-your-security
+- https://cloud.google.com/blog/product/gcp/4-steps-for-hardening-your-cloud-storage-buckets-taking-charge-of-your-security
 - https://rhinosecuritylabs.com/gcp/google-cloud-platform-gcp-bucket-enumeration/
 tags:
-  analytics_story: Suspicious GCP Storage Activities
+  analytic_story: Suspicious GCP Storage Activities
   category:
   - Cloud Security
   product:
@@ -23,5 +25,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_mshta_activity.yml b/stories/suspicious_mshta_activity.yml
index 39968b78a8..bf10152d8e 100644
--- a/stories/suspicious_mshta_activity.yml
+++ b/stories/suspicious_mshta_activity.yml
@@ -2,38 +2,39 @@ name: Suspicious MSHTA Activity
 id: 2b1800dd-92f9-47dd-a981-fdf13w1q5d55
 version: 2
 date: '2021-01-20'
+author: Bhavin Patel, Michael Haag, Splunk
+type: batch
 description: Monitor and detect techniques used by attackers who leverage the mshta.exe
   process to execute malicious code.
 narrative: 'One common adversary tactic is to bypass application control solutions
-  via the mshta.exe process, which loads Microsoft HTML applications (mshtml.dll) with the .hta
-  suffix. In these cases, attackers use the trusted Windows utility to proxy execution
-  of malicious files, whether an .hta application, javascript, or VBScript.\
+  via the mshta.exe process, which loads Microsoft HTML applications (mshtml.dll)
+  with the .hta suffix. In these cases, attackers use the trusted Windows utility
+  to proxy execution of malicious files, whether an .hta application, javascript,
+  or VBScript.\
 
   The searches in this story help you detect and investigate suspicious activity that
   may indicate that an attacker is leveraging mshta.exe to execute malicious code.\
-  
-  Triage\
-  Validate execution \
-  1. Determine if MSHTA.exe executed. Validate the OriginalFileName of MSHTA.exe and further
-  PE metadata. If executed outside of c:\windows\system32 or c:\windows\syswow64, it should be highly suspect.\
-  
+
+  Triage\ Validate execution \ 1. Determine if MSHTA.exe executed. Validate the OriginalFileName
+  of MSHTA.exe and further PE metadata. If executed outside of c:\windows\system32
+  or c:\windows\syswow64, it should be highly suspect.\
+
   1. Determine if script code was executed with MSHTA.\
-  
-  Situational Awareness\
-  The objective of this step is meant to identify suspicious behavioral indicators related to executed of 
-  Script code by MSHTA.exe.\
 
-  1. Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application?\
+  Situational Awareness\ The objective of this step is meant to identify suspicious
+  behavioral indicators related to executed of Script code by MSHTA.exe.\
 
-  1. Module loads. Are the known MSHTA.exe modules being loaded by a non-standard application? Is
-  MSHTA loading any suspicious .DLLs?\
+  1. Parent process. Is the parent process a known LOLBin? Is the parent process an
+  Office Application?\
 
-  1. Network connections. Any network connections? Review the reputation of the remote IP or domain.\
-  
-  Retrieval of script code\
-  The objective of this step is to confirm the executed script code is benign or malicious.'
-author: Bhavin Patel, Michael Haag, Splunk
-type: ESCU
+  1. Module loads. Are the known MSHTA.exe modules being loaded by a non-standard
+  application? Is MSHTA loading any suspicious .DLLs?\
+
+  1. Network connections. Any network connections? Review the reputation of the remote
+  IP or domain.\
+
+  Retrieval of script code\ The objective of this step is to confirm the executed
+  script code is benign or malicious.'
 references:
 - https://redcanary.com/blog/introducing-atomictestharnesses/
 - https://redcanary.com/blog/windows-registry-attacks-threat-detection/
@@ -41,6 +42,10 @@ references:
 - https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5
 tags:
   analytics_story: Suspicious MSHTA Activity
-  usecase: Advanced Threat Detection
   category:
   - Adversary Tactics
+  usecase: Advanced Threat Detection
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/stories/suspicious_okta_activity.yml b/stories/suspicious_okta_activity.yml
index 086b991cec..5e255e2fe3 100644
--- a/stories/suspicious_okta_activity.yml
+++ b/stories/suspicious_okta_activity.yml
@@ -1,12 +1,14 @@
-author: Rico Valdez, Splunk
+name: Suspicious Okta Activity
+id: 9cbd34af-8f39-4476-a423-bacd126c750b
+version: 1
 date: '2020-04-02'
+author: Rico Valdez, Splunk
+type: batch
 description: Monitor your Okta environment for suspicious activities. Due to the Covid
   outbreak, many users are migrating over to leverage cloud services more and more.
