This project implements a Detection-as-Code framework for building, validating, version-controlling, and maintaining scalable security detections across enterprise environments.
Detection rules are managed using structured engineering workflows and automatically validated through CI/CD automation to ensure operational reliability, consistency, and deployment readiness.
The project reflects practical detection engineering methodologies used by mature enterprise security teams to improve detection quality, reduce false positives, and maintain sustainable security content governance.
- Develop reusable security detections
- Automate detection validation workflows
- Reduce false-positive noise
- Maintain version-controlled detection logic
- Simulate enterprise rule deployment processes
Detection coverage includes:
- Encoded PowerShell execution
- Service persistence creation
- Privilege escalation behavior
- Suspicious child process spawning
- Administrative misuse indicators
- Process chain anomaly detection
Detection logic is created using Sigma-based standardized rule structures.
Rules are automatically tested against validation pipelines to ensure syntax and operational integrity.
Detection performance is measured against:
- Precision
- Recall
- False-positive rates
- Operational effectiveness
Validated detections are simulated through automated deployment workflows.
- Sigma Detection Rules
- GitHub Actions
- Python Validation Automation
- CI/CD Pipelines
- YAML Detection Standards
- Total Rules Tested: 24
- Validation Success Rate: 91.6%
- False Positive Reduction: 17%
- Detection Stability: High
Automated validation significantly improved detection consistency while reducing deployment errors and operational alert fatigue.
Detection tuning improved analyst trust in alert quality and strengthened triage confidence.
- Detection rule library
- Automated validation pipelines
- CI/CD workflows
- Detection quality assurance reports
- Governance documentation
- Rule lifecycle tracking