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Keep qualified humans accountable for every merge, but change how they spend review time. AI can produce a large, polished-looking diff whose uncertain and reliable parts have the same surface confidence. Require small, well-scoped changes; give reviewers the intended behavior and risk context; read the change from system intent to high-risk details; challenge generated tests independently; and scrutinize dependencies, build automation and security boundaries.
The goal is not to prove that AI-written code is always worse or to let an AI reviewer replace an engineer. It is to preserve the controls you already rely on while adapting the review workflow to faster, less socially transparent and sometimes much larger contributions.
What should stay the same—and what must change
The approval bar does not change because a model produced the code. The UK Home Office engineering standard requires AI-assisted code to be reviewed and approved by suitably qualified people before production, tested, traceable and subject to the same security expectations as human-written code. It also says teams must understand what they run. That is a UK departmental standard, not a universal law or a policy every employer has adopted; use it as a concrete example when writing your own policy (Home Office SEGAS-00020).
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|---|---|
| Human ownership of the decision to merge and release | Ask for the intent, assumptions, affected boundaries and AI assistance before opening the diff |
| Normal functional, security, performance and operational gates | Use a staged read: overview first, risk-based filtering second, detailed inspection third |
| Traceability from requirement to commit, pull request and test result | Split agent-sized output into coherent commits or pull requests that one reviewer can reason about |
| Qualified reviewers for sensitive components | Direct the most experienced attention to authentication, authorization, validation, cryptography, dependencies and delivery infrastructure |
| Independent evidence that tests exercise the requirement | Treat generated tests as untrusted proposals, not proof that the implementation is secure |
JetBrains researchers and Lund University describe this as a trust-calibration problem: an AI system can present dependable and faulty code with similar confidence and offers fewer social cues than a human author. Their proposed overview-to-details workflow came from a participatory design study with 17 practitioners and a follow-up survey of 43 software professionals. Those are sample sizes, not population estimates or measured defect-reduction effects, and the framework is a research proposal rather than a proven production policy (JetBrains Research, October 2026).
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Make the change reviewable before anyone opens the diff
Keep scope small enough to reason about
Large agent-generated changes hide unrelated edits, make rollback harder and exhaust reviewers before they reach the risky lines. Break a broad request into coherent pull requests or commits: for example, schema migration, service logic, API contract and user-interface change should normally be separable when the architecture permits. The UK National Cyber Security Centre advises small regular commits because large commits make effective review more difficult and small commits simplify review and rollback (NCSC clean-code guidance).
For work that cannot be split safely, attach a reviewed specification or explicit acceptance criteria. A reviewer should be able to state what behavior is intended without reverse-engineering it from generated code.
Require a context packet in the pull request
Ask the author to provide five short items:
- The user or system problem and the intended behavior.
- Components, data stores, external services and trust boundaries touched.
- Important assumptions, rejected alternatives and known limitations.
- Tests, static checks, migrations and manual checks that were run.
- Which portions were AI-assisted and which parts received direct human design or editing.
Disclosure wording is an implementation choice, not a universal mandate. The Home Office standard gives examples of AI-assisted commit and pull-request wording; teams can adapt that idea to their existing traceability system rather than forcing a new form for every repository (Home Office SEGAS-00020).
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Apply tool and data-handling rules first
Use only tools approved by your organization, and do not send restricted, personal, customer or proprietary information to a service without authorization. Approval, retention, training and permission settings differ by employer and tool configuration. Record the tool identity and relevant mode when your audit requirements call for it; do not assume a model’s chat history is a durable audit trail.
Define a review budget and escalation path
Assign a reviewer with enough domain and security expertise, and reserve time proportional to the change’s risk. If the reviewer cannot understand the behavior, reproduce the tests or inspect a new dependency, stop the review and escalate rather than approving on schedule pressure. The NCSC identifies suitable reviewer expertise and sufficient review time as conditions for effective review (NCSC guidance).
A staged review workflow for AI-assisted changes
1. Start with system intent, not individual lines
Read the requirement, acceptance criteria, architecture notes and pull-request summary before the diff. Ask whether the change solves the right problem, preserves invariants and fits the ownership model. Identify what must remain true at system boundaries: who may call an endpoint, what data may cross a service boundary, which component validates input, and who owns retries, timeouts and failure handling.
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At this stage, do not try to prove every implementation detail. Build a map of the change: entry points, state transitions, side effects, persistence, external calls and operational controls. If that map is inconsistent with the stated intent, return the change before line-by-line review.
