Direct answer: Secure AI code deployment means generated code is not shipped merely because it compiles or passes a narrow test. It is accepted only after evidence shows the change matches intent, avoids obvious shortcuts, and has an explicit risk profile.
The deployment risk
AI-generated code can introduce subtle security, reliability, and data-handling problems while still looking clean in a diff.
The safest deployment posture is to treat agent-created work as untrusted until a separate verification gate produces a reviewable receipt.
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Pre-merge evidence
Intent mapping, test strength, risky file detection, secret-handling checks, dependency awareness, migration risk, and explicit unknowns all belong in the acceptance path.
FeelGoot focuses on building that evidence trail for the human reviewer and the engineering system.
Security-sensitive workflows
Authentication changes, authorization rules, billing logic, customer data pipelines, infrastructure-as-code, release automation, and emergency fixes should receive a higher evidence threshold.