What is Detection as Code?

Detection as Code (DaC) is a modern cybersecurity practice that treats threat detection logic in the same way as software development. It involves writing detection rules in a structured, version-controlled, and testable code format, such as YAML or Python. This enables security teams to apply software engineering principles, such as version control, code review, automated testing, and continuous integration/continuous deployment (CI/CD) pipelines, to manage and deploy their detection capabilities efficiently.

How Detection as Code Works?

1. Define Detections as Code:

Security Engineers write detection rules as code (e.g., YAML, Python, or query languages). This code specifies what to detect and what the potential actions will be.

2. Version Control (Git):

All detection code resides in version control systems, such as Git. This ensures change tracking, collaboration, code reviews, and easy rollbacks.

3. Automated Testing (CI/CD Pipeline):

A CI/CD pipeline automatically tests new or updated detection code for syntax, functionality (using unit tests), and conflicts (through integration tests) before deployment.

4. Automated Deployment:

Once tested and approved, the CI/CD pipeline automatically deploys detections to security tools (SIEM, EDR), reducing errors and speeding up implementation.

5. Monitoring and Iteration:

After deployment, detections are continuously monitored. Feedback from alerts informs ongoing refinements, creating a cycle of improvement.

Common Tools Supporting Detection as Code

1. Version Control Systems (VCS):

Git (GitHub, GitLab, Bitbucket): Essential for storing, managing changes, and collaborating on detection code.

2. Detection Rule Formats and Converters:

Sigma: An open-source, generic format to write rules once, then convert them for various SIEMs/EDRs, ensuring portability. PySigma / Sigma CLI: Tools for automated Sigma rule conversion in pipelines.

3. Security Information and Event Management / Security Data Platforms:

Splunk, Microsoft Sentinel (KQL), Elastic Stack, and Panther: Core platforms that execute detections, increasingly offering APIs for Data Collection (DaC) integration.

4. Endpoint Detection and Response (EDR) Platforms:

Microsoft Defender for Endpoint, CrowdStrike Falcon: Provide endpoint telemetry and allow programmatic management of detection logic via APIs.

5. Infrastructure as Code (IaC) Security Scanners:

Checkov, KICS: Tools for scanning infrastructure code (e.g., Terraform) to find misconfigurations, extending DaC to infrastructure.

6. Static Application Security Testing (SAST) Tools:

SonarQube, Semgrep: For application code, they define security rules that align with DaC principles for app vulnerabilities.

7. CI/CD Pipeline Tools:

Jenkins, GitLab CI/CD, GitHub Actions: Orchestrate the automated testing and deployment of detection code from version control systems (VCS) to security tools.

8. Security Orchestration, Automation, and Response (SOAR) Platforms:

Splunk SOAR, Microsoft Sentinel Playbooks: Automate responses to alerts, with playbooks often defined as code and managed via DevOps Automation (DaC).

9. Attack Simulation / Breach and Attack Simulation (BAS) Tools:

Piccus Security, AttackIQ: Simulate attacks to validate detection effectiveness and find gaps, feeding improvements back into DaC.

DFIR Training with InfosecTrain

Detection as Code (DaC) empowers security teams to build flexible, scalable, and maintainable threat detection systems by blending software development practices with cybersecurity operations. This approach helps organizations strengthen their defenses and stay ahead of evolving threats, proving essential as attacks grow more sophisticated. For more in-depth expertise, InfosecTrain's Advanced Threat Hunting and DFIR training offers hands-on experience in proactive threat detection, malware analysis, and incident response, including detection engineering, to effectively address complex cyber threats.