Put every AI-written change through a pull request that must compile, pass the repository’s existing tests, clear independent security and quality checks, and receive approval from a qualified human engineer. Treat code, tests, fixes, and review comments produced by the same AI agent as hypotheses—not as independent assurance.
The reliable setup is a layered pipeline: define the change and its threat model, ask for a small and explainable diff, run functional checks first, add static and dependency analysis, apply dynamic or fuzz testing to risky paths, verify package provenance, and block unsafe merges. Preserve the results and the identity of the human approver so the decision can be audited later.
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What an automated review workflow must prove
AI can produce plausible code quickly, but plausibility is not correctness. A pull request should provide evidence for four separate questions:
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- Does it introduce a known security or quality defect? Static analysis, secret scanning, dependency analysis, license checks, and maintainability checks look for classes of problems that ordinary tests miss.
- Does it withstand hostile or unusual input? Dynamic application tests, API abuse cases, browser checks, and fuzzing exercise behavior that a happy-path test may never reach.
- Has a responsible person accepted the risk? A qualified human reviews the diff, the evidence, and any exception before merge.
These controls are complementary. NIST’s IR 8397 (2021), authored by Paul E. Black, Vadim Okun, and Barbara Guttman, presents eleven broadly applicable software-verification recommendations rather than a single universal scanner. Its coverage includes automated testing, static scanning, heuristic secret checks, structural tests, fuzzing, web-application scanners, and review of included libraries and services.
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Build the pull-request pipeline
Run checks automatically when a pull request opens, when commits change, and immediately before merge. Keep the checks reproducible in a clean environment and make the merge decision depend on their status.
| Pipeline stage | What it checks | Default merge policy |
|---|---|---|
| Build and type validation | Compilation, type errors, generated artifacts, schema or migration validity | Block on failure |
| Tests | Unit, integration, contract, regression, and relevant end-to-end behavior | Block on failure, missing, skipped, or deleted required tests |
| Static analysis | Code patterns, data flow, reliability, maintainability, and security findings | Block on critical findings; triage lower severities |
| Secret scanning | Credentials, tokens, private keys, and accidental sensitive-data disclosure | Block confirmed secrets and revoke exposed credentials |
| Software-composition analysis | Known vulnerabilities, dependency freshness, package provenance, and license policy | Block policy violations and critical exploitable vulnerabilities |
| Dynamic, web, API, or fuzz testing | Runtime authorization, malformed input, injection, rate limits, SSRF, browser behavior, and crash cases | Required for exposed or high-risk components |
| Human approval | Intent, architecture, threat model, evidence, and residual risk | Required for every AI-generated change |
Use a separate job or status for each class of evidence. A single green “CI” check can hide a skipped scanner or a test command that ran no tests. Branch protection should require the specific statuses that correspond to your policy.
Step 1: Define the change and threat model before generation
Write acceptance criteria
Describe the behavior that must be true, the behavior that must remain unchanged, and the conditions under which the change is rejected. Include input constraints, error handling, authorization rules, performance limits that matter to the feature, and compatibility requirements. Acceptance criteria give both the AI assistant and the reviewer a reference that is independent of the generated implementation.
Map affected components and trust boundaries
List the services, packages, data stores, queues, browsers, and infrastructure touched by the change. Mark boundaries where data or authority crosses between users, tenants, services, administrative roles, or third-party systems. Identify files that deserve elevated scrutiny, such as authentication, authorization, cryptography, payments, secrets handling, deployment configuration, and infrastructure-as-code.
Record abuse cases
For each boundary, write how an attacker or malfunctioning client could supply unexpected data or obtain an action they should not be allowed to perform. Include forged identity claims, cross-tenant identifiers, replayed requests, oversized payloads, malformed serialization, injection strings, server-side request forgery paths, and rate-limit exhaustion where applicable. NIST describes threat modeling as a way to find design-level security issues before implementation details obscure them.
