The Tool Desk
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AI-assisted code is not automatically unsafe. The risk is shipping a change nobody can explain, verify, or maintain. Treat every suggestion as untrusted until a developer understands what it does, tests it against the project’s requirements, and gets meaningful human review.
Why understanding the change matters more than how it was written
A coding assistant can generate code, help explore an unfamiliar codebase, write tests, or produce documentation. But a plausible suggestion is not proof that the implementation follows business rules, handles edge cases, or protects sensitive data. The UK government’s guidance says an assistant may lack the broader business and algorithmic context, and places responsibility for resulting changes on the programmer. GOV.UK guidance for developers recommends accepting only changes the developer understands.
This is a software-assurance principle, not evidence that AI assistance itself causes vulnerabilities. The guidance supports careful review and layered safeguards; it does not establish a universal defect or vulnerability rate comparing AI-assisted code with human-written code.
A practical review sequence before merging
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Ask for an explanation of the change
Have the developer explain what each meaningful part does, why it is needed, and how it satisfies the requirement. If nobody can explain the behavior, pause the merge and resolve that uncertainty first.
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Review the diff in small, specific units
Read the actual changes rather than relying on a summary from the assistant. Check whether the implementation matches the requirement, including edge cases, authorization boundaries, input handling, error paths, and possible exposure of data. GOV.UK advises small, specific commits because they are easier to review.
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Test the behavior that motivated the change
Run relevant automated tests and add tests for the intended behavior, including important failure cases. Test coverage is one layer of assurance; a passing test suite does not by itself establish that the change is correct or secure.
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Verify every new dependency
Check package names and versions against trusted registries and official documentation. GOV.UK warns that coding assistants can hallucinate dependency versions, so a plausible-looking package suggestion should not be accepted without verification.
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Run static analysis and vulnerability scans
Use the team’s established static-analysis and vulnerability-scanning tools, then investigate findings. A clean scan is useful evidence, but it is not proof that the code is safe or that it meets business requirements.
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Require independent human review and controlled deployment
Protect the main branch and require peer review before merging. Keep production secrets out of development workspaces accessible to assistants: GOV.UK warns that workspace content, including secrets, may be uploaded to an inference service. Separate development from production changes and deploy through multiple stages.
Make review a team control, not a checkbox
A review policy works only if reviewers have enough context, time, and authority to block a change. Teams can use these questions to assess whether their process is meaningful:
- Can the author explain the code and its intended behavior?
- Is the diff small enough for a reviewer to inspect carefully?
- Do tests and scans cover the changed behavior and relevant risks?
- Are dependencies verified against trusted sources?
- Are secrets and production access separated from assistant-accessible development work?
- Are branch protections, independent review, and staged deployment actually enforced?
GOV.UK says main-branch merges need human peer review by one or more peers and should follow organizational policy. That makes review an accountable decision, not an approval generated by the coding assistant.
What the evidence does—and does not—show
NIST’s SP 800-218A, published in July 2024, adds AI-specific practices to the Secure Software Development Framework (SSDF) for producers of AI models, producers of AI systems that use those models, and acquirers. NIST says to use it together with SP 800-218; it is a development-practices profile, not a finding that all AI-generated code is insecure.
Best Value
A 2024 qualitative study by Jan H. Klemmer and coauthors combined 27 semi-structured interviews with software professionals and a review of 190 relevant Reddit posts and comments. The authors reported that participants used AI assistants for security-critical work despite security and quality concerns, and recommended critically checking suggestions. Those figures describe the study’s inputs; the interview-and-forum design is not a population-wide estimate or a causal experiment. Read the study.
ANSSI’s 4 October 2024 page describes uses including code generation, familiarization with unfamiliar codebases, test writing, and documentation, while noting security risks and advising caution. ANSSI’s overview of AI coding assistants. Separately, eu-LISA’s 7 September 2026 report page says coding assistants may support productivity and emphasizes regular tool evaluation and adequate resources to review generated code. eu-LISA’s report page.
Together, these sources support a practical standard: evaluate the code that will ship, preserve human accountability, and use testing, scanning, dependency checks, access controls, and deployment safeguards as complementary layers.
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