   Okta is a popular tool to manage multiple users and the web-based applications they
   need to stay productive. The searches in this story will help monitor your Okta
   environment for suspicious activities and associated user behaviors.
-id: 9cbd34af-8f39-4476-a423-bacd126c750b
-name: Suspicious Okta Activity
 narrative: 'Okta is the leading single sign on (SSO) provider, allowing users to authenticate
   once to Okta, and from there access a variety of web-based applications. These applications
   are assigned to users and allow administrators to centrally manage which users are
@@ -28,7 +30,7 @@ references:
 - https://owasp.org/www-community/attacks/Credential_stuffing
 - https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work
 tags:
-  analytics_story: Suspicious Okta Activity
+  analytic_story: Suspicious Okta Activity
   category:
   - Adversary Tactics
   product:
@@ -36,5 +38,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/suspicious_regsvr32_activity.yml b/stories/suspicious_regsvr32_activity.yml
index 89aaa298eb..fb1a5987fe 100644
--- a/stories/suspicious_regsvr32_activity.yml
+++ b/stories/suspicious_regsvr32_activity.yml
@@ -2,32 +2,37 @@ name: Suspicious Regsvr32 Activity
 id: b8bee41e-624f-11eb-ae93-0242ac130002
 version: 1
 date: '2021-01-29'
+author: Michael Haag, Splunk
+type: batch
 description: Monitor and detect techniques used by attackers who leverage the regsvr32.exe
   process to execute malicious code.
-narrative: 'One common adversary tactic is to bypass application control solutions
+narrative: One common adversary tactic is to bypass application control solutions
   via the regsvr32.exe process. This particular bypass was popularized with "SquiblyDoo"
-  using the "scrobj.dll" dll to load .sct scriptlets. This technique is still widely used
-  by adversaries to bypass detection and prevention controls. 
-  The file extension of the DLL is irrelevant (it may load a .txt file extension for example). 
-  The searches in this story help you detect and investigate suspicious activity that
-  may indicate that an adversary is leveraging regsvr32.exe to execute malicious code.  
-  Validate execution 
-  Determine if regsvr32.exe executed. Validate the OriginalFileName of regsvr32.exe and further PE metadata. If executed outside of c:\windows\system32 or c:\windows\syswow64, it should be highly suspect.
-  Determine if script code was executed with regsvr32.  
-  Situational Awareness - The objective of this step is meant to identify suspicious behavioral indicators related to executed of 
-  Script code by regsvr32.exe. 
-  Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application? 
-  Module loads.  Is regsvr32 loading any suspicious .DLLs? Unsigned or signed from non-standard paths. 
-  Network connections. Any network connections? Review the reputation of the remote IP or domain.     
-  Retrieval of Script Code - confirm the executed script code is benign or malicious.'
-author: Michael Haag, Splunk
-type: ESCU
+  using the "scrobj.dll" dll to load .sct scriptlets. This technique is still widely
+  used by adversaries to bypass detection and prevention controls. The file extension
+  of the DLL is irrelevant (it may load a .txt file extension for example). The searches
+  in this story help you detect and investigate suspicious activity that may indicate
+  that an adversary is leveraging regsvr32.exe to execute malicious code. Validate
+  execution Determine if regsvr32.exe executed. Validate the OriginalFileName of regsvr32.exe
+  and further PE metadata. If executed outside of c:\windows\system32 or c:\windows\syswow64,
+  it should be highly suspect. Determine if script code was executed with regsvr32.
+  Situational Awareness - The objective of this step is meant to identify suspicious
+  behavioral indicators related to executed of Script code by regsvr32.exe. Parent
+  process. Is the parent process a known LOLBin? Is the parent process an Office Application?
+  Module loads.  Is regsvr32 loading any suspicious .DLLs? Unsigned or signed from
+  non-standard paths. Network connections. Any network connections? Review the reputation
+  of the remote IP or domain. Retrieval of Script Code - confirm the executed script
+  code is benign or malicious.
 references:
 - https://attack.mitre.org/techniques/T1218/010/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
 - https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/
 tags:
   analytics_story: Suspicious Regsvr32 Activity
-  usecase: Advanced Threat Detection
   category:
-  - Adversary Tactics
\ No newline at end of file
+  - Adversary Tactics
+  usecase: Advanced Threat Detection
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/stories/suspicious_rundll32_activity.yml b/stories/suspicious_rundll32_activity.yml
index 727dd2bd7d..3834c54dd1 100644
--- a/stories/suspicious_rundll32_activity.yml
+++ b/stories/suspicious_rundll32_activity.yml
@@ -2,21 +2,29 @@ name: Suspicious Rundll32 Activity
 id: 80a65487-854b-42f1-80a1-935e4c170694
 version: 1
 date: '2021-02-03'
-description: Monitor and detect techniques used by attackers who leverage rundll32.exe to execute arbitrary malicious code.