2. Mark risk surfaces and uncertainty
Use the map to prioritize attention. The following areas deserve direct human inspection even when an automated review reports no issue:
| Area | Questions to answer |
|---|---|
| Authentication and authorization | Can an unauthenticated or lower-privilege caller reach the new path? Are object-level checks performed for every identifier? |
| Input and output validation | Are types, lengths, encodings, ranges and allowed values checked at the trust boundary? Is output escaped for its destination? |
| Cryptography and secrets | Are approved primitives, key lifetimes, nonce rules and secret-storage mechanisms used? Does logging reveal credentials or personal data? |
| Database and file operations | Are queries parameterized, migrations reversible where required, transactions correctly scoped and paths protected against traversal? |
| Dependencies | Why is each package needed? Is its license acceptable, maintained, transitive dependency set understood and version pinned according to policy? |
| Build, CI/CD and deployment | Did the change alter scripts or permissions that run in a trusted context? Could an untrusted value become a command, artifact or deployment input? |
| Concurrency and resilience | What happens under retries, duplicate messages, timeouts, partial failure, cancellation and simultaneous updates? |
OWASP specifically warns that coding agents may change build scripts, package scripts, CI/CD configuration and deployment infrastructure, all of which can execute with elevated trust. Review those files as production code, not as incidental scaffolding (OWASP Secure Coding with AI Cheat Sheet).
3. Filter and drill down instead of reading every line equally
For a broad diff, first identify files and hunks that implement the risk surfaces, cross a trust boundary or introduce unfamiliar behavior. Then inspect those sections in detail, requesting or generating explanations only where context is missing. Finally, verify the remaining low-risk edits for consistency, dead code and accidental scope expansion.
This overview, selective drill-down and details-on-demand sequence is the core of the JetBrains/Lund conceptual framework. It is a useful way to allocate scarce reviewer attention, but the study does not establish that it reduces defects or outperforms conventional review in production (JetBrains Research).
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4. Verify behavior at boundaries
Trace representative valid, invalid and adversarial inputs from entry point to side effect. Check error semantics, authorization decisions, transaction boundaries, idempotency and observability. Generated code often appears locally reasonable while violating an assumption held by a neighboring service or operational process. Compare the implementation with API contracts, schemas, feature flags, backwards-compatibility promises and runbook expectations.
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5. Inspect dependencies as design decisions
For every added or upgraded package, record its purpose and inspect its provenance, license, maintenance signals, vulnerability status and transitive footprint using the organization’s normal software-composition process. Confirm that the package is necessary and that its API, runtime cost and failure behavior fit the architecture. A dependency update bundled into an unrelated feature should be split or justified so that reviewers can assess it independently. The Home Office and NCSC guidance both support dependency scrutiny as part of maintainable, secure engineering (Home Office standard; NCSC guidance).
Review tests as independently authored evidence
Passing tests supplied by the same generation process do not demonstrate that the requirement was tested. Read tests as if they were a separate artifact with their own threat model.
Look for tests that disappeared or became weaker
- Compare deleted tests and changed assertions, not only newly added test files.
- Reject assertions that were replaced with snapshots, broad truthiness checks or “does not throw” claims when a precise result is required.
- Check whether mocks have replaced the real parser, database constraint, authorization layer or network failure that matters to the requirement.
- Watch for tests that merely encode the implementation’s current behavior instead of the externally required behavior.
Add cases the model is unlikely to volunteer
Write or independently verify negative, boundary and adversarial cases: malformed and oversized input, missing fields, duplicate requests, expired or revoked tokens, cross-tenant identifiers, clock skew, concurrent updates, partial outages, rollback, permission changes during a session and unexpected encodings. For security-critical behavior, a qualified engineer should author or independently verify the test rather than accepting a generated assertion at face value.
OWASP recommends examining deleted tests and weakened assertions, adding adversarial and negative cases that the AI did not generate, and manually writing or independently verifying security-critical tests. A green generated suite is not evidence that a change is secure (OWASP guidance).
Keep test intent visible
Name tests after the rule they protect, keep fixtures understandable and explain unusual security or concurrency setup. When a test depends on a production-like service, state that requirement rather than silently replacing it with a mock. Run the normal unit, integration, end-to-end, static-analysis and security gates; AI contribution is not a reason to lower a threshold.
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Do not treat an AI-generated explanation, self-review or vulnerability scan as an approval. Use automated tools for repeatable checks and triage, then have a qualified human validate findings, investigate suppressions and decide whether residual risk is acceptable. The reviewed guidance does not establish that an AI reviewer can replace human accountability.
