Kseniia Ignatovych and Isai Anthony's blog post offers a comprehensive guide to creating custom detection rules in Elastic Security, focusing on using Elasticsearch Query Language (ES|QL) and Elastic AI Assistant to improve threat detection and provide context for analysts. The authors emphasize the importance of developing detection logic that captures behavioral patterns rather than relying solely on static indicators, thereby ensuring that detections remain effective despite evolving threats. The blog outlines a four-step process for creating and deploying detection rules, including defining a focus area, refining detection logic, previewing and testing rules, and deploying them to production. Through examples involving AWS CloudTrail, the post illustrates techniques for identifying potential threats and evasion tactics by querying for specific API calls and analyzing log data. The article also highlights best practices for rule creation, such as mapping detections to MITRE ATT&CK, providing investigation guides, and setting up automated responses to reduce mean time to respond (MTTR). Elastic Security's community involvement is encouraged, inviting users to share custom rules and engage with Elastic's community resources for continual improvement of security operations.