-narrative: 'One common adversary tactic is to bypass application control solutions
-  via the rundll32.exe process. Natively, rundll32.exe will load DLLs and is a great example of a Living off the Land Binary. Rundll32.exe may load malicious DLLs by ordinals, function names or directly. 
-  The queries in this story focus on loading default DLLs, syssetup.dll, ieadvpack.dll, advpack.dll and setupapi.dll from disk that may be abused by adversaries. 
-  Additionally, two analytics developed to assist with identifying DLLRegisterServer, Start and StartW functions being called. 
-  The searches in this story help you detect and investigate suspicious activity that
-  may indicate that an adversary is leveraging rundll32.exe to execute malicious code.'
 author: Michael Haag, Splunk
-type: ESCU
+type: batch
+description: Monitor and detect techniques used by attackers who leverage rundll32.exe
+  to execute arbitrary malicious code.
+narrative: One common adversary tactic is to bypass application control solutions
+  via the rundll32.exe process. Natively, rundll32.exe will load DLLs and is a great
+  example of a Living off the Land Binary. Rundll32.exe may load malicious DLLs by
+  ordinals, function names or directly. The queries in this story focus on loading
+  default DLLs, syssetup.dll, ieadvpack.dll, advpack.dll and setupapi.dll from disk
+  that may be abused by adversaries. Additionally, two analytics developed to assist
+  with identifying DLLRegisterServer, Start and StartW functions being called. The
+  searches in this story help you detect and investigate suspicious activity that
+  may indicate that an adversary is leveraging rundll32.exe to execute malicious code.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
 - https://lolbas-project.github.io/lolbas/Binaries/Rundll32
 tags:
   analytics_story: Suspicious Rundll32 Activity
-  usecase: Advanced Threat Detection
   category:
-  - Adversary Tactics
\ No newline at end of file
+  - Adversary Tactics
+  usecase: Advanced Threat Detection
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/stories/suspicious_windows_registry_activities.yml b/stories/suspicious_windows_registry_activities.yml
index 21d3a5c0a8..ebc24e9a20 100644
--- a/stories/suspicious_windows_registry_activities.yml
+++ b/stories/suspicious_windows_registry_activities.yml
@@ -1,9 +1,11 @@
-author: Bhavin Patel, Splunk
+name: Suspicious Windows Registry Activities
+id: 2b1800dd-92f9-47dd-a981-fdf1351e5d55
+version: 1
 date: '2018-05-31'
+author: Bhavin Patel, Splunk
+type: batch
 description: Monitor and detect registry changes initiated from remote locations,
   which can be a sign that an attacker has infiltrated your system.
-id: 2b1800dd-92f9-47dd-a981-fdf1351e5d55
-name: Suspicious Windows Registry Activities
 narrative: "Attackers are developing increasingly sophisticated techniques for hijacking\
   \ target servers, while evading detection. One such technique that has become progressively\
   \ more common is registry modification.\\\n The registry is a key component of the\
@@ -17,7 +19,7 @@ references:
 - https://redcanary.com/blog/windows-registry-attacks-threat-detection/
 - https://attack.mitre.org/wiki/Technique/T1112
 tags:
-  analytics_story: Suspicious Windows Registry Activities
+  analytic_story: Suspicious Windows Registry Activities
   category:
   - Adversary Tactics
   product:
@@ -25,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/suspicious_wmi_use.yml b/stories/suspicious_wmi_use.yml
index 3057a0a1e9..af0c505ffc 100644
--- a/stories/suspicious_wmi_use.yml
+++ b/stories/suspicious_wmi_use.yml
@@ -1,12 +1,14 @@
-author: Rico Valdez, Splunk
+name: Suspicious WMI Use
+id: c8ddc5be-69bc-4202-b3ab-4010b27d7ad5
+version: 2
 date: '2018-10-23'
+author: Rico Valdez, Splunk
+type: batch
 description: Attackers are increasingly abusing Windows Management Instrumentation
   (WMI), a framework and associated utilities available on all modern Windows operating
   systems. Because WMI can be leveraged to manage both local and remote systems, it
   is important to identify the processes executed and the user context within which
   the activity occurred.
