Detection Engineering: Practicing Detection-as-Code – Monitoring – Part 7

This article presents a practical automation framework for the maintenance phase of detection engineering in Microsoft Sentinel, including KQL queries to track detection trigger rates, entity appearances, tampering (rule deletions/disablement), and rule health (daily/monthly failure ratios). It pairs these analytics with a Python script, Jinja templates, and an Azure DevOps pipeline to filter results against a detection repository and automatically create or update Azure Boards work items, enabling proactive tuning and remediation of noisy or failing analytic rules.