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A human peer should review and approve AI-generated code before it is merged. The developer submitting the change remains responsible for understanding what it does, while AI tools can help surface possible defects, suggest tests, and broaden the review. They should not be the only gate between generated code and production.
Who is responsible for AI-generated code?
The person submitting the change is responsible for understanding its behavior, assumptions, and dependencies—even when an assistant produced much of the code. UK Government guidance puts it plainly: “You should only commit code changes that you understand. As a developer, you are fully responsible for the code you produce, whether this is AI assisted or not.” That is public-sector guidance rather than a universal legal rule, so teams should also follow their own policies and applicable jurisdiction.
Responsibility does not mean one developer must discover every possible defect alone. It means a human owns the decision to accept the change, and that decision should include peer review, testing, and security checks appropriate to the code. UK Government guidance says merges to the main branch need human peer review by one or more peers and should follow organizational policy. It also recommends branch protection.
Why another AI should not be the sole reviewer
An AI reviewer can be useful, but assistance is not proof of correctness. A 2026 study in Empirical Software Engineering examined 2,315 code snippets from the DevGPT dataset. Static scanners flagged candidate files; manual review confirmed 56 vulnerabilities in 48 files. In experiments on those confirmed issues, newer language models detected and repaired many—but not all—of them. The snippets were selected C, C++, and C# examples, and the results do not establish what share of all AI-generated code is insecure or how every current tool performs. Read the study and its methods.
The same paper reports varying results across its specific experiments. In an October 2024 experiment, GPT-4o detected 18 of 32 confirmed vulnerabilities and resolved 17. In September 2025 tests, GPT-4.1 detected 82% and fixed 75%; GPT-5 detected and fixed 78.6%; Claude Opus 4.1 detected 80.4% and fixed 76.8%. These are results on the paper’s selected confirmed issues and experimental setup—not general guarantees of real-world detection or repair.
A separate 2026 arXiv preprint analyzed 1.02 million pull requests across 207 GitHub projects. It found some agent-involved review patterns associated with faster decisions, but those efficiency gains did not correspond to better review quality. Because the results are associations, they do not prove that AI agents cause faster reviews or preserve quality in every project. Read the preprint.
So the useful distinction is not “AI review is worthless” versus “AI review is enough.” AI can point reviewers toward issues or help generate test ideas; a human still has to judge whether the findings are valid and whether the change is safe, appropriate, and maintainable.
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A practical review path for AI-written changes
- Understand the proposed change. Before committing, the submitter should be able to explain what the code does, what assumptions it makes, and which dependencies or interfaces it affects. If they cannot explain it, they are not ready to approve it.
- Keep the diff small and specific. Split a large generated result into focused commits or pull requests. UK Government guidance recommends low-volume, specific changes so reviewers can track the work and avoid overload. A reviewer should be able to connect each change to a clear purpose.
- Require human review before protected-branch merges. Configure branch protection so changes to the main branch cannot bypass the organization’s required peer approval. Set the number of approvers and any exceptions according to team policy rather than treating a tool’s approval as a human sign-off.
- Run the checks that fit the change. Execute the project’s tests and security checks, including static analysis where appropriate. Treat a clean result as one signal, not as proof that the change has no vulnerabilities; the 2026 security study’s confirmed issues were not all detected or repaired in its experiments.
- Use AI review as a second set of prompts, not the final verdict. Ask it to identify risky assumptions, edge cases, or tests worth considering. Then verify each finding against the code and requirements. The human reviewer remains responsible for product behavior, security, privacy, and maintainability.
- Make review capacity part of the rollout. More generated code still needs enough skilled review time. eu-LISA’s July 9, 2026 report highlights the need to evaluate tools and ensure sufficient resources to review AI-generated code. It does not prescribe a universal reviewer-to-code ratio, so teams should watch their own queue, delays, and review quality as adoption grows. See the eu-LISA report.
What secure-development guidance adds
Good instructions can improve the odds of useful output, but they do not transfer accountability to the assistant. OpenSSF says that clear, security-focused prompts can improve the chance of correct and secure output, while cautioning that assistants still make mistakes and need human guidance. Its practical takeaway is to state security requirements explicitly and then review the result. Read OpenSSF’s guidance on AI code assistant instructions.
For organizational governance, NIST SP 800-218A is an SSDF Community Profile that adds secure-development practices for AI model development and related systems, complementing SP 800-218. It is useful context for secure-development programs, but it is not a ready-made checklist for every AI-generated patch. Read NIST SP 800-218A.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why review workload matters
Code generation can increase the amount of material a team must inspect, so review capacity is part of adopting these tools responsibly. Black Duck’s 2026 report describes an independent UserEvidence survey of 831 software engineering and DevOps professionals conducted in March 2026: 52% named manual review and 51% named security testing as bottlenecks. These are respondents’ reported experiences, not measured industry-wide review times or a controlled study. Read the Black Duck report.
If review queues grow, the answer is not to remove the human gate by default. Teams can limit change size, prioritize risk, improve tests and automation, and ensure reviewers have time and context. A fast merge is valuable only when the review still meaningfully checks the change.
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