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AI-assisted code is not automatically unsafe. The risk is shipping a change no one can explain, verify, or maintain. Treat an AI coding assistant’s output like any other proposed code: understand the diff, test it against the intended behavior, check its dependencies and security implications, and require human review before merge.
Why understanding the change matters more than who wrote it
A code suggestion is not proof that an implementation matches the business rules, handles edge cases, or protects sensitive data. An assistant may not have the broader business and algorithmic context needed to judge those things. GOV.UK guidance puts responsibility for resulting changes on the programmer and says, “You should only commit code changes that you understand.” GOV.UK guidance for developers in HMG
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This is a practical assurance principle, not evidence that AI-generated code is inherently insecure. The available sources do not establish a universal defect or vulnerability rate comparing AI-assisted code with human-written code across different tasks and settings. The useful question for a team is whether it can explain and verify the particular change it plans to ship.
What AI coding assistants do—and what the evidence says
Coding assistants are used for more than code completion. ANSSI’s 4 October 2024 summary of joint ANSSI-BSI guidance describes uses including generating code, becoming familiar with an unfamiliar codebase, writing tests, and producing documentation. It also warns that these tools bring security risks and should be approached cautiously. ANSSI: AI Coding Assistants
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A 2024 qualitative study by Jan H. Klemmer and colleagues combined 27 semi-structured interviews with software professionals and analysis of 190 relevant Reddit posts and comments. The authors describe professionals using AI assistants for security-critical work despite concerns about security and quality, and recommend critically checking suggestions. These figures describe the study’s inputs; they are not estimates of how common a practice is among all developers, nor do they show that AI assistance caused a particular defect. Klemmer et al., “Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns”
NIST’s SP 800-218A, published in July 2024, adds AI-specific practices to the Secure Software Development Framework (SSDF 1.1). It is intended 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 secure-development framework, not a claim that any one review step can certify a change as safe. NIST SP 800-218A
How to review AI-generated code before shipping it
Use the same standard you would apply to a consequential code change, regardless of whether a person or an assistant drafted it. The following sequence turns the guidance into a practical review.
- Ask for an explanation. Have the developer describe what each meaningful change does, why it is needed, and how it fits the relevant requirements. If the author cannot explain a part of the change, pause and investigate rather than treating a plausible explanation from the assistant as verification.
- Read the diff in small units. Keep commits small and specific so a reviewer can follow the change. Check behavior against the project’s requirements, especially edge cases, authorization boundaries, input handling, error paths, and possible exposure of data.
- Test the behavior that motivated the change. Run the relevant automated tests and add tests for the intended behavior and important edge cases. GOV.UK recommends sufficient test coverage; passing tests are evidence about the cases they exercise, not proof that every failure mode is covered. GOV.UK guidance for developers in HMG
- Verify dependencies independently. Check package names and versions against trusted registries and official documentation. A version that looks plausible may not exist or may not be the version the project needs; GOV.UK specifically warns that assistants can hallucinate dependency versions.
- Run security checks and investigate findings. Use the team’s static analysis and vulnerability-scanning tools as additional layers. A clean scan does not establish that code is safe, and a finding needs review to determine its relevance and appropriate fix. GOV.UK recommends supplemental scanning and names Snyk Code, Aikido, Checkov, and tfsec as examples; that mention is not an endorsement or confirmation of their current features or availability. GOV.UK guidance for developers in HMG
- Require independent human review before merge. Protect the main branch and ensure a reviewer with enough time and authority can block a change they do not understand or consider unsafe. GOV.UK states: “Merges made to the main branch need to be subject to human peer review by one or more peers, and adhere to your organisation’s policies.” GOV.UK guidance for developers in HMG
Keep assistant access separate from production risk
Review is only one part of assurance. GOV.UK warns that a coding assistant may have access to workspace content and that secrets placed in an assistant-accessible workspace may be uploaded to the inference service. Keep production secrets out of development workspaces assistants can access, and strictly separate and audit any access to production secrets.
Separate development from production changes and use a multi-stage deployment process. These controls limit the chance that an unreviewed development change or exposed credential can directly affect production. Apply the organization’s security policies to tools and environments rather than assuming the assistant’s interface provides protection.
What good team controls look like
A review process works only if people can use it effectively. For each consequential change, check whether:
- A developer can explain what the change does and why it is needed.
- The diff is small and specific enough for a thorough review.
- Relevant tests, static analysis, and vulnerability scanning have been run, with findings investigated.
- New or changed dependencies have verified names and versions from trusted sources.
- Assistant-accessible development workspaces do not contain production secrets.
- The main branch is protected, peer review is required, and reviewers can block a merge.
- Deployment separates development from production and proceeds through appropriate stages.
These safeguards are not a guarantee that defects will never reach production. They give a team practical ways to detect misunderstandings and limit their consequences before a change is accepted.
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The sources serve different purposes. GOV.UK provides operational recommendations for developers in HMG, including peer review, protected branches, testing, scanning, dependency checks, secrets handling, and staged deployment. NIST SP 800-218A is a broader AI-specific secure-development profile to use alongside SP 800-218. ANSSI’s page summarizes the scope of joint guidance but does not, by itself, substantiate more detailed controls than those described there.
eu-LISA’s Technology Monitoring Report page, dated 7 September 2026, says coding assistants may support productivity while emphasizing regular evaluation of tools and adequate resources to review generated code. That is a reason to make review capacity part of adoption planning: a team should not increase the volume of changes without ensuring people have time to assess them. eu-LISA: Technology Monitoring Report – Generative AI in Software Development
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