Step 2: Ask for a small, reviewable change
Give the assistant the acceptance criteria, relevant project documentation, and the narrowest set of files needed. Ask it to state assumptions, identify the project sources it relied on, explain security-sensitive decisions, and list tests it did not write. Keep feature work, refactoring, dependency upgrades, and formatting changes in separate pull requests when possible.
Why diff size matters
A reviewer can compare a small diff with the stated intent. A broad generated rewrite makes it difficult to distinguish a required change from accidental behavior changes, hidden dependency additions, or deleted tests. If the assistant proposes a large edit, split it into independently testable commits or regenerate with a narrower scope.
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Preserve provenance
Record which assistant or model produced material code, the prompt or task description used by your process, and any human edits made afterward. Provenance is not a substitute for testing, but it helps investigators understand how a vulnerable line entered the repository and whether the same pattern may exist elsewhere.
Step 3: Run functional checks first
GitHub’s guidance for reviewing AI-generated code says, “Always run automated tests and static analysis tools first.” Start with the repository’s documented build and test commands in a clean checkout, then inspect the results rather than trusting a successful process exit alone.
Build and type checks
- Compile all affected targets and generate required clients, schemas, or assets.
- Run the project’s type checker with the same strictness used in production or release builds.
- Verify database migrations in an isolated environment and test both upgrade and, where supported, rollback behavior.
- Confirm that generated files are reproducible and that no untracked artifact is required for the build to pass.
Test layers
- Unit tests: Exercise validation, business rules, edge values, and error paths around changed functions.
- Integration tests: Verify real boundaries such as databases, queues, caches, filesystem permissions, and external-service adapters.
- Contract tests: Check that API schemas, events, and client expectations remain compatible.
- Regression tests: Reproduce each fixed defect so it cannot silently return.
- End-to-end tests: Use them for critical user journeys, authorization flows, and browser behavior that cannot be represented by lower-level tests.
Investigate tests that are missing, newly skipped, weakened, deleted, or no longer assert meaningful outcomes. A coverage percentage can rise while important assertions disappear, so review changed-lines coverage and the quality of assertions rather than treating a threshold as proof of correctness.
Step 4: Add independent security and quality gates
Static application security testing
Run a static analyzer that can inspect control flow, data flow, and framework-specific hazards. GitHub identifies CodeQL as one useful option. Review findings in the context of the threat model: a data-flow warning on an administrative endpoint deserves different urgency from a low-confidence style issue. Do not suppress a finding solely because the line was generated by an assistant.
Secret scanning
Scan the full diff and repository history where appropriate for API keys, passwords, private keys, session material, and cloud credentials. A confirmed secret is an incident: remove it, revoke or rotate it, check logs for use, and determine whether copies remain in artifacts or forks. A scanner that reports only newly added strings does not replace periodic historical scans.
Dependency and license analysis
Inspect every new or changed package, plugin, container image, and service. Confirm that the package exists in the intended registry, is maintained, has a license your organization accepts, and comes from a trusted source. GitHub warns about hallucinated packages and “slopsquatting,” in which an attacker publishes a package name that an AI system may invent. Enforce approved registries, lockfiles, signatures or checksums where available, and review of transitive dependencies.
Reliability and maintainability analysis
Run linters, formatting checks, complexity or duplication analysis, and reliability rules. These checks catch unreachable branches, unsafe error handling, resource leaks, and inconsistent conventions that functional tests may not expose. Keep formatting automatic so reviewers spend their time on behavior and risk.
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Step 5: Exercise risky behavior dynamically
Static analysis reasons about code; dynamic testing observes the running system. Apply it in a test environment that contains representative configuration and data without exposing production secrets.
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- Attempt each protected operation as an unauthenticated user, an ordinary user, a user from another tenant, and an administrator.
- Change object identifiers, role claims, and session state to test for horizontal and vertical privilege escalation.
- Verify that authorization is enforced server-side, not only by a user-interface control.
Input and protocol abuse
- Send malformed, oversized, truncated, and deeply nested payloads.
- Exercise injection paths appropriate to the stack, including command, query, template, and browser-context injection.
- Test replay, duplicate submission, timeout, partial failure, and unexpected content types.