-id: c8ddc5be-69bc-4202-b3ab-4010b27d7ad5
-name: Suspicious WMI Use
 narrative: 'WMI is a Microsoft infrastructure for management data and operations on
   Windows operating systems. It includes of a set of utilities that can be leveraged
   to manage both local and remote Windows systems. Attackers are increasingly turning
@@ -25,7 +27,7 @@ references:
 - https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf
 - https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html
 tags:
-  analytics_story: Suspicious WMI Use
+  analytic_story: Suspicious WMI Use
   category:
   - Adversary Tactics
   product:
@@ -33,5 +35,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/suspicious_zoom_child_processes.yml b/stories/suspicious_zoom_child_processes.yml
index 8a6e3747f2..fae67cd19b 100644
--- a/stories/suspicious_zoom_child_processes.yml
+++ b/stories/suspicious_zoom_child_processes.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Suspicious Zoom Child Processes
+id: aa3749a6-49c7-491e-a03f-4eaee5fe0258
+version: 1
 date: '2020-04-13'
+author: David Dorsey, Splunk
+type: batch
 description: Attackers are using Zoom as an vector to increase privileges on a sytems.
   This story detects new child processes of zoom and provides investigative actions
   for this detection.
-id: aa3749a6-49c7-491e-a03f-4eaee5fe0258
-name: Suspicious Zoom Child Processes
 narrative: 'Zoom is a leader in modern enterprise video communications and its usage
   has increased dramatically with a large amount of the population under stay-at-home
   orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny
@@ -18,7 +20,7 @@ references:
 - https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/
 - https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/
 tags:
-  analytics_story: Suspicious Zoom Child Processes
+  analytic_story: Suspicious Zoom Child Processes
   category:
   - Adversary Tactics
   product:
@@ -26,5 +28,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/trusted_developer_utilities_proxy_execution.yml b/stories/trusted_developer_utilities_proxy_execution.yml
index 0b12ea3155..6a16527344 100644
--- a/stories/trusted_developer_utilities_proxy_execution.yml
+++ b/stories/trusted_developer_utilities_proxy_execution.yml
@@ -1,9 +1,11 @@
-author: Michael Haag, Splunk
+name: Trusted Developer Utilities Proxy Execution
+id: 270a67a6-55d8-11eb-ae93-0242ac130002
+version: 1
 date: '2021-01-12'
+author: Michael Haag, Splunk
+type: batch
 description: Monitor and detect behaviors used by attackers who leverage trusted developer
   utilities to execute malicious code.
-id: 270a67a6-55d8-11eb-ae93-0242ac130002
-name: Trusted Developer Utilities Proxy Execution
 narrative: 'Adversaries may take advantage of trusted developer utilities to proxy
   execution of malicious payloads. There are many utilities used for software development
   related tasks that can be used to execute code in various forms to assist in development,
@@ -19,7 +21,7 @@ references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md
 - https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/
 tags:
-  analytics_story: Trusted Developer Utilities Proxy Execution
+  analytic_story: Trusted Developer Utilities Proxy Execution
   category:
   - Adversary Tactics
   product:
@@ -27,5 +29,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/trusted_developer_utilities_proxy_execution_msbuild.yml b/stories/trusted_developer_utilities_proxy_execution_msbuild.yml
index 9afa461d44..7e7ba69a53 100644
--- a/stories/trusted_developer_utilities_proxy_execution_msbuild.yml
+++ b/stories/trusted_developer_utilities_proxy_execution_msbuild.yml
@@ -2,48 +2,50 @@ name: Trusted Developer Utilities Proxy Execution MSBuild
 id: be3418e2-551b-11eb-ae93-0242ac130002
 version: 1
 date: '2021-01-21'
+author: Michael Haag, Splunk
+type: batch
 description: Monitor and detect techniques used by attackers who leverage the msbuild.exe
   process to execute malicious code.
