The CI/CD pipeline for a high-risk AI system enforces compliance at every stage, from static analysis through deployment. Static analysis extends conventional linting with AI-specific Semgrep rules that detect demographic feature handling violations, hardcoded thresholds, missing logging, and model registry bypasses.

Unit testing covers every layer of the eight-layer reference architecture, from data pipeline boundary cases through explainability coverage and human oversight interface bypass prevention. Integration testing validates end-to-end inference paths, regression against golden datasets, and system resilience under load and fault injection.

Model validation gates enforce four non-negotiable quality checks: performance, fairness, robustness, and documentation completeness. Automated documentation generates model cards, test reports, SBOMs, and AISDP section updates per build. Compliance-gated deployment requires all four gates plus human approval before production promotion, with canary or shadow deployment phases and immutable deployment ledger entries. The section concludes with the artefacts produced.

Note:

This section corresponds to the CI/CD Pipelines section and feeds primarily into AISDP Module 2 (Development Process) and Module 5 (Testing and Validation).