- Check rate limits, quota accounting, lockout behavior, and resource consumption under repeated requests.
External-request and browser paths
For features that fetch URLs or render untrusted content, test server-side request forgery defenses, redirect handling, private-network access, content-type validation, and browser isolation. Dynamic web-application scanners in the OWASP ZAP category can complement targeted tests, but a scanner cannot infer every business authorization rule.
Fuzzing
Use fuzzing for parsers, protocol handlers, file readers, serialization code, and other components with large or adversarial input spaces. Define useful assertions such as no crash, bounded resource use, valid error responses, and preservation of invariants. Save crashing inputs as regression cases and investigate whether a timeout indicates a denial-of-service risk.
Step 6: Verify dependencies and provenance
Before approval, compare the dependency manifest and lockfile with the pull request’s stated purpose. For each addition, answer:
- Is the package name real and available from the approved registry?
- Is the publisher or repository the intended source?
- Is the project maintained, and does its release history make sense for the claimed capability?
- Does its license fit the product and distribution model?
- Does it introduce native code, install scripts, network calls, telemetry, or new privileges?
- Are transitive packages and container layers acceptable under the same policy?
Require a human to inspect unfamiliar packages even when an automated vulnerability database reports no issue. “No known vulnerability” does not establish that a package is authentic, maintained, or appropriate.
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OWASP’s AI Security Verification Standard says, “Verify that AI-generated code always goes through code review by a qualified human engineer.” The agent that generated the change cannot serve as the reviewer. The reviewer should understand the affected language and framework, the threat model, and the operational consequences of failure.
Review order
- Read the acceptance criteria and threat model before opening the diff.
- Confirm that the diff is limited to the stated purpose and that tests were not removed or weakened.
- Trace untrusted input to storage, interpreters, network calls, logs, and responses.
- Check authentication, authorization, tenant isolation, cryptography, secrets, and error handling.
- Read the automated results, including suppressed findings and flaky or skipped tests.
- Run or reproduce important cases locally or in a review environment.
- Record the decision, residual risk, and any time-bounded follow-up.
Elevated approval
Require two-person or security-team approval for authentication, authorization, cryptography, payment processing, secrets handling, and infrastructure changes. The second reviewer should be able to reject the change independently; an approval that merely confirms the first reviewer’s opinion adds little protection.
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Step 8: Block unsafe merges and retain evidence
Configure branch protection so critical scanner findings, failed required tests, unresolved review threads, and missing approvals prevent merging. Permit exceptions only through a documented, human-approved process that names the risk, compensating control, owner, expiration date, and conditions for removal.
Retain build logs, test reports, scan results, dependency manifests, code-review approvals, and exception records with the pull request or an associated audit system. OWASP governance guidance assigns humans accountability and calls for audit trails and approval for irreversible actions. Evidence should show what ran, against which commit, with which configuration; a screenshot of a green dashboard is not sufficient.
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A passing test suite produced by the same agent that wrote the implementation can confirm the agent’s own assumptions while missing the requirement it misunderstood. OWASP’s Secure Coding with AI guidance states, “A passing test suite generated by the same agent that produced the code provides no independent assurance.”
Use generated tests as a starting point, then require tests that were already in the repository, tests written or substantially inspected by a different person or tool, and adversarial cases derived directly from the acceptance criteria and threat model. Look for weak assertions such as checking only that a function does not throw, snapshotting an entire response without validating authorization fields, mocking the component under test, or asserting the same implementation detail in both code and test.
Signs of false confidence
- All new tests pass, but no test fails when a key authorization check is deliberately removed.
- Coverage increases only because generated tests execute lines without checking outcomes.
- Tests use the same hard-coded values and assumptions as the implementation.
- External services, retries, timeouts, and malformed inputs are always mocked away.
- The test command completes suspiciously quickly or reports zero discovered tests.