-narrative: 'Adversaries may use MSBuild to proxy execution of code through a trusted 
+narrative: 'Adversaries may use MSBuild to proxy execution of code through a trusted
   Windows utility. MSBuild.exe (Microsoft Build Engine) is a software build platform
   used by Visual Studio and is native to Windows. It handles XML formatted project
   files that define requirements for loading and building various platforms and configurations.\
 
   The inline task capability of MSBuild that was introduced in .NET version 4 allows
-  for C# code to be inserted into an XML project file. MSBuild will compile and
-  execute the inline task. MSBuild.exe is a signed Microsoft binary, so when it is
-  used this way it can execute arbitrary code and bypass application control defenses
-  that are configured to allow MSBuild.exe execution.\
+  for C# code to be inserted into an XML project file. MSBuild will compile and execute
+  the inline task. MSBuild.exe is a signed Microsoft binary, so when it is used this
+  way it can execute arbitrary code and bypass application control defenses that are
+  configured to allow MSBuild.exe execution.\
 
   The searches in this story help you detect and investigate suspicious activity that
   may indicate that an adversary is leveraging msbuild.exe to execute malicious code.\
-  
+
   Triage\
 
   Validate execution\
 
-  1. Determine if MSBuild.exe executed. Validate the OriginalFileName of MSBuild.exe and further
-  PE metadata.\
-  
+  1. Determine if MSBuild.exe executed. Validate the OriginalFileName of MSBuild.exe
+  and further PE metadata.\
+
   1. Determine if script code was executed with MSBuild.\
-  
+
   Situational Awareness\
 
-  The objective of this step is meant to identify suspicious behavioral indicators related to executed of 
-  Script code by MSBuild.exe.\
-  
-  1. Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application?\
-  
-  1. Module loads. Are the known MSBuild.exe modules being loaded by a non-standard application? Is
-  MSbuild loading any suspicious .DLLs?\
-  
-  1. Network connections. Any network connections? Review the reputation of the remote IP or domain.\
-  
+  The objective of this step is meant to identify suspicious behavioral indicators
+  related to executed of Script code by MSBuild.exe.\
+
+  1. Parent process. Is the parent process a known LOLBin? Is the parent process an
+  Office Application?\
+
+  1. Module loads. Are the known MSBuild.exe modules being loaded by a non-standard
+  application? Is MSbuild loading any suspicious .DLLs?\
+
+  1. Network connections. Any network connections? Review the reputation of the remote
+  IP or domain.\
+
   Retrieval of script code\
-  
+
   The objective of this step is to confirm the executed script code is benign or malicious.'
-author: Michael Haag, Splunk
-type: ESCU
 references:
 - https://attack.mitre.org/techniques/T1127/001/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
@@ -53,6 +55,10 @@ references:
 - https://github.com/MHaggis/CBR-Queries/blob/master/msbuild.md
 tags:
   analytics_story: Trusted Developer Utilities Proxy Execution MSBuild
-  usecase: Advanced Threat Detection
   category:
-  - Adversary Tactics
\ No newline at end of file
+  - Adversary Tactics
+  usecase: Advanced Threat Detection
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
diff --git a/stories/unusual_aws_ec2_modifications.yml b/stories/unusual_aws_ec2_modifications.yml
index 10ca204326..d58c8c9214 100644
--- a/stories/unusual_aws_ec2_modifications.yml
+++ b/stories/unusual_aws_ec2_modifications.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Unusual AWS EC2 Modifications
+id: 73de57ef-0dfc-411f-b1e7-fa24428aeae0
+version: 1
 date: '2018-04-09'
+author: David Dorsey, Splunk
+type: batch
 description: Identify unusual changes to your AWS EC2 instances that may indicate
   malicious activity. Modifications to your EC2 instances by previously unseen users
   is an example of an activity that may warrant further investigation.
-id: 73de57ef-0dfc-411f-b1e7-fa24428aeae0
-name: Unusual AWS EC2 Modifications
 narrative: "A common attack technique is to infiltrate a cloud instance and make modifications.\
   \ The adversary can then secure access to your infrastructure or hide their activities.\
   \ So it's important to stay alert to changes that may indicate that your environment\
@@ -17,7 +19,7 @@ narrative: "A common attack technique is to infiltrate a cloud instance and make
 references:
 - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf
 tags:
-  analytics_story: Unusual AWS EC2 Modifications
+  analytic_story: Unusual AWS EC2 Modifications
   category:
   - Cloud Security
   product:
@@ -25,5 +27,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/unusual_processes.yml b/stories/unusual_processes.yml
index 945f3c39ef..128a66c667 100644
--- a/stories/unusual_processes.yml
+++ b/stories/unusual_processes.yml
@@ -1,11 +1,13 @@
-author: Bhavin Patel, Splunk
+name: Unusual Processes
+id: f4368e3f-d59f-4192-84f6-748ac5a3ddb6
+version: 2
 date: '2020-02-04'
+author: Bhavin Patel, Splunk
+type: batch
 description: Quickly identify systems running new or unusual processes in your environment
   that could be indicators of suspicious activity. Processes run from unusual locations,
   those with conspicuously long command lines, and rare executables are all examples
   of activities that may warrant deeper investigation.
