The Challenge
Security engineering and site reliability teams deploy regularly a high volume of detection rules for security detection and observability checks. Traditionally these rules are deployed manually through platform web or app UIs (in SIEM platforms such as Elastic, Datadog, and Splunk and across multiple environments). Approaching detection engineering with such legacy processes brings several challenges:
- Slow rule deployments: The time critical task of developing, testing and deploying detection rules on the SIEM becomes slow and time-intensive.
- Poor rollback and recovery: Restoring or redeploying rules after failures is risky and error-prone. Backups are limited.
- Environment drift: Test, staging and production rules diverge, leading to missed detections and/or false positives.
- Limited testing and validation: Lack of automated testing reduces confidence in rule accuracy.
- No version control or visibility: Missing audit trails reduce authorship tracking, limit peer rule quality reviews and create knowledge silos within the organization.
- Inconsistent quality and scalability: Rule management does not scale across teams and platforms, especially as time brings in more rules and complexity.
- Weak change management: Limited structured approval and traceability makes it hard to trace rule updates to specific incidents and playbooks.
Solution
Maintaining the Detection as Code, storing them in Git repositories as YAML files or terraform configurations and defining appropriate tests to validate that they work are remediations to the challenges of legacy UI deployments.
- Build CI/CD pipelines that validate syntax and automated checks
- Require human peer review and context documentation for each rule before approving
- Test rules against simulated attack logs, or perform purple team exercises regularly to validate detection effectiveness or detection gaps
- Deploy to different environments automatically through a common source of truth using CI/CD pipelines
- Track metrics such as false positive rates per rule, time from commit to production deployment.
Impact
Teams gain detection engineering that they can reproduce and better manage: peer-reviewed and agreed rules, validated against attacks, deployed in a consistent manner across environments, tracked for effectiveness and audited properly. Incident response improves as playbooks deploy with detections. Purple team results directly drive detection development priorities.
- The deployment of new detections drops from days to hours, saving hundreds of human hours and speeding up security
- Version control enables better auditing and instant rollback or recovery from disasters
- Automated tests catch errors or misconfigurations before they end up on production
- Consistent rule format and documentation allow better visibility and knowledge sharing within the team
- Change management becomes frictionless and tied to semi-automated detection and response playbooks
- False positive rates decrease as rules are tested thoroughly by humans and automations
