How can organisations tell whether AI is improving code quality or just increasing output?

Look at rework rates, defect escape rates, and how often generated code is rewritten after review. If output rises but cleanup effort and remediation also rise, the programme is producing volume rather than quality. Real improvement shows up in fewer escaped defects and lower review friction.

Why This Matters for Security Teams

AI-assisted development can create a false sense of progress. A team may ship more code, faster reviews, and larger backlogs of generated pull requests, yet still end up with brittle logic, hidden defects, and more security review effort. For security leaders, the real question is not whether output increased, but whether the delivery system is producing code that survives review, testing, and production use with less friction.

This matters because code quality is not only a developer concern. Weaknesses introduced through AI-assisted coding can widen attack paths, increase maintenance burden, and create downstream exposure in CI/CD, secrets handling, and access control. Control frameworks such as NIST SP 800-53 Rev 5 Security and Privacy Controls help teams anchor this discussion in measurable governance rather than anecdotal productivity claims.

What practitioners often miss is that higher output can temporarily mask quality erosion because reviewers adapt, test coverage drifts, and defect discovery moves later in the lifecycle. In practice, many security teams encounter the real cost only after release hotfixes, audit findings, or incident response activity reveal that “faster” was really just “more to clean up.”

How It Works in Practice

The clearest way to judge AI’s effect is to measure the full path from generation to production outcome, not just the number of lines or pull requests created. A useful baseline compares pre-AI and post-AI periods across the same repositories, teams, and release cadence, then tracks whether review effort, defect density, and remediation time move in the same direction as output.

• Track rework rate: how often generated code is rewritten, refactored, or rejected during review.
• Measure defect escape rate: defects found after merge, in test, or in production compared with defects found during review.
• Observe review friction: number of review comments, time-to-approval, and how often security reviewers must intervene.
• Separate productivity from quality: count accepted changes, but also count follow-up fixes, rollbacks, and hotfixes.
• Include operational signals: test failures, static analysis findings, dependency issues, and policy exceptions.

This approach aligns with broader engineering governance practices in the NIST Secure Software Development Framework, which emphasises building security and quality into the lifecycle rather than assessing them after the fact. It also benefits from release-level controls such as code signing, branch protection, and automated checks that prevent low-quality output from being mistaken for progress.

For teams using AI in code generation, the key is to distinguish assistive use from autonomous acceptance. If engineers accept model output with only superficial edits, the programme may increase throughput while silently raising technical debt. If reviewers spend less time debating syntax but more time correcting architecture, threat modelling, or secure defaults, the AI is not improving quality. These controls tend to break down in fast-moving monorepos with weak test coverage and inconsistent ownership because bad generated code can merge before anyone has a stable quality baseline.

Common Variations and Edge Cases

Tighter review gates often slow delivery, requiring organisations to balance speed against assurance. That tradeoff becomes more visible when AI is introduced into teams that already operate with limited testing, high legacy debt, or unclear code ownership. In those environments, a rise in output can reflect automation of already weak practices rather than genuine improvement.

There is no universal standard for this yet, but current guidance suggests treating AI-assisted code as one input into a larger control system. For example, green test runs alone do not prove quality if the tests are shallow or the model repeatedly reproduces the same flawed pattern. Likewise, lower cycle time is not meaningful if security defects are escaping review or if maintainers are spending more time rewriting than extending the codebase.

Organisations should also account for different use cases. Generating boilerplate, scaffolding tests, or accelerating documentation can improve throughput without changing core code quality much. By contrast, using AI for security-sensitive logic, authorization paths, or infrastructure code requires stronger validation and more conservative acceptance thresholds. Where AI systems are helping produce code that handles secrets, identity, or privilege boundaries, the review model should be stricter because the security impact is higher.

Related resources from NHI Mgmt Group

• How can organisations tell whether AI-generated code is improving or weakening governance?
• How can organisations tell whether AI output drift is a security problem or a model-quality issue?
• How can teams tell whether AI-driven SIEM is actually improving investigation quality?
• How can organisations tell whether OCR is improving KYC quality?