-id: f4368e3f-d59f-4192-84f6-748ac5a3ddb6
-name: Unusual Processes
 narrative: 'Being able to profile a host''s processes within your environment can
   help you more quickly identify processes that seem out of place when compared to
   the rest of the population of hosts or asset types.\
@@ -24,7 +26,7 @@ references:
 - https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf
 - https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262
 tags:
-  analytics_story: Unusual Processes
+  analytic_story: Unusual Processes
   category:
   - Malware
   product:
@@ -32,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/use_of_cleartext_protocols.yml b/stories/use_of_cleartext_protocols.yml
index adf2c000a3..b6e205332f 100644
--- a/stories/use_of_cleartext_protocols.yml
+++ b/stories/use_of_cleartext_protocols.yml
@@ -1,9 +1,11 @@
-author: Bhavin Patel, Splunk
+name: Use of Cleartext Protocols
+id: 826e6431-aeef-41b4-9fc0-6d0985d65a21
+version: 1
 date: '2017-09-15'
+author: Bhavin Patel, Splunk
+type: batch
 description: Leverage searches that detect cleartext network protocols that may leak
   credentials or should otherwise be encrypted.
-id: 826e6431-aeef-41b4-9fc0-6d0985d65a21
-name: Use of Cleartext Protocols
 narrative: Various legacy protocols operate by default in the clear, without the protections
   of encryption. This potentially leaks sensitive information that can be exploited
   by passively sniffing network traffic. Depending on the protocol, this information
@@ -14,7 +16,7 @@ narrative: Various legacy protocols operate by default in the clear, without the
 references:
 - https://www.monkey.org/~dugsong/dsniff/
 tags:
-  analytics_story: Use of Cleartext Protocols
+  analytic_story: Use of Cleartext Protocols
   category:
   - Best Practices
   product:
@@ -22,5 +24,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 1
diff --git a/stories/web_fraud_detection.yml b/stories/web_fraud_detection.yml
index 218c6362ff..a0bdff5e92 100644
--- a/stories/web_fraud_detection.yml
+++ b/stories/web_fraud_detection.yml
@@ -1,9 +1,11 @@
-author: Jim Apger, Splunk
+name: Web Fraud Detection
+id: 31337aaa-bc22-4752-b599-ef112dq1dq7a
+version: 1
 date: '2018-10-08'
+author: Jim Apger, Splunk
+type: batch
 description: Monitor your environment for activity consistent with common attack techniques
   bad actors use when attempting to compromise web servers or other web-related assets.
-id: 31337aaa-bc22-4752-b599-ef112dq1dq7a
-name: Web Fraud Detection
 narrative: 'The Federal Bureau of Investigations (FBI) defines Internet fraud as the
   use of Internet services or software with Internet access to defraud victims or
   to otherwise take advantage of them. According to the Bureau, Internet crime schemes
@@ -41,7 +43,7 @@ references:
 - https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud
 - https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718
 tags:
-  analytics_story: Web Fraud Detection
+  analytic_story: Web Fraud Detection
   category:
   - Abuse
   product:
@@ -49,5 +51,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Fraud Detection
-type: ESCU
-version: 1
diff --git a/stories/windows_defense_evasion_tactics.yml b/stories/windows_defense_evasion_tactics.yml
index fc63d52b18..3f9b2ca5f2 100644
--- a/stories/windows_defense_evasion_tactics.yml
+++ b/stories/windows_defense_evasion_tactics.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Windows Defense Evasion Tactics
+id: 56e24a28-5003-4047-b2db-e8f3c4618064
+version: 1
 date: '2018-05-31'
+author: David Dorsey, Splunk
+type: batch
 description: 'Detect tactics used by malware to evade defenses on Windows endpoints.
   A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe`
   and disabling user-account control, among many others '
-id: 56e24a28-5003-4047-b2db-e8f3c4618064
-name: Windows Defense Evasion Tactics
 narrative: Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that
   adversaries employ in a variety of ways to bypass or defeat defensive security measures.
