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AI-generated code is ready for production only after a qualified human has verified its intent, security, behavior, and fit with the surrounding system. Treat a model’s suggestion as an untrusted contribution, not as an approval. Read the complete change, compare it with the requirement and threat model, run deterministic tests and security automation, probe failure paths, and require the appropriate owner to approve the result. The amount of review should rise with the change’s blast radius: an internal formatting adjustment does not warrant the same scrutiny as an authorization change, a dependency update, or a deployment-pipeline edit.
Automation is valuable for repeatable checks, but a clean scanner cannot establish that a user is allowed to perform an action, that a business rule is complete, or that a generated dependency is legitimate. The engineer who merges the change remains accountable for the code that reaches production.
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Generated code can look polished while silently omitting requirements that were never stated in the prompt. A model may implement the happy path but miss authorization on one endpoint, validate one input format while allowing another, choose a package with a similar name, or copy a familiar but unsafe pattern. These failures are not unique to models; they are reasons to apply the same security and engineering standards used for code from any unfamiliar contributor.
The UK Home Office engineering standard states: “AI-assisted outputs MUST be reviewed and approved by a human before reaching production.” Its guidance also makes clear that teams retain accountability for AI-assisted code and that a tool cannot replace human judgment, understanding, ownership, or responsibility. In practice, that means your normal pull-request, testing, security, and release controls remain in force. AI assistance should be visible in those controls rather than creating a parallel path that bypasses them.
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OWASP guidance recommends a risk-based approach. Spend the most review time where a defect could cross a trust boundary, expose sensitive data, grant privileges, alter infrastructure, or interrupt delivery. Keep low-risk changes efficient, but do not let the apparent fluency of generated code reduce scrutiny.
How do I define the intended behavior before reading the diff?
Start with the requirement, not with the implementation. Before evaluating a generated patch, assemble the information that defines what “correct” means and where the change is allowed to operate.
- Read the issue and acceptance criteria. Identify required behavior, explicitly excluded behavior, supported inputs, error semantics, and compatibility obligations.
- Review the design and local conventions. Understand the component’s data flow, API contracts, error handling, logging policy, and patterns for authentication, authorization, and persistence.
- Identify data and trust boundaries. Mark where requests enter, where identities are established, where untrusted values are parsed, where data crosses services, and where credentials or personal information are handled.
- Check the threat model. List plausible misuse cases and assets at risk. NIST describes threat modeling as a way to reveal overlooked testing targets and focus verification effort.
- Write a short review charter. State the behavior that must be demonstrated, the invariants that must remain true, and the evidence required before merge. This prevents a plausible implementation from becoming the de facto specification.
If the requirement is ambiguous, resolve it with the product or service owner before accepting code. A reviewer cannot prove conformance to a rule that has never been defined.
How should I read an AI-generated diff?
Read the entire change and understand every line before deciding whether it is safe. Do not review only the lines that appear to implement the ticket; generated patches often include incidental refactoring, configuration changes, new packages, or test edits that alter the risk.
Trace the change through the architecture
Follow inputs from their entry point to storage, external calls, and responses. Check error and timeout paths, retries, logging, metrics, and cleanup. Confirm that the implementation uses the project’s established abstractions instead of introducing a second way to perform the same security-sensitive operation.
Ask whether every line is necessary
Unrelated formatting and broad refactors increase review volume without improving the feature. Remove or split incidental edits so the behavior under review is visible. If the patch remains unusually large, divide it into smaller changes or add another qualified reviewer; do not allow code volume to turn review into a superficial approval.
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The approving engineer should be able to explain the control flow, state transitions, assumptions, and failure behavior without asking the model to interpret its own output. An AI agent can summarize a diff, but that summary is not evidence that the reviewer understands it.
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Which parts of generated code deserve extra scrutiny?
Increase review depth when a change can affect identity, confidentiality, integrity, availability, or the software-delivery system itself. The following areas routinely require context that pattern matching cannot supply.
Authentication and authorization
Verify that authentication is required where intended and that authorization is enforced on every relevant path, including alternate HTTP methods, background jobs, administrative endpoints, object-level access, and error-handling branches. Test a valid identity attempting to access another user’s resource, an unauthenticated request, an expired session, and a role that is authenticated but not permitted. Do not assume that checking a token proves the caller may perform the requested action.
Input parsing and output handling
Trace every untrusted value. Confirm type, length, encoding, range, and structural validation before use. Look for string-built queries, unsafe template interpolation, dynamic evaluation, path traversal, command construction, deserialization of attacker-controlled data, and output that is returned without the required escaping. Validation should be aligned with the accepted format rather than a superficial “not empty” check.
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Cryptography and secrets
Check that algorithms, modes, key sizes, randomness, password handling, and key storage follow the project’s approved approach. Reject hardcoded credentials, tokens in source or logs, secrets passed through unprotected process arguments, and newly invented cryptographic helpers. A generated implementation that “works” with a placeholder key is not production-safe.
File uploads, storage, and network exposure
Review file type and size checks, filename handling, storage location, malware-scanning hooks, and download authorization. For network-facing changes, inspect default listeners, exposed ports, cross-origin policy, rate limits, and failure behavior. A small configuration edit can turn an internal service or bucket into a public one.
