Overview#
Designing, implementing, and tuning detections for threats and misconfigurations. Requires high-fidelity signals, threat models, and continuous validation with adversary simulation.
Core objectives#
- Establish shared definitions of Detection Engineering for security, engineering, and leadership teams.
- Connect Detection Engineering activities to measurable risk reduction and resilience goals.
- Provide onboarding notes so new team members can quickly understand how Detection Engineering works here.
Implementation notes#
- Identify the primary owner for Detection Engineering, the data sources involved, and the systems affected.
- Document the minimum viable process, tooling, and runbooks that keep Detection Engineering healthy.
- Map Detection Engineering practices to standards such as ISO/IEC 27001, NIST CSF, or CIS Controls.
Operational signals#
- Leading indicators: early warnings that Detection Engineering might degrade (e.g., backlog growth, noisy alerts, or missed SLAs).
- Lagging indicators: realized impact that shows Detection Engineering failed or needs investment (e.g., incidents, audit findings).
- Feedback loops: retrospectives and metrics reviews that tune Detection Engineering continuously.
Related practices#
- Align Detection Engineering with defense-in-depth planning, threat modeling, and disaster recovery tests.
- Communicate updates to stakeholders through concise briefs, dashboards, and internal FAQs.
- Pair Detection Engineering improvements with tabletop exercises to validate expectations.