TL;DR
- Sigma is to SIEM detections what SQL is to databases: a portable rule language.
- You get leverage by standardizing how you write rules, then compiling to your backend.
- The hard part isn’t the rule syntax—it’s field mapping, log quality, and tuning.
What it is
An open standard and community rule set for describing detection logic in a backend-agnostic way.
When to use it
- You want a vendor-neutral detection content workflow.
- You operate multiple environments (Splunk + Sentinel + Elastic) and don’t want to rewrite detections.
- You want to treat detections as code: review, test, version, deploy.
When not to use it
- Your logs aren’t normalized (you’ll spend all your time mapping fields).
- You need deeply backend-specific features (advanced correlation, ML-driven detections) as your first step.
Practical setup (minimal)
- Pick your log source priorities (auth, endpoint, cloud audit).
- Establish field mapping (Sigma expects certain fields).
- Start with a small curated set of rules; compile/translate to your SIEM.
- Add feedback loops: false positive rate, suppression, environment-specific exceptions.
Recommended actions
- Now: add Sigma to the “detection content” toolbelt and translate a handful of high-signal rules.
- A practical repository workflow is: lint → translate → test against representative logs → deploy through review.
- Later: score rules by evidence strength + operational noise and build a “tiered detections” library.
Links
- GitHub: SigmaHQ/sigma