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Make human review a release gate, not a courtesy check. Before AI-assisted code reaches production, a qualified person must understand the change, inspect its full context, evaluate test and security evidence, resolve or explicitly accept findings, and approve the release. Keep the AI’s involvement and the approval evidence in the same engineering records used for other changes.
What human review is supposed to control
AI assistance can enter a software change in many ways: a developer may accept a suggested line, ask a model to draft a function or test, or use an agent that edits files and runs commands. Those modes do not transfer responsibility for the resulting code. The accountable release decision still belongs to people with the context and authority to judge the change.
Review is not simply checking whether generated code looks plausible. It is a controlled decision that the change satisfies a stated requirement, behaves acceptably when conditions are unusual or fail, fits the surrounding system, and has passed the required verification. A reviewer should be able to explain what the code does and why it is safe to release, rather than relying on the fact that a tool produced it or another AI system endorsed it.
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Set the rules before anyone uses an AI tool
A release process cannot reliably control AI use if the organization has not defined what use is allowed. Set policy before enabling tools in repositories or development environments. The policy should give developers, reviewers, security owners, and release managers an operational answer to what can be used, on what data, and with what degree of autonomy.
Maintain an approved-tool list
Identify the tools and configurations developers are permitted to use, and define the approval route for adding or changing them. Tool approval should be specific enough to distinguish a conversational assistant from an agent that can inspect a repository, run commands, or modify an environment. Record the tool identity or model context when it is available and meaningful; do not claim more precise attribution than the tool exposes.
Define data-sharing limits
State what source code, credentials, customer information, production data, and other sensitive material may be entered into each approved tool. Define restrictions for tools whose data handling has not been approved. Make prohibited uses explicit so developers do not have to infer the rule from a general instruction to “be careful.”
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Set autonomy boundaries
Specify whether a tool may only suggest text, edit local files, open or update a pull request, execute commands, or interact with deployment environments. The more authority the tool has, the more important it is to restrict access, monitor actions, and require approval before consequential operations. OWASP SAMM recommends policies that address approved tools, data sharing, autonomy, licensing, and AI-specific risks.
Define prohibited uses and ownership
List activities that are out of bounds under the organization’s policy, and identify who can approve exceptions. Assign responsibility for maintaining the policy, repository instructions, and review requirements. Tool output must not be treated as a substitute for a named human owner who can answer for the release decision.
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Make AI involvement visible in the work record
Record enough context for a reviewer to understand the change and its provenance. Put the requirement, risk classification, and a short AI-use disclosure in the issue or pull request. The disclosure should identify the role AI played in producing or changing code, tests, or configuration. Include tool or model context when available, without turning the description into a claim that the recorded tool account proves exactly how every line was generated.
The UK Home Office offers a practical pattern: mark commits or pull requests as AI-assisted and state that the author reviewed and amended the output. That distinction is useful because authorship of the final change and assistance used to produce it are separate facts. A disclosure should not imply that AI review constituted approval.
Keep the record in normal source-control and release systems where possible. Link the change to its requirement and preserve review decisions, CI results, security findings, and release artifact provenance alongside it. NIST’s notional reference model emphasizes traceable AI outputs, established SDLC gates, audit logs, and approval by accountable stakeholders before output is used as a deployment input.
Use a risk-based review process
Every AI-assisted change needs a qualified human reviewer. Review effort should rise with three things: how independently the reviewer can assess the author’s work, how much action the AI tool can take, and how large or irreversible the change’s potential impact is. This avoids two poor extremes: treating every trivial suggestion as a special incident, or letting high-impact agent changes pass through routine review without stronger evidence.
Match the reviewer to the risk
For ordinary changes, assign a reviewer with enough knowledge of the affected code and requirement to assess correctness. Add a security owner or domain owner when the change concerns authentication, authorization, cryptography, sensitive data, infrastructure, deployment configuration, or irreversible operations. A familiar reviewer who cannot evaluate the relevant security or domain behavior is not a substitute for the required expertise.
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Scale approval authority with autonomy and blast radius
Use the following decision model when choosing controls. It is a practical application of the four comparison axes identified in the guidance: review independence, automation depth, evidence strength, and blast radius.
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| Small, local code change with limited impact | A qualified reviewer inspects the author’s change | AI suggestions or edits that do not act on production | Review the complete diff and relevant test and scan results; use the existing pull-request approval gate |
| Change to sensitive or security-critical behavior | Qualified reviewer plus the relevant security or domain owner | Assistant or agent may have drafted or modified code | Check security validation, dependencies, failure modes, and requirement coverage; record the additional owner’s decision |
| Agent can execute commands or change environments | Human review must be independent of the agent’s own assessment | Agent performs actions beyond suggesting code | Restrict and log actions, verify the resulting diff and test evidence, and require human authorization for consequential steps |
| Production-facing or irreversible operation | Accountable release and relevant technical owners approve | Automation may affect deployment or operational state | Require strong traceability, staged deployment where applicable, monitoring, and a tested rollback or kill-switch path |
The table is a control-selection framework, not a claim that a particular review pattern guarantees a particular defect rate. No independent quantitative finding is established here for the effect of human review specifically on AI-assisted releases.
Follow the change from requirement to production
Human review works best when it is part of the normal pull-request and release path rather than a separate AI-only ritual. The sequence below places the human decision after independent verification and before production, then extends control through deployment and follow-up.
- Define the requirement and risk. Link the change to an issue or other clear requirement. Classify the risk and identify any sensitive areas, domain owners, or additional approval needs before implementation begins.