Choosing and combining analysis options
No single product covers every defect class. Compare tools and services against the workflow rather than choosing by brand familiarity.
| Decision factor | Questions to ask |
|---|---|
| Defect classes | Does it find data-flow vulnerabilities, secrets, dependency risk, reliability defects, license violations, runtime failures, or only one of these? |
| Independence | Does the check analyze the committed code independently of the generator, and can a separate team or tool validate the result? |
| Language and framework support | Are the project’s languages, frameworks, generated files, infrastructure, and APIs covered? |
| Speed and cost | Can fast checks run on every pull request while deeper scans run on a schedule or for high-risk changes? |
| False-positive workflow | Can reviewers explain, suppress, expire, and audit a finding without hiding future instances? |
| Merge controls | Can severity thresholds and required statuses block a pull request reliably? |
| Auditability | Are commit-specific results, reviewer identity, policy decisions, and exceptions retained? |
| Execution model | Is local, self-hosted, or hosted execution acceptable for the code and data involved? |
| Scope | Can it analyze changed code and the whole repository, including dependencies and generated artifacts? |
GitHub documentation names CodeQL, Dependabot, and GitHub Code Quality as useful checks. OWASP DevSecOps guidance names Semgrep Assistant and Snyk DeepCode AI for security triage and remediation. Treat these as complementary capabilities, verify current support and terms, and keep a human accountable for the decision.
A practical pull-request checklist
Put a concise checklist in the pull-request template and make the corresponding automated statuses required:
Change definition
- Acceptance criteria and affected components are stated.
- Trust boundaries and security-sensitive files are identified.
- The diff is limited to the stated purpose.
Functional evidence
- Build and type checks passed.
- Unit, integration, contract, regression, and required end-to-end tests passed.
- Missing, skipped, deleted, or weakened tests were investigated.
Security and supply chain
- Static analysis passed or findings have documented dispositions.
- Secret scanning passed; any exposed credential was revoked.
- Dependencies, provenance, maintenance, licenses, and transitive packages were reviewed.
- Dynamic, abuse-case, web, API, or fuzz tests ran where risk requires them.
Approval
- A qualified human reviewed the implementation and evidence.
- Elevated approval was obtained for security-critical files.
- Exceptions name an owner, compensating control, and expiry.
- Logs and reports are retained with the change.Troubleshooting common failures
The pipeline is green but the feature is wrong
Check whether the tests encode the acceptance criteria or merely execute the new lines. Add an independently designed failing case, especially around authorization, error handling, and boundary values. Confirm that the test job discovered and ran the intended test files.
A scanner reports too many findings
Separate findings by severity and exploitability, fix systemic patterns first, and tune rules only with documented justification. Prefer a narrow, expiring suppression tied to a tracked issue over a repository-wide exclusion.
Dynamic tests are flaky
Classify failures as environment, timing, data-isolation, or product defects. Stabilize setup and teardown, remove shared mutable state, and keep a flaky-test quarantine visible. Never convert a security test to an allowed failure merely to restore a green badge.
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A package suggested by the assistant cannot be verified
Do not install it to discover whether it works. Search the approved registry and project documentation manually, confirm the publisher and license, and choose a known, maintained alternative if provenance remains uncertain.
The change is too large to review
Return it for decomposition. Separate mechanical formatting, dependency upgrades, refactoring, and behavior changes; then review each pull request against its own acceptance criteria and threat model.
Rollout plan for teams starting from scratch
- Baseline: Require builds, existing tests, linting, static analysis, secret scanning, dependency checks, and one qualified approval on every pull request.
- Risk classification: Mark changes that touch identity, authorization, cryptography, payments, secrets, infrastructure, or public interfaces.
- Layering: Add dynamic, abuse-case, browser, API, or fuzz tests to the classified changes first, then expand coverage as the team learns which paths fail.
- Governance: Define severity thresholds, exception expiry, elevated-approval rules, artifact retention, and who owns remediation.
- Review quality: Periodically sample merged AI-assisted changes, compare incidents and escaped defects with the checks that ran, and improve the pipeline without weakening independent review.
There is no published defect-rate or productivity figure in the primary guidance that can justify skipping these controls. The defensible conclusion is procedural: AI-written code should enter the same engineering controls as other code, with additional attention to provenance, independent tests, and human accountability.
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