A human peer should review and approve AI-generated code before it is merged. AI can help inspect a change, suggest tests, or flag potential defects, but it should not be the sole reviewer. The person submitting the code remains responsible for understanding what it does; teams need small, reviewable changes, tests, security checks, and human review rules for protected branches.
Who is responsible for AI-generated code?
The developer who submits the change is responsible for it, whether they wrote every line or used an assistant. UK Government guidance puts the standard 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.” It also says merges to the main branch need human peer review and must follow the organization’s policies. This is practical UK public-sector guidance, not a universal legal rule; teams should apply their own jurisdictional and organizational requirements. UK Government developer guidance
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That responsibility is not satisfied by accepting an AI assistant’s explanation or a second model’s approval. The submitter should be able to explain the change’s behavior, assumptions, dependencies, and likely failure modes. A reviewer should independently assess whether the patch is correct and appropriate for the product.
Why not let another AI approve the change?
AI review can be useful as an additional check, but the available evidence does not establish it as a substitute for human review. A 2026 arXiv preprint examined 1.02 million pull requests across 207 GitHub projects. It found that some agent-involved review patterns were associated with faster decisions, but those efficiency gains did not correspond to better review quality. The study reports associations, not proof that AI agents cause faster reviews or preserve quality in every project. Zhong, Noei, Adams, and Zou, “From Human-Centric to Agentic Code Review”
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Security checks show the same distinction between assistance and assurance. A 2026 study analyzed 2,315 snippets from the DevGPT dataset. Static scanners flagged candidates in 114 files; manual review confirmed 56 vulnerabilities across 48 files. In experiments on those confirmed issues, models detected and repaired many, but not all. The study’s selected C, C++, and C# snippets and experimental setup do not establish how often AI-written code is vulnerable in general, or guarantee performance on another codebase or tool version. Empirical Software Engineering, “Secure coding with AI – from detection to repair”
For example, in the paper’s October 2024 experiment, GPT-4o detected 18 of 32 confirmed vulnerabilities and resolved 17. In the authors’ September 2025 tests, GPT-4.1 detected 82% and fixed 75%; GPT-5 detected and fixed 78.6%; and Claude Opus 4.1 detected 80.4% and fixed 76.8%. These are results on that paper’s selected issues, not general real-world detection guarantees. They show why a model’s finding—or silence—should be treated as one input rather than a final security verdict.
How to make AI-assisted changes reviewable
- Understand the proposed change. Before committing, inspect the code and check its behavior, assumptions, and dependencies. If you cannot explain it, do not ask a reviewer to approve it yet.
- Keep the patch small and specific. Split broad generated work into focused commits or pull requests. UK Government guidance recommends low-volume, specific changes so reviewers can follow the work and avoid overload. UK Government developer guidance
- Require a human peer review for protected branches. Configure branch protection so the main branch cannot receive a change without the required human approval, following your organization’s review policy. An automated check may be another required gate, but it should not be the only one.
- Run relevant tests and security checks. Use the project’s tests and appropriate security tooling, including static analysis where it fits. A clean scan is a useful signal, not proof that the change is safe; the cited security study found that both automated detection and model repairs could miss confirmed issues.
- Use AI review to broaden attention, not transfer accountability. A reviewer can ask an assistant to identify suspicious logic or suggest test cases. A human must decide whether each finding is valid and whether the change meets the project’s product, security, privacy, and maintainability needs.
What should teams do about review capacity?
Review is part of the cost of adopting code-generation tools. eu-LISA’s July 9, 2026 report highlights the need to evaluate tools and ensure sufficient resources to review AI-generated code. That means accounting for reviewer time and queue pressure when a team increases code output; the report does not specify a universal reviewer-to-code ratio. eu-LISA, “Generative AI in Software Development”
A Black Duck report published in 2026 describes an independent UserEvidence survey of 831 software engineering and DevOps professionals conducted in March 2026. Respondents named manual review as a bottleneck at 52% and security testing at 51%. These are reported experiences from a survey, not measured industry-wide review times or a controlled study. Black Duck, “The State of AI-Powered Software Development”
Organizations can also use broader secure-development guidance to shape governance. NIST SP 800-218A is an SSDF Community Profile that adds practices for AI model development and related systems and complements SP 800-218. It provides relevant governance context, but it is not a universal checklist for reviewing every AI-generated patch. NIST SP 800-218A
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can better prompts make generated code safer?
Yes, security-focused instructions can improve the chance of correct and secure output, but they do not guarantee it. OpenSSF’s guidance says, “Assistants will still make mistakes, but better prompts make a difference.” Use clear instructions about security requirements and expected behavior, then verify the result with tests, checks, and human review. OpenSSF guidance on AI code assistant instructions
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