- Disclose AI assistance. In the pull request or commit record, say whether AI assisted with implementation, tests, review, or agent actions. Include available tool context and state that the author reviewed and amended the output when that is accurate.
- Inspect the full change. A human author or reviewer should examine the complete diff and the relevant surrounding code, not only the snippets highlighted by an assistant. Look for generated tests, changed configuration, dependency additions, and effects outside the immediate file.
- Run independent checks. Execute the project’s unit and integration tests, static analysis, dependency and software-composition checks, secret scanning, security tests, and policy gates as applicable. An AI system can help interpret a result, but deterministic checks remain authoritative for their configured pass/fail conditions.
- Resolve findings. Address review comments and verification failures, or record why a finding is explicitly accepted by an accountable owner. Do not treat a suggested fix as validated merely because an AI tool proposed it; inspect the follow-up change and rerun relevant checks.
- Obtain the required approvals. The qualified reviewer verifies behavior, failure modes, maintainability, dependency provenance, test adequacy, and rollback planning. Add security or domain-owner approval for the sensitive areas identified by policy.
- Release progressively and observe. Use a staged rollout, feature flag, or canary where appropriate. Monitor the change after deployment and keep a tested rollback path. When agents can run commands or modify environments, retain logs of their actions and tool calls.
- Feed outcomes back into controls. Review escaped defects, false positives, rejected suggestions, and incidents. Update policy, repository instructions, test cases, and checklists as needed. Protect instruction files with the same normal change-control practices used for other repository content.
What a reviewer should actually inspect
Approval should rest on evidence and understanding, not a general impression that the output looks polished. The author’s explanation, test results, and AI comments can help focus attention, but the reviewer remains responsible for making a reasoned decision.
Requirement and behavior
Compare the implementation with the linked requirement. Check both the expected path and relevant failure modes: what happens when input is invalid, a dependency is unavailable, permissions are insufficient, or an operation is retried? Confirm that generated tests meaningfully exercise the intended behavior instead of merely reflecting the implementation’s assumptions.
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Context and maintainability
Read the change in the context of neighboring code, interfaces, conventions, and callers. Look for duplicated logic, confusing abstractions, mismatched error handling, and unnecessary scope expansion. A syntactically correct implementation can still violate system assumptions or make later changes harder.
Dependencies and security
Inspect new and changed dependencies for provenance and purpose. Review authentication, authorization, cryptography, sensitive-data handling, secrets, infrastructure, and deployment changes with the expertise required by policy. Confirm that security scans and other applicable CI checks ran and that their results are visible in the change record.
Evidence and reversibility
Check that the stated tests and scans correspond to the version being approved, and that unresolved findings have an explicit disposition. For changes with operational impact, understand how the release can be paused, disabled, or reversed. A reviewer should not approve a production change if the recovery plan is unclear for the risk involved.
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AI review tools can identify possible bugs, suggest edits, summarize a diff, or help interpret a failing test. They can broaden the set of observations available to a human, but their conclusions are not proof that a change is correct. OWASP SAMM treats AI-based verification as a complement to deterministic checks, not a replacement.
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Make verification evidence easy to inspect in the pull request or change record. Record the outcomes and relevant failures rather than relying on an author’s summary alone. When a finding is waived, make the acceptance explicit and attributable to an owner with authority to accept that risk.
Use AI review assistants without delegating approval
GitHub documents Copilot code review for selected code and uncommitted changes, repository-wide and path-specific instructions, suggested edits, and feedback on review comments. In a pull-request workflow, this can serve as an additional source of observations: it may point a reviewer to a concern worth checking or suggest a possible edit.
Treat each AI comment as a claim to evaluate. The author or reviewer should verify whether the issue applies, make or reject the suggested change, and rerun relevant checks after edits. A comment does not count as a human review, and an AI-generated review cannot be the approval gate. Human approval, deterministic CI, and security ownership where required remain separate controls.
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Audit the process and improve it
Traceability is useful only if a later reader can reconstruct what was changed, what evidence was available, and who accepted the release risk. Retain the AI-use disclosure, tool identity or available context, review approvals, unresolved or waived findings, CI and security evidence, and release artifact provenance in the normal engineering record. For agents with command or environment access, include action and tool-call logs appropriate to the system.
Use incidents and near misses to improve controls rather than treating every failure as an isolated coding mistake. Review escaped defects, false positives, rejected AI suggestions, and failures in review or deployment. Determine whether the weakness was in the policy, reviewer assignment, test coverage, access boundary, approval evidence, or rollback readiness. Then adjust repository instructions, test cases, policy, and reviewer checklists through ordinary change control.
Human review checklist for an AI-assisted pull request
- Is the change linked to a clear requirement and a risk classification?
- Is AI assistance disclosed in the pull request or commit history?
- Has a human inspected the complete diff, surrounding code, generated tests, and new dependencies?
- Did CI run the applicable tests, static analysis, software-composition checks, secret scanning, and security validation?
- Are authentication, authorization, cryptography, sensitive-data, infrastructure, and deployment changes escalated to the appropriate owner?
- Are review comments resolved, or explicitly accepted by an accountable owner?
- Is there a rollback or kill-switch path for an agent or deployment where the change needs one?
- Are approval, verification evidence, available tool identity, and release artifact provenance retained?
What this process can and cannot establish
These controls establish accountable review, traceability, and independent verification practices for AI-assisted changes. They do not establish a guaranteed reduction in defects or a measured productivity improvement. The available guidance supports human scrutiny and established release gates as risk-management practice; it does not provide an independent quantitative defect-rate result specific to human review of AI-assisted releases. Avoid turning compliance with a checklist into a claim that the software is defect-free.
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