Detection-as-Code Tools is a repository designed to standardize the creation, validation, and deployment of detection rules across multiple security platforms. Itprovides a structured, automation-ready framework that enables security engineeringteams to manage detections as version-controlled code artifacts improvingconsistency, traceability, reviewability, and operational scalability.

The repository supports rule definition using TOML for metadata and configuration, Sigma for detection logic, and Python-based tooling for validation and SIEM deployment.

By adopting this approach, teams can move from ad-hoc, platform-specific rulemanagement to a repeatable, auditable, and automated detection engineeringworkflow.

 

Core Concepts

The repository is built around the following principles:

• Detection-as-Code: Detections are treated as code artifacts (versioned, reviewed, tested, and deployed like software).
• Platform abstraction: Sigma rules provide vendor-agnostic detection logic thatcan be converted into SIEM-specific query languages.
• Automation-ready: Designed to integrate into CI/CD pipelines for validation, packaging, and deployment.

 

Detection-as-Code

All detection logic and metadata are stored as code. This allows teams to:

• Use version control for detections
• Track changes over time (diffs, history, authorship)
• Enforce peer reviews and approvals
• Standardize structure and required fields
• Integrate detections into CI/CD pipelines

 

Platform Abstraction (Sigma)

Detection logic is written in Sigma, an open, platform-agnostic detection rule format. This enables:

• A single source of truth for detection logic
• Portability across SIEM platforms
• Reduced vendor lock-in
• Faster multi-platform deployment

Sigma rules can be translated into platform-specific queries during deployment.

 

Automation-Ready

The tools in this repository are structured to support automation at every stage of thedetection lifecycle:

• Automated validation of metadata and schema requirements
• Guardrails to prevent broken rules from shipping
• CI/CD compatibility for scalable rule management
• Deployment scripts that push content to target platforms

 

Typical Workflow

A standard detection engineering workflow using this repository looks like:

1. Create or update a Sigma rule in sigma/.
2. Create or update the corresponding TOML metadata/config in rules/.
3. Run validation using validator.py to ensure the rule is structurally correct and references are valid.
4. Commit changes to version control (with review/approval as needed).
5. CI/CD pipeline runs validation and excludes invalid or broken rules.
6. Deployment scripts (elasticuploader.py, splunkuploader.py) push updates to theSIEM(s).

 

Benefits

Using this repository provides several operational advantages:

• Consistency at scale: standardized rule structure and metadata requirements
• Auditability: full history of changes, reviews, and ownership via git
• Portability: Sigma logic enables vendor-agnostic detections
• Lower operational risk: validation prevents broken/malformed rules fromdeploying
• Faster deployments: automation reduces manual work and turnaround time
• Better collaboration: rules can be reviewed like code and improved iteratively
 

Contact Us

If you’re interested in implementing a full-scale Detection-as-Code program, needenterprise-grade detection content, or want to learn more about automateddetection pipelines, visit our Detection-as-Code offering: https://blackcell.io/detection-as-code-dac/

Our DaC Feed provides continuously updated, production-ready detection contentdesigned for modern security operations.

Author

Gábor Lázár

L2 SOC ANALYST / ESM ENGINEER

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