   There are many techniques enumerated by the MITRE ATT&CK framework that are applicable
@@ -13,7 +15,7 @@ narrative: Defense evasion is a tactic--identified in the MITRE ATT&CK framework
 references:
 - https://attack.mitre.org/wiki/Defense_Evasion
 tags:
-  analytics_story: Windows Defense Evasion Tactics
+  analytic_story: Windows Defense Evasion Tactics
   category:
   - Adversary Tactics
   product:
@@ -21,5 +23,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/windows_dns_sigred_cve_2020_1350.yml b/stories/windows_dns_sigred_cve_2020_1350.yml
index 835bc5b9f0..8a4296c821 100644
--- a/stories/windows_dns_sigred_cve_2020_1350.yml
+++ b/stories/windows_dns_sigred_cve_2020_1350.yml
@@ -1,5 +1,9 @@
-author: Shannon Davis, Splunk
+name: Windows DNS SIGRed CVE-2020-1350
+id: 36dbb206-d073-11ea-87d0-0242ac130003
+version: 1
 date: '2020-07-28'
+author: Shannon Davis, Splunk
+type: batch
 description: Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered
   by Checkpoint researchers, this vulnerability affects Windows 2003 to 2019, and
   is triggered by a malicious DNS response (only affects DNS over TCP). An attacker
@@ -7,8 +11,6 @@ description: Uncover activity consistent with CVE-2020-1350, or SIGRed. Discover
   leading to compromise.  The included searches in this Analytic Story are designed
   to identify the large response payload for SIG and KEY DNS records which can be
   used for the exploit.
-id: 36dbb206-d073-11ea-87d0-0242ac130003
-name: Windows DNS SIGRed CVE-2020-1350
 narrative: When a client requests a DNS record for a particular domain, that request
   gets routed first through the client's locally configured DNS server, then to any
   DNS server(s) configured as forwarders, and then onto the target domain's own DNS
@@ -25,7 +27,7 @@ references:
 - https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/
 - https://support.microsoft.com/en-au/help/4569509/windows-dns-server-remote-code-execution-vulnerability
 tags:
-  analytics_story: Windows DNS SIGRed CVE-2020-1350
+  analytic_story: Windows DNS SIGRed CVE-2020-1350
   category:
   - Adversary Tactics
   product:
@@ -33,5 +35,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/windows_file_extension_and_association_abuse.yml b/stories/windows_file_extension_and_association_abuse.yml
index 2650850964..f1436b70df 100644
--- a/stories/windows_file_extension_and_association_abuse.yml
+++ b/stories/windows_file_extension_and_association_abuse.yml
@@ -1,11 +1,13 @@
-author: Rico Valdez, Splunk
+name: Windows File Extension and Association Abuse
+id: 30552a76-ac78-48e4-b3c0-de4e34e9563d
+version: 1
 date: '2018-01-26'
+author: Rico Valdez, Splunk
+type: batch
 description: Detect and investigate suspected abuse of file extensions and Windows
   file associations. Some of the malicious behaviors involved may include inserting
   spaces before file extensions or prepending the file extension with a different
   one, among other techniques.
-id: 30552a76-ac78-48e4-b3c0-de4e34e9563d
-name: Windows File Extension and Association Abuse
 narrative: "Attackers use a variety of techniques to entice users to run malicious\
   \ code or to persist on an endpoint. One way to accomplish these goals is to leverage\
   \ file extensions and the mechanism Windows uses to associate files with specific\
@@ -35,7 +37,7 @@ references:
 - https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/
 - https://attack.mitre.org/wiki/Technique/T1042
 tags:
-  analytics_story: Windows File Extension and Association Abuse
+  analytic_story: Windows File Extension and Association Abuse
   category:
   - Malware
   product:
@@ -43,5 +45,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 1
diff --git a/stories/windows_log_manipulation.yml b/stories/windows_log_manipulation.yml
index e8df583cb7..1fe8857921 100644
--- a/stories/windows_log_manipulation.yml
+++ b/stories/windows_log_manipulation.yml
@@ -1,10 +1,12 @@
-author: Rico Valdez, Splunk
+name: Windows Log Manipulation
+id: b6db2c60-a281-48b4-95f1-2cd99ed56835
+version: 2
 date: '2017-09-12'
+author: Rico Valdez, Splunk
+type: batch
 description: Adversaries often try to cover their tracks by manipulating Windows logs.
   Use these searches to help you monitor for suspicious activity surrounding log files--an
   essential component of an effective defense.