Dependencies and external integrations
Confirm that every suggested package exists, is the intended package, comes from the approved registry, and has credible provenance and maintenance history. Check the resolved version and lockfile rather than trusting a name in generated text. Examine new SDK permissions, outbound data, webhook verification, retry behavior, and handling of provider errors.
CI/CD and infrastructure
Review pipeline and infrastructure files as production code. A change can execute with deployment credentials even if it never runs in the application process. Look for widened permissions, unpinned actions or images, disabled branch or environment protections, exposed services or storage, secrets printed to logs, and steps that execute unreviewed content. Require the same ownership and approval discipline as application code.
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What automated checks should run before approval?
Run the repository’s normal build, unit tests, integration tests, static analysis, and formatting checks, then add security checks appropriate to the changed surfaces. Automation produces repeatable evidence and catches regressions quickly; it does not decide whether the feature satisfies its business and security intent.
| Check | Useful for | What it cannot prove |
|---|---|---|
| Build and type checks | Compilation errors, incompatible interfaces, missing symbols, and basic contract breaks. | That the implemented behavior is authorized, complete, or safe under misuse. |
| Unit and integration tests | Expected behavior, component interactions, regressions, and documented error handling. | Coverage of requirements that were never encoded, unusual abuse paths, or realistic production data conditions. |
| Static application security testing | Repeatable code patterns such as dangerous sinks, weak validation, and some injection risks. | Whether a caller should be allowed to reach a sink or whether a business rule is bypassed across several services. |
| Software-composition analysis | Known vulnerabilities, licenses, and dependency inventory when package identity and versions are resolved. | That a similarly named package is the intended one, that its maintainer is trustworthy, or that its use is correctly authorized. |
| Secret scanning | Credentials and token-like values accidentally committed to code, configuration, or test fixtures. | Secrets exfiltrated through logs, prompts, runtime requests, or a value that does not match scanner patterns. |
| Infrastructure-as-code scanning | Common public-exposure, permission, encryption, and configuration mistakes. | Whether the resulting architecture is appropriate for the service’s threat model and operational dependencies. |
| Dynamic and abuse testing | Observed behavior at runtime, malformed requests, authentication failures, and boundary conditions. | Every possible state transition or a guarantee that an untested business workflow is correct. |
OWASP recommends using automation for repeatable findings while keeping human attention on context-dependent flaws such as broken authorization and business-logic bypasses. GitHub’s review guidance similarly places automated tests and static analysis before a review of functional intent and project fit. A clean scan is the beginning of evidence gathering, not a production certificate.
How do I test the behavior and failure cases?
Tests should demonstrate the requirement and challenge the assumptions in the generated implementation. Existing tests must continue to pass, and new behavior should have tests that would fail if the requirement were removed or reversed.
Cover normal, invalid, and unauthorized paths
- Exercise valid inputs at minimum, maximum, and boundary values.
- Send malformed, missing, duplicated, oversized, and incorrectly encoded values.
- Try unauthenticated requests and authenticated users without the required role or resource ownership.
- Force dependency timeouts, partial failures, rejected writes, and unexpected response shapes.
- Check retries, idempotency, cancellation, cleanup, and transaction rollback.
- Verify that errors reveal enough for operators while withholding credentials, personal data, stack traces, and internal topology from callers.
Test invariants, not just outputs
For a permission change, an important assertion is that an unauthorized actor remains unable to read or modify the object. For a payment, quota, or state transition, assert that the invariant holds after retries and concurrent requests. For a parser, assert that rejected input cannot reach a dangerous sink. These tests are more valuable than assertions that merely mirror the implementation’s current branches.
Review AI-written tests skeptically
OWASP’s SAMM guidance advises checking whether generated tests validate the requirement rather than simply confirming that the code does what it already does. Watch for tests that mock away the authorization layer, assert only that a function was called, use fixed “happy path” data, or duplicate private implementation details. Ask what functional tests are absent, using GitHub’s review prompt: “What functional tests to validate this code change do not exist or are missing?”
Validate security findings
Reproduce scanner and review findings in a controlled environment. Confirm the affected path, prerequisites, impact, and whether an existing control blocks exploitation. Record false positives with a reason, and fix true positives before merge; do not dismiss a finding merely because the generated code looks conventional.
How should review depth change with risk?
Use explicit triggers so that sensitive changes receive more than a routine glance. The table below is a practical allocation model; adapt it to your organization’s ownership and release controls.
