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security-content security-content

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The security-content was designed to bring the community together to improve our collective defenses. By sharing research and analytics, we can help the entire industry craft more effective strategies. security-content provides a mechanism to facilitate this exchange.

security-content is composed of a collection of security guides called Analytic Stories that provide background on TTPs, mapped to the MITRE framework, the Lockheed Martin Kill Chain, and CIS controls. They include Splunk searches, machine-learning algorithms, and Splunk Phantom playbooks (where available)—all designed to work together to detect, investigate, and respond to threats.

You can set security-content to run detections and automatically funnel the results to investigations, reducing the need for manual intervention. When available, you can automatically trigger reponses, as well. The alerts you'll get include context (history, correlations, etc.), so they help you understand their importance. The net effect is a more efficient workflow, as well as more comprehensive, effective defenses.

Follow the instructions below to get started.

Usage

security-content can be used via:

Splunk App

Grab the latest release of DA-ESS-ContentUpdate and install it on a Splunk Enterprise server (search head).

API

curl -s https://content.splunkresearch.com | jq
{
  "hello": "welcome to Splunks Research security content api",
  "available_endpoints": [
    "/stories",
    "/detections",
    "/investigations",
    "/baselines",
    "/responses",
    "/package"
  ]
}

What's in an Analytic Story?

Analytic Stories and their corresponding searches are composed of .yml files (manifests) and associated .conf files. The stories reside in /stories and the searches live in /detections.

Manifests contain a number of mandatory and optional fields. You can see the full field list for each piece of content here

Writing Content

First, make sure to follow the steps to install dependecies and pre-commit hook's under developing before you begin.

  1. Select the content piece you want to write.
  2. Copy an example and edit it to suit your needs. At a minimum, you must write a story, a detection search, and an investigative search.
  3. Make a pull request. If Circle CI fails, refer to troubleshooting.

For a more detailed explanation on how to contribute to the project please see Contributing

Security Content Layout

Content Parts

  • stories/: All Analytic Stories for ESCU
  • detections/: Splunk Enterprise, Splunk UBA, and Splunk Phantom detection searches that power Analytic Stories
  • investigations/: Splunk Enterprise and Splunk Phantom investigative searches and playbooks employed by Analytic Stories
  • responses/: Automated Splunk Enterprise and Splunk Phantom responses used by Analytic Stories
  • baselines/: Splunk Phantom and Splunk Enterprise baseline searches needed to support detection searches in Analytic Stories

Supporting parts

  • package/: Splunk content app-source files, including lookups, binaries, and default config files
  • bin/: All binaries required to produce and test content

Docs

  • docs/: Documentation for all spec files
  • spec/: All spec files that describe ESCU content

Developing

dependecies and pre-commit hook's

Install project dependecies and tests that run before content is commited

  1. Create virtualenv and install requirements: virtualenv venv && source venv/bin/activate && pip install -r requirements.txt.
  2. Install pre-commit install.
CI tools

tools that help with testing CI jobs

  1. Install CircleCi CLI Tool.
  2. To test a local change to CircleCi or build, make sure you are running Docker and then enter circleci local execute -e GITHUB_TOKEN=$GITHUB_TOKEN --branch <your branch>

Generate docs from schema

To generate docs from schema automatically

  1. install https://github.com/adobe/jsonschema2md
  2. Enter jsonschema2md -d spec/v2/detections.json.spec -o docs

Troubleshooting

Our Automated Tests

  1. CI validates that the content was written to spec using validate.py. To run validation manually, execute: python bin/generate.py --path . --output package --storiesv1 --use_case_lib -v.
  2. CI generates Splunk configuration files using generate.py. If you want to export Splunk .conf files manually from the content, run: python bin/generate.py --path . --output package --storiesv1 --use_case_lib -v.
  3. CI builds a DA-ESS-ContentUpdate Splunk package using the Splunk Packaging Toolkit.
  4. CI tests the newly produced package using Splunk Appinspect.

Support

Please use the GitHub issue tracker to submit bugs or request features.

If you have questions or need support, you can:

Contributing

We welcome feedback and contributions from the community! Please see our contribution guidelines for more information on how to get involved.

Todo's

  • Build CLI for interacting and developing
S
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