A Checklist for Making AI-Generated Code Audit-Ready

Audit-ready AI-generated code requires a traceable connection between approved requirements, acceptance criteria, verification evidence, reviewer approval, the merged revision, and the deployed artifact, enabling others to reconstruct why a change was shipped. Passing tests alone are insufficient because they may omit critical scenarios, use unrealistic mocks, or be altered to match flawed implementations, making verification against explicit business intent and edge cases essential. Teams should link tickets and requirement changes to pull requests, preserve relevant generation provenance while protecting sensitive data, tie decisions to exact commits, and retain evidence for each criterion. Repeated review concerns can be encoded as reusable invariants, while scenario testing, structural code scans, and clearly distinguished confidence-based LLM checks can provide different forms of evidence. An effective audit trail may span existing issue trackers, CI logs, version control, and deployment systems, but it must preserve the relationships among requirements, tests, approvals, exceptions, rollbacks, and production environments; this engineering practice is distinct from formal compliance programs such as SOC 2.

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