Why Detections-as-Code Matters

In cloud environments, detection content fails when it is treated as static SIEM configuration. Mature teams treat detections as software artifacts: versioned, testable, peer-reviewed, and promoted through controlled release stages.

Reference Implementation

• Rule repository: Sigma/KQL/SPL rules with owners, ATT&CK mapping, and telemetry dependencies.
• CI checks: schema validation, query linting, syntax checks, and metadata policy enforcement.
• Synthetic testing: replay CloudTrail/Azure Activity/M365 fixtures to validate true positives before production release.
• Promotion gates: dev to staging to prod with approvals and rollback tags.

Engineering Controls

• Minimum data quality thresholds before enabling rules
• Alert enrichment with identity, asset criticality, and geo context
• Detection SLOs for precision, noise ratio, and time-to-tune
• Canary window after deploy to catch noisy regressions

Operational Outcomes

This model reduces deployment friction and improves analyst trust by making detection quality measurable. Instead of ad hoc rule edits, the team gains a repeatable engineering pipeline that scales with cloud growth.

Lessons Learned

Most detection failures are process failures, not query failures. The highest ROI came from quality gates, ownership metadata, and fast feedback loops between detections and incident responders.