Your organization plans to let a generative-AI assistant create Bash scripts and Dockerfiles that will be merged into a production Git repository. To stay compliant with corporate policy and industry guidance on responsible AI, which practice should the DevSecOps team adopt when incorporating the assistant's output into the code base?
Merge the assistant's commits directly to production whenever the CI pipeline's unit tests succeed.
Perform a mandatory human code review and static-analysis scan of all AI-generated changes before they are merged.
Disable logging of the assistant's recommendations to prevent accidental disclosure of sensitive logic.
Improve the assistant's accuracy by retraining the public model on the company's entire private code base without legal approval.
Responsible-AI guidance from vendors and standards bodies stresses that large-language-model output can contain bugs or vulnerabilities and must be treated like untrusted third-party code. The safest approach is to keep humans "in the loop": subject every AI-generated file to normal peer review and automated security scanning before it reaches the main branch. Automatically merging code that merely passes unit tests, suppressing audit logs, or uploading proprietary code to retrain a public model would bypass verification or violate data-governance rules and therefore contradict best-practice recommendations.
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