Detection-as-Code (DaC)

Detection-as-Code is a cybersecurity practice that treats detection rules and logic as software code, applying software development methodologies to security monitoring.

This approach involves writing, versioning, testing, and deploying detection rules using the same tools and processes used for application development, such as version control systems, automated testing, and continuous integration/continuous deployment (CI/CD) pipelines.

Traditional security detection methods often rely on manual rule creation and maintenance through security information and event management (SIEM) interfaces, leading to inconsistencies, errors, and difficulty tracking changes over time. Detection-as-Code addresses these challenges by storing detection logic in code repositories, enabling collaborative development, peer review, and automated validation of detection rules before deployment.

Key benefits include improved rule quality through code review processes, better documentation and change tracking, easier replication across environments, and the ability to roll back problematic detections quickly. Security teams can leverage programming languages like Python, YAML, or domain-specific languages to create more sophisticated and maintainable detection logic.

This methodology also enables security teams to adopt DevOps practices, fostering better collaboration between security and engineering teams while ensuring detection capabilities evolve systematically alongside threats and organizational changes.