-id: b6db2c60-a281-48b4-95f1-2cd99ed56835
-name: Windows Log Manipulation
 narrative: 'Because attackers often modify system logs to cover their tracks and/or
   to thwart the investigative process, log monitoring is an industry-recognized best
   practice. While there are legitimate reasons to manipulate system logs, it is still
@@ -24,7 +26,7 @@ references:
 - https://zeltser.com/security-incident-log-review-checklist/
 - http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html
 tags:
-  analytics_story: Windows Log Manipulation
+  analytic_story: Windows Log Manipulation
   category:
   - Adversary Tactics
   product:
@@ -32,5 +34,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Security Monitoring
-type: ESCU
-version: 2
diff --git a/stories/windows_persistence_techniques.yml b/stories/windows_persistence_techniques.yml
index b08ba06bb7..43d49de5d5 100644
--- a/stories/windows_persistence_techniques.yml
+++ b/stories/windows_persistence_techniques.yml
@@ -1,9 +1,11 @@
-author: Bhavin Patel, Splunk
+name: Windows Persistence Techniques
+id: 30874d4f-20a1-488f-85ec-5d52ef74e3f9
+version: 2
 date: '2018-05-31'
+author: Bhavin Patel, Splunk
+type: batch
 description: Monitor for activities and techniques associated with maintaining persistence
   on a Windows system--a sign that an adversary may have compromised your environment.
-id: 30874d4f-20a1-488f-85ec-5d52ef74e3f9
-name: Windows Persistence Techniques
 narrative: Maintaining persistence is one of the first steps taken by attackers after
   the initial compromise. Attackers leverage various custom and built-in tools to
   ensure survivability and persistent access within a compromised enterprise. This
@@ -16,7 +18,7 @@ references:
 - https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html
 - https://www.youtube.com/watch?v=dq2Hv7J9fvk
 tags:
-  analytics_story: Windows Persistence Techniques
+  analytic_story: Windows Persistence Techniques
   category:
   - Adversary Tactics
   product:
@@ -24,5 +26,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/windows_privilege_escalation.yml b/stories/windows_privilege_escalation.yml
index c02011d9ad..ebc3787d94 100644
--- a/stories/windows_privilege_escalation.yml
+++ b/stories/windows_privilege_escalation.yml
@@ -1,10 +1,12 @@
-author: David Dorsey, Splunk
+name: Windows Privilege Escalation
+id: 644e22d3-598a-429c-a007-16fdb802cae5
+version: 2
 date: '2020-02-04'
+author: David Dorsey, Splunk
+type: batch
 description: Monitor for and investigate activities that may be associated with a
   Windows privilege-escalation attack, including unusual processes running on endpoints,
   modified registry keys, and more.
-id: 644e22d3-598a-429c-a007-16fdb802cae5
-name: Windows Privilege Escalation
 narrative: 'Privilege escalation is a "land-and-expand" technique, wherein an adversary
   gains an initial foothold on a host and then exploits its weaknesses to increase
   his privileges. The motivation is simple: certain actions on a Windows machine--such
@@ -16,7 +18,7 @@ narrative: 'Privilege escalation is a "land-and-expand" technique, wherein an ad
 references:
 - https://attack.mitre.org/tactics/TA0004/
 tags:
-  analytics_story: Windows Privilege Escalation
+  analytic_story: Windows Privilege Escalation
   category:
   - Adversary Tactics
   product:
@@ -24,5 +26,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 2
diff --git a/stories/windows_service_abuse.yml b/stories/windows_service_abuse.yml
index 866912c427..1baa5e434c 100644
--- a/stories/windows_service_abuse.yml
+++ b/stories/windows_service_abuse.yml
@@ -1,11 +1,13 @@
-author: Rico Valdez, Splunk
+name: Windows Service Abuse
+id: 6dbd810e-f66d-414b-8dfc-e46de55cbfe2
+version: 3
 date: '2017-11-02'
+author: Rico Valdez, Splunk
+type: batch
 description: Windows services are often used by attackers for persistence and the
   ability to load drivers or otherwise interact with the Windows kernel. This Analytic
   Story helps you monitor your environment for indications that Windows services are
   being modified or created in a suspicious manner.
-id: 6dbd810e-f66d-414b-8dfc-e46de55cbfe2
-name: Windows Service Abuse
 narrative: The Windows operating system uses a services architecture to allow for
   running code in the background, similar to a UNIX daemon. Attackers will often leverage
   Windows services for persistence, hiding in plain sight, seeking the ability to
@@ -19,7 +21,7 @@ references:
 - https://attack.mitre.org/wiki/Technique/T1050
 - https://attack.mitre.org/wiki/Technique/T1031
 tags:
-  analytics_story: Windows Service Abuse
+  analytic_story: Windows Service Abuse
   category:
   - Malware
   product:
@@ -27,5 +29,3 @@ tags:
   - Splunk Enterprise Security
   - Splunk Cloud
   usecase: Advanced Threat Detection
-type: ESCU
-version: 3