Impact
Increased consistency in application security review.
Impact
Improved signal quality by focusing detections on meaningful exploit paths.
Impact
Reduced manual effort in early-stage code triage and prioritization.
Deliverables
- Rule-driven scanning pipeline connected to CI context.
- Match enrichment with severity weighting and remediation guidance.
- Developer-facing reporting outputs for review and remediation flow.
References
Artifacts
- CI scan pipeline artifact slot reserved for upcoming diagram
Problem
Security review processes often become bottlenecked when risky code behaviors are only caught during manual review or after deployment-stage testing.Approach
- Combine custom rules, repository metadata, and severity weighting into a repeatable code scanning pipeline.
- Focus detection logic on exploit-relevant behaviors rather than generic lint-style findings.
- Export prioritized reports suitable for engineering review and remediation tracking.
Architecture / Workflow
- CI pipeline collects changed code context and routes it through rule packs.
- Analysis layer enriches matches with repository metadata and remediation guidance.
- Reporting service publishes outputs for developer workflows and security review queues.
Tools and Technologies Used
Python, Semgrep, CodeQL, GitLab CI, Docker
Results / Impact
- Increased consistency in application security review.
- Improved signal quality by tuning detections around meaningful exploit paths.
- Reduced manual effort for early-stage code triage.
Key Technical Takeaways
- Security scanning fails when outputs are too noisy for engineering teams.
- Context enrichment is critical for prioritization.
- Rule maintenance has to track how codebases actually evolve.