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Set review depth by risk, not by how the code was written
NIST’s Secure Software Development Framework (SSDF) provides a useful foundation: prepare the organization, protect the software, produce well-secured software, and respond to vulnerabilities. NIST describes it as a basis for planning and continuous improvement, not a universal checklist. Apply the practices in proportion to the application’s purpose, criticality, risk tolerance, and available resources.
The following tiers are a practical way to make that decision; they are not an official NIST scoring system.
| Change context | Review scope | Evidence to expect before release |
|---|---|---|
| Routine, isolated change with limited impact | Review the diff, affected behavior, nearby code, dependencies changed, and relevant tests. | A human reviewer understands the change; relevant checks run; findings are resolved or explicitly accepted. |
| Change touching sensitive data, access control, external services, or important business rules | Trace affected data and trust boundaries through validation, authorization, logic, storage, integrations, and errors. Inspect configuration and deployment implications. | Independent review of security-sensitive behavior, relevant tests or analysis, dependency checks, and an accountable approval. |
| New application, major release, or change affecting critical assets or production control | Review the application baseline as well as the diff: architecture, entry points, privileged operations, dependencies, build and deployment paths, and recovery behavior. | Broader security validation and testing, documented risk decisions, provenance and approval, and a release path that preserves established controls. |
For any tier, first identify the assets that matter, the requirements the change must meet, the boundaries between trusted and untrusted components, and any previous findings that apply. A small diff can still carry high risk if it changes authorization, handles secrets, or introduces a new external integration.
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Keep a human owner responsible for the change
Assign a named developer who can explain the change and is accountable for its correctness, security, and maintenance. That person should review and approve it before merge, rather than treating an AI-generated explanation, code review, or test result as approval. OWASP’s Secure Coding with AI Cheat Sheet likewise calls for every AI-assisted change to be reviewed, approved, and attributable to a responsible developer.
Record enough provenance to answer who approved the change and, where available, which AI tool and model version contributed. Fit that record into the team’s normal change history and approval workflow. Attribution makes ownership traceable; it does not by itself prove code quality.
Rank #2
Trace the risky paths in the code
Do not review only the generated function or the lines changed. Follow important data and control paths across the application, including existing code the change relies on.
- Inputs and validation: Find where user, file, network, and service inputs enter. Check that validation is appropriate to the operation and that untrusted values cannot bypass it.
- Authentication and authorization: Verify who can perform each sensitive action and whether access is checked at the point where the action occurs. Check that identity and permission assumptions hold across service boundaries.
- Business logic: Compare behavior with requirements and expected edge cases. Manually inspect context-specific rules, such as ownership, limits, state transitions, and exceptional cases; automated analysis may not understand the intended policy.
- Storage and cryptography: Inspect what is persisted, how sensitive values are handled, and whether cryptographic operations and key handling fit the application’s established design.
- External calls and errors: Check destinations, data sent, timeouts or failure handling where relevant, and whether error paths expose sensitive information or leave state inconsistent.
- Configuration and deployment: Review new permissions, environment settings, secrets handling, build steps, and deployment changes. A safe code diff can still be undermined by an unsafe configuration or release path.
Use tests and automated analysis as evidence, not a verdict
Combine human review with checks that fit the change: static analysis, dynamic analysis, dependency checks, and tests. Triage the results rather than assuming every alert is valid or every clean report is conclusive. Verify that fixes address the underlying issue and do not introduce a different failure.
Passing tests show that tested cases passed under the conditions of the test. They do not prove the absence of security flaws. OWASP cautions against trusting AI-generated test suites without scrutiny and against using test pass rate alone as a confidence measure.
- Check whether tests cover the changed behavior and meaningful failure or boundary cases, not just the expected success path.
- Independently inspect security-critical tests and assertions. Confirm they would fail if the relevant protection were removed or bypassed.
- Investigate failures and warnings in context; document a risk decision when a finding is accepted rather than silently ignoring it.
Check dependencies and what the AI tool can see
Confirm that newly introduced packages are real, appropriate for the project, maintained, and configured at expected versions. Review lockfiles and build changes as well as source imports. A coding tool may not know about vulnerabilities disclosed after its training cutoff or its latest security-index update, so independently check dependency risk with current project processes.
Rank #4
Also review the tool’s data boundary. Establish what files, terminal output, credentials, personal information, or proprietary context are sent to the provider, and exclude sensitive context where possible. Do not expose secrets to a coding assistant simply because they appear in a local file or command output.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check reliability and maintainability against the product requirements
There is no validated universal rubric for “vibe-coded” reliability or maintainability in the cited guidance. Review these as ordinary product qualities: whether the software meets its requirements, preserves intended behavior, fits the project’s architecture, and can be understood and maintained by the team.
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- Walk through expected behavior and important edge cases, including how errors affect state and what happens when dependencies or services fail.
- Check that configuration is understandable, dependencies have a clear purpose, and the implementation follows the project’s established design rather than adding avoidable complexity.
- Look for tests and observability appropriate to the behavior, so the team can detect and diagnose problems after release.
- Ask whether another maintainer can explain the logic and change it safely. If not, simplify or document the non-obvious parts before accepting the change.
Keep AI agents inside the normal release controls
NIST’s DevSecOps reference model says AI-generated outputs should pass through established processes, including peer review, security validation, automated testing, and approval. It identifies risks including inaccurate outputs, insecure code, unauthorized actions, data leakage, and artifacts entering the supply chain without provenance or approval.
Accordingly, do not let an agent independently deploy or alter production state outside the team’s established authorization and release controls. Apply the same review, validation, testing, approval, and traceability requirements to agent-produced changes as to other changes.
Which NIST guidance applies to AI-assisted code?
NIST SP 800-218 version 1.1 is the final SSDF guidance published on February 3, 2022. NIST’s publications listing identifies SP 800-218 Rev. 1, version 1.2, as an initial public draft dated December 17, 2025; that draft should not be described as the final version.
NIST SP 800-218A, published in July 2024, adds practices for generative-AI and dual-use foundation-model development, including extending code-review and analysis policies to AI-model code and related components. It can inform AI-specific practice, but its scope is model development; it is not a bespoke standard for every application built with a coding assistant.
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