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| Approach | Strengths | Risks to manage | Best use |
|---|---|---|---|
| Traditional line-by-line diff | Familiar, concrete and easy to audit for a small change | Reviewer fatigue and missed architectural or cross-file risks in a large generated diff | Small, well-bounded changes with stable context |
| Overview, risk-based zoom and details on demand | Matches attention to system impact and makes large changes navigable | Important low-salience defects can be missed if the initial risk map is wrong; requires good context | Broad agent output, provided the reviewer verifies the map and returns to the full diff |
Compare these approaches using reviewer comprehension, missed-risk exposure, cognitive effort and the quality of context the interface exposes. The second approach is a conceptual framework, not a demonstrated superior method; pilot it with explicit exit criteria rather than declaring it universally better.
Using AI to assist review without outsourcing approval
An assistant can summarize files, point to changed data flows, suggest missing tests or perform a first-pass lint and security triage. Treat those results as hypotheses. Require a human to confirm the finding, inspect the surrounding code and record the disposition. Measure false alarms, missed defects, privacy exposure, auditability and total reviewer effort—not just the number of comments produced.
Do not allow a model with write access to approve its own changes, modify required checks or silently suppress a finding. Keep branch protections, required reviewers, signing, test gates and deployment authorization independent of the generation tool.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure whether the workflow is preserving quality
Review process outcomes over time instead of assuming that a new workflow works. Establish a comparable human-only baseline where practical, segment results by repository and risk class, and document changes in tooling or team composition.
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| Vulnerability density | Track confirmed vulnerabilities relative to changed code or another stable denominator | Definitions, severity and discovery effort must remain consistent |
| Mean time to detect | Measure time from introduction to reliable detection of a defect or vulnerability | A faster detector can raise reported counts before it lowers harm |
| AI-attributable defect rate | Classify defects linked to AI-mediated changes using a documented attribution rule | Attribution is uncertain; avoid blaming the tool from a single incident |
| Prompt-injection detection rate | Record attempts and confirmed detections when assistants process untrusted repository or issue content | Coverage depends on what attacks are included and how they are labeled |
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| Review time and escaped issues | Watch reviewer effort, rework, rollback and post-release defects together | Optimizing speed alone can reward shallow review |
These examples come from OWASP AISVS Appendix C, which proposes comparing AI-produced or AI-mediated code with a human-only baseline. They are proposed controls and metrics, not a validated universal scorecard; check the living document’s current revision before adopting definitions (OWASP AISVS Appendix C).
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Common failure modes and corrective actions
“The tests pass, so the generated code is safe.”
Passing tests show only that the executed assertions passed. Compare the tests with the requirement, add negative and adversarial cases, and inspect security-critical paths independently.
“The diff is too large to split, so approval is a formality.”
Pause the merge, extract coherent commits or require a reviewed design and risk map. If the change cannot be made understandable, escalate the schedule conflict instead of waiving review.
“The assistant found no security issue.”
Run the normal security gates and have a qualified reviewer inspect trust boundaries, secrets, authorization, dependencies and delivery scripts. An assistant’s silence is not evidence.
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“Generated build files are harmless plumbing.”
Review package scripts, CI configuration, container definitions and deployment changes for command injection, permission expansion, secret exposure and untrusted input. These files can execute in trusted contexts.
“Disclosure creates bureaucracy, so we will record nothing.”
Use lightweight traceability already present in commits and pull requests. Record enough context to reconstruct what was generated, reviewed and tested without demanding a new form for every line.
“One incident proves AI is the cause—or proves the workflow works.”
Classify the defect, preserve the relevant context and compare trends with a defined baseline. Avoid causal claims that the available data cannot support.
A pull-request checklist
- Intent: Is the problem, expected behavior and acceptance criteria explicit?
- Scope: Is the work split into coherent, reversible units?
- Traceability: Are affected components, assumptions, AI assistance and checks recorded?
- Risk: Have authentication, authorization, validation, cryptography, data stores, dependencies and delivery infrastructure been marked?
- Behavior: Were boundaries, failures, retries, concurrency and observability inspected?
- Tests: Were deleted tests, weakened assertions, mocks, negative cases and security-critical tests independently reviewed?
- Controls: Did all normal functional, static, dependency and security gates run without relaxed thresholds?
- Approval: Is the reviewer suitably qualified and able to explain why residual risk is acceptable?
What this guidance does not establish
There is no directly reported causal statistic in the cited material showing how much AI-specific review practices improve code quality. The 17-person and 43-person JetBrains/Lund samples do not measure a population-wide defect rate. NIST SP 800-218A addresses secure development of generative AI and dual-use foundation models, not a direct code-review standard for teams using assistants to write ordinary application code (NIST SP 800-218A). OWASP’s cheat sheet and AISVS are living documents, so version-specific requirements can change. Use the principles here as a controlled workflow to evaluate, not as proof that a particular tool or process guarantees secure software.
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