Detection as Code: Modernizing Threat Detection in Enterprise Security

In today’s fast-paced cyber landscape, organizations face an evolving array of threats. Effective detection is all about building scalable, reliable, and maintainable detection capabilities. This is where Detection Engineering and the emerging practice of Detection-as-Code (DaC) come into play.

This blog post introduces Detection-as-Code, explaining its principles, workflows, and value for security teams, particularly Managed Security Service Providers (MSSPs) and in-house Security Operations Centers (SOCs).

What is Detection Engineering?

Detection Engineering is the systematic practice of designing, developing, testing, and maintaining threat detection logic.

While the role may overlap with other security functions, it is distinct from:

• Configuring audit policies and generating telemetry
• Collecting and normalizing telemetry
• Building tools to apply detection logic to data

Detection engineers focus on creating reliable detection rules and processes rather than the broader infrastructure around them.

The Detection Development Life Cycle (DDLC)

Detection engineering follows a structured approach known as the Detection Development Life Cycle (DDLC), inspired by software development practices. The DDLC consists of six key phases:

1. Requirement Gathering – Define what threats need detection, assess risk, and establish success criteria.
2. Design – Identify data sources, relevant fields, and detection mapping to taxonomies while considering performance and evasion techniques.
3. Development – Implement detection rules using platform-specific languages (KQL, EQL, SPL) or agnostic formats (Sigma). Proper documentation is essential.
4. Testing & Deployment – Validate detections with replayed or simulated attack data, tune false positives/negatives, and deploy to production.
5. Monitoring – Continuously review and adjust detections to ensure ongoing reliability. Decommission outdated rules when necessary.
6. Continuous Testing – Automate tests and threat simulations to maintain detection resilience and accuracy over time.

Introducing Detection-as-Code

Detection-as-Code (DaC) brings software engineering principles to threat detection. It allows security teams to treat detection rules like code, applying version control, peer review, automated testing, and CI/CD workflows.

Key practices include:

• Version Control – Store detection rules in Git repositories for change tracking, rollbacks, and collaboration.
• Code Reviews & Pull Requests – Peer review ensures quality and shared ownership.
• Testing & Validation – Unit tests, syntax validation, and simulated attack scenarios guarantee optimal detection accuracy.
• CI/CD Pipelines – Continuous Integration and Deployment pipelines automate validation and delivery of detection rules.
• Standardized Formats – Use YAML, JSON, or other schemas with consistent fields (title, description, queries, references).
• Reusable Components – Shared filters and mappings improve readability and reduce duplication, enabling large-scale updates efficiently.

Why Detection-as-Code Matters

Adopting a DaC approach delivers tangible benefits:

• Collaboration – Peer reviews enhance shared understanding and accountability.
• Consistency – Standardized formats and taxonomies simplify maintenance and search.
• Quality – Peer review, syntax validation, and best practices enforcement improve detection reliability.
• Efficiency – Automation reduces manual effort in development and testing.
• Scaling – Structured processes enable rapid delivery across multiple environments.
• Improved Documentation – Metadata fields ensure every detection is well-documented and traceable.

Who Benefits from Detection-as-Code?

MSSPs gain scalable and consistent detection management across multiple clients, reducing operational costs while improving service quality.

In-house SOCs benefit from maintainable, well-documented detections, faster issue resolution, and enhanced security maturity. Continuous monitoring and testing help teams fine-tune rules without overwhelming analysts with false positives.

Moving Forward

Detection-as-Code is more than a methodology—it’s a strategic framework for bringing software engineering rigor to threat detection. By adopting DaC, security teams can scale, standardize, and automate detection workflows, driving measurable improvements in efficiency, accuracy, and resilience.

In the next installment of this series, we’ll explore practical implementation strategies, including CI/CD integration, automated testing, and large-scale deployment of detection rules.

Key Takeaways for Security Leaders:

• Treat detection rules like software: versioned, tested, and peer-reviewed.
• Use structured lifecycles (DDLC) to improve reliability and maintainability.
• Automate wherever possible to scale detection across multiple environments.
• Continuous monitoring and testing are essential to keep detection relevant and effective.