This blog post outlines the process of creating custom detection rules in Elastic Security for enhanced threat detection. It emphasizes using the Elasticsearch Query Language (ES|QL) for precise filtering and categorization of security events. The Elastic AI Assistant is presented as a tool to streamline ES|QL query creation, particularly with the CASE function for categorizing API calls. The guide details how to refine initial broad searches, like AWS CloudTrail logs, to focus on specific actions related to privilege escalation. It explains mapping AI-generated queries to actual datastream fields and adding contextual criteria like successful execution and specific user identities. The post then moves to rule creation, suggesting building block alerts for less critical events and custom query detections for immediate triage. Automated response actions are highlighted as a method to reduce Mean Time To Respond (MTTR). A crucial step involves previewing rule results against historical data to validate alert volume and analyst experience. End-to-end testing using threat emulation scripts confirms rule functionality. Finally, it touches upon rule deployment to production, ongoing maintenance, and the importance of the Detection Engineering Behavior Maturity Model.