Case Study
Detection Engineering Framework
Established a governed detection lifecycle with ATT&CK alignment, quality checks, and production tuning standards.
Detection UpliftDetection Engineering
Problem
Detection content was ad-hoc, noisy, and lacked ownership, resulting in analyst fatigue and missed high-value behavior.
Architecture Overview
Implemented a pipeline for use-case intake, rule design, validation, deployment, tuning, and retirement with measurable quality controls.
What Was Designed
- Detection lifecycle governance model
- Standardized detection metadata and severity schema
- QA validation and tuning workflow
- Analyst feedback loop for precision improvements
Impact
40% faster rule deployment
28% reduction in false positives
Improved detection confidence across SOC teams
Need Similar Architecture or Detection Modernization?
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Tech Stack
SigmaKQLSIEMPythonGitHub Actions
View Source on GitHub