2026-05-15 Detection Engineering

Microsoft Sentinel Detection Engineering: A Practical Approach

Sentinel KQL Detection

The Case for Detection-as-Code

Detection rules in Microsoft Sentinel are typically managed through the portal — a workflow that works for small deployments but breaks down at scale. When you have hundreds of rules across multiple workspaces, portal-based management becomes error-prone and impossible to version control.

Detection-as-code treats detection rules like software: versioned, tested, reviewed, and deployed through automated pipelines. This approach brings discipline to detection engineering and makes the entire detection lifecycle auditable.

KQL Rule Authoring

The foundation of Sentinel detection is Kusto Query Language. Writing effective KQL rules requires understanding both the syntax and the underlying data schemas. Key patterns include:

  • Time-window aggregation — Detecting patterns over sliding windows.
  • Join operations — Correlating events across multiple tables.
  • Parse operators — Extracting structured data from unstructured logs.
  • Anomaly detection — Using built-in anomaly functions for baseline deviations.

Version Control with Git

Store all detection rules in a Git repository. Each rule is a file — typically JSON or YAML for scheduled analytics rules, KQL for the query itself. This gives you:

  • Full history of every rule change.
  • Branch-based workflows for rule development.
  • Pull request reviews before deployment.
  • Rollback capability when rules cause issues.

Testing and Validation

Before deploying rules to production, validate them against known datasets. This can be as simple as running the KQL query against a test workspace or as sophisticated as a full CI/CD pipeline with synthetic event generation.

"A detection rule that has never been tested is a detection rule that does not exist."

Continuous Tuning

Detection engineering is never finished. Rules need tuning as the environment changes, new threat patterns emerge, and false positives are identified. Track rule performance metrics and schedule regular reviews.

顺势而为，趋吉避凶