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|---|---|---|---|
| Lower | Documentation, isolated presentation changes, or logic with no external input or privilege effect. | Diff review, build, relevant unit tests, and confirmation that no unrelated files changed. | Owner approval when the change affects a shared component. |
| Moderate | New data handling, API behavior, persistence logic, or third-party integration. | All normal tests, targeted negative cases, dependency review, static analysis, and an explanation of error and rollback behavior. | Review by a maintainer of the affected subsystem. |
| High | Authentication, authorization, cryptography, file upload, deserialization, public endpoints, sensitive data, dependency changes with runtime privileges, or network exposure. | Threat-model review, abuse tests, security automation, provenance checks, and evidence that every access path is covered. | A suitably qualified security or service owner approves; split the change if possible. |
| Critical delivery impact | CI/CD, infrastructure, deployment credentials, production configuration, or secret-management changes. | Configuration review, permission-diff review, pinned and verified actions or images, deployment rehearsal where available, and rollback steps. | Independent approval from the responsible platform or operations owner. |
Risk is determined by blast radius, not by the number of generated lines. A five-line pipeline edit can deserve more scrutiny than a large internal refactor because it may execute with production credentials.
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How do I keep AI-assisted changes traceable?
Use the existing engineering record rather than an informal chat history. Keep the issue, pull request, commits, test results, security findings, approvals, and amendments connected. If policy requires disclosure of AI assistance, record it in the normal pull-request or commit metadata and identify which parts a human changed or verified.
Define approved tools and data-sharing rules before developers paste source into an AI service. Restricted source, credentials, customer information, and regulated data should not be sent to a service unless its use and retention are explicitly approved. Give coding agents the minimum credentials and actions they need; separate read, write, deploy, and secret-access capabilities. Preserve a human approval gate for consequential actions such as merging, changing production configuration, or deploying.
The Home Office guidance requires AI-assisted changes to remain traceable through engineering processes and to meet existing testing standards before merge or deployment. That traceability lets an incident responder determine what was proposed, what was reviewed, which tests ran, and who accepted the risk.
What AI-specific hazards should reviewers look for?
Security intent missing from the prompt
If the request did not mention authorization, rate limits, tenant isolation, or privacy, the generated code may omit them. Compare the implementation with the system’s actual security requirements, not only with the wording of the prompt.
Familiar-looking unsafe patterns
Generated code can reproduce weak cryptography, string-built queries, hardcoded credentials, insecure dynamic execution, permissive cross-origin settings, and logging of sensitive values. Familiar syntax is not evidence of a safe design.
Hallucinated dependencies
A model may invent a package, confuse two similarly named packages, or suggest an obsolete version. Verify registry identity, version resolution, provenance, license, maintenance history, and the package’s requested permissions before installation.
Prompt and context leakage
Review prompts, tool context, logs, and generated comments for restricted source, secrets, customer records, or internal architecture details. Remove sensitive material and use an approved service configuration before sharing any context.
Overpowered agents
Inspect the credentials and tools available to an agent. Limit filesystem, network, repository, and deployment permissions; protect CI configuration and secrets; require explicit human confirmation for actions with durable or external effects.
Review overload
AI can increase the amount of code produced faster than a team can understand it. Set practical limits on pull-request size, require ownership rules for sensitive paths, split generated work into reviewable units, and add a second reviewer when the change crosses a high-impact boundary.
What should a reviewer ask in the pull request?
Use a concise checklist after the deeper analysis, not instead of it:
- What requirement and acceptance criterion does each changed component satisfy?
- Can I explain every changed line, new dependency, configuration value, and test assertion?
- Which trust boundaries, identities, data stores, external services, and credentials are involved?
- Are authorization and tenant or object ownership checks present on every relevant path?
- Are untrusted inputs validated, safely encoded, and prevented from reaching dangerous operations?
- Do dependencies exist under the intended identity and come from an acceptable source?
- Do tests cover invalid input, unauthorized access, unexpected states, dependency failure, retries, and rollback?
- Did build, unit, integration, static, dependency, secret, infrastructure, and dynamic checks appropriate to the change pass?
- What possible vulnerabilities or security issues could this code introduce?
- Is the patch small enough to review, and does the designated owner have enough context and authority to approve it?
- Are AI assistance, data handling, approvals, findings, and amendments recorded in the normal engineering workflow?
What is the production-readiness decision?
Approve only when the implementation matches the stated intent, the risk-sensitive paths have been examined, deterministic checks and meaningful tests provide evidence, and the responsible human owner accepts the remaining risk. If any of those conditions is missing, return the change for clarification, additional tests, a smaller diff, or a qualified review. No scanner, model, or green test suite can transfer accountability away from the person and team that deploy the code.
Frequently Asked Questions
Can an AI tool review the code it generated?
It can provide a second analysis, suggest tests, or identify repeated patterns, but it should not be the approving authority. A different qualified human must determine whether requirements, system context, authorization, and operational risks are satisfied.
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Should AI-generated code be labeled in a repository?
Use the team’s normal policy for recording AI assistance in commits or pull requests. The useful record identifies what was generated or amended, what checks ran, and which owner approved the final change, without placing secrets or restricted prompts in the repository.
What should I do when a generated dependency has no clear history?
Do not install it merely because the name appears plausible. Verify the package identity in the approved registry, inspect its resolved version and provenance, and choose an established alternative or implement the small function locally if that is safer.
How can a small team review a large AI-generated patch?
Reduce the patch before review: remove unrelated refactors, split features into separate pull requests, and prioritize files that cross trust or deployment boundaries. Add an owner with the relevant security or platform expertise instead of relying on a superficial line-by-line pass.
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