The Splunk Security Content Exchange 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. This project provides a mechanism to facilitate this exchange.

The repo includes 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 this tool 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 better 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

The Splunk Security Content Exchange 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 dependencies and pre-commit hooks under "Developing" before you begin.

Select the content piece you want to write.
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.
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
detections/: Splunk Enterprise, Splunk UBA, and Splunk Phantom detections 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 triggered 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 the security content

Developing

Dependecies and Pre-Commit Hooks

Install project dependecies and tests that run before content is committed:

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

CI Tools

Tools that help with testing CI jobs:

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

Generate Docs from Schema

To automatically generate docs from schema:

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

Troubleshooting

Our Automated Tests

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.
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.
CI builds a DA-ESS-ContentUpdate Splunk package using the Splunk Packaging Toolkit.
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:

Post a question to Splunk Answers
Join the #security-research room in the Splunk Slack channel
If you are a Splunk Enterprise customer with a valid support entitlement contract and have a Splunk-related question, you can also open a support case on the https://www.splunk.com/ support portal

Contributing

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

To Dos

Build CLI for interacting and developing