CQ-AI: Deterministic Security Scanning with Ternary Polarity

Deterministic security scanning analyzes code with reproducible results using seeded algorithms. Run CQ-AI with a fixed seed value to obtain consistent findings across multiple scans, enabling auditable vulnerability detection and risk prioritization in multi-language codebases.

Ternary polarity applies GF(3) mathematical mapping to classify security findings into three severity states: Critical, Medium, and Info. This tri-state model provides deterministic severity ordering that remains consistent regardless of scan execution order.

Yes. CQ-AI supports parallel split-stream scanning that decomposes work across multiple processors while composing results out-of-order without losing determinism. SplitMix64 seeding ensures findings remain reproducible even when scan order varies.

Out-of-order composition allows security findings to aggregate and deduplicate regardless of when individual scans complete. This supports distributed, collaborative scanning where multiple processes contribute results asynchronously while maintaining deterministic final output.

MCP integration enables auditable prioritization and reproducible finding delivery by connecting CQ-AI's deterministic scanning pipeline to external systems. It ensures findings propagate with consistent ordering and severity classification across collaborative workflows.

Yes. CQ-AI applies to multi-language codebases for static analysis and vulnerability detection. Deterministic ordering and ternary polarity classification work uniformly across languages, producing consistent findings regardless of codebase composition.