AI Code Quality & Security Guardrails

What goes wrong with generated code

Review AI-generated code for nonexistent functions, unapproved dependencies, unsafe queries, broad exception handlers and tests that merely repeat the implementation. Keep the existing application-security controls, then assess agent-specific risks such as instructions hidden in repository content or an agent with permission to run commands it does not need.

The pipeline we build

We design the CI/CD stages around your existing platform, whether GitHub Actions, GitLab CI, Azure DevOps or Jenkins, rather than introducing a parallel one.

• Static analysis and secret scanning on pull requests, with findings routed to an owner and false positives reviewed.
• Dependency checks: known-vulnerability audit, lockfile review, and scrutiny of new or look-alike package names.
• A dependency license inventory for your legal or compliance owner. This surfaces items for review; it does not establish compliance or prove the provenance of every generated line.
• Risk-based test gates: authorization failures, retries and recovery for critical workflows, contract tests at service boundaries, and mutation or property-based tests where useful. Coverage is one indicator, not evidence that the assertions are meaningful.
• Architectural drift checks: import rules, layer constraints and API-surface diffs against your documented boundaries.

AI code review, placed carefully

Automated reviewers are useful for first-pass feedback: obvious bugs, missing tests, style and risky patterns. We configure them as advisory, with findings visible beside human comments, and keep merge approval with a named person. An AI reviewer can share the blind spots of the AI that wrote the code, so we do not treat agreement between them as evidence of correctness.

Honest limits

A generated test can mock an API that the real service does not provide. A business-logic flaw can pass a syntax check and a vulnerability scan. We map risks to checks and record the gaps: which tests exercise real integrations, who reviews tenant permissions, and how a failed release is recovered. Automated checks do not replace threat modeling, specialist review or penetration testing where the application requires them.

Deliverables

• Pipeline definitions and policy-as-code committed to your repository
• A control matrix mapping each risk to a check, an owner and a known gap
• Tuned rule sets and a false-positive triage process
• Dashboards for drift and exception trends, built from your own data
• Runbook for responding when a gate fails or is bypassed

Keep going

Gates are strongest when paired with trained reviewers (enablement) and a consistent environment (agentic workspaces). The full picture is in the service overview. To discuss your pipeline, contact us.