How do security teams evaluate whether AI coding tools are improving secure development?

Why This Matters for Security Teams

AI coding tools are only valuable when they improve secure development outcomes rather than create a new layer of noise. Security teams need to know whether these tools help developers catch issues earlier, write safer code by default, and remediate findings without slowing delivery. That means measuring outcomes in the same places risk appears: code review, dependency handling, secrets exposure, and pipeline gates. Guidance from the NIST Cybersecurity Framework 2.0 is useful here because it pushes teams toward measurable governance, not tool adoption for its own sake.

The common mistake is treating AI assistance as an automatic control improvement. In practice, a coding assistant can reduce repetitive mistakes, but it can also normalize insecure patterns if prompts, review rules, and policy checks are weak. Security leaders should ask whether secure coding guidance is being surfaced inside the developer workflow, whether reviewers are seeing fewer preventable issues, and whether the tool is changing the shape of risk or just moving it around. In practice, many security teams encounter the real impact only after insecure patterns have already been embedded into shared codebases, rather than through intentional measurement of developer behaviour.

How It Works in Practice

Effective evaluation starts by defining a baseline before rollout. That baseline should include defect density for security-relevant findings, mean time to remediate, false positive rates from code scanning, and the percentage of issues fixed before merge. The goal is not to prove that the AI tool writes perfect code. The goal is to see whether it changes developer behaviour and control performance in a measurable way.

Teams usually need a mix of quantitative and qualitative signals. A helpful review model includes:

• Security findings discovered in pre-commit, pull request, and CI stages
• Change in repeated classes of defects such as injection flaws, weak authentication, or exposed secrets
• Developer acceptance rate for secure suggestions versus rejected insecure suggestions
• Impact on review workload for application security and platform teams
• Evidence that fixes are applied before release rather than after escalation

Tool output also needs validation. AI-assisted code should still be checked by policy-as-code, static analysis, dependency scanning, and human review for high-risk changes. If a tool suggests secure patterns but developers override them frequently, that signals poor fit, weak policy alignment, or insufficient contextual understanding. Current guidance suggests measuring the workflow effect, not just the number of suggestions generated, because volume alone can hide risk.

For governance, security teams should align evaluation with secure development practices and software supply chain controls. The OWASP Top 10 for Large Language Model Applications is relevant when AI coding tools are connected to prompts, repositories, or code generation pipelines that may be exposed to injection or data leakage. The practical question is whether the tool helps developers avoid introducing flaws, or whether it simply accelerates the rate at which flawed patterns are copied into production code. These controls tend to break down when teams use the tool across legacy monoliths, mixed-language repositories, and inconsistent CI pipelines because security feedback becomes too fragmented to measure reliably.

Common Variations and Edge Cases

Tighter measurement often increases process overhead, requiring organisations to balance developer speed against confidence in the results. That tradeoff is especially visible when teams try to evaluate AI coding tools across very different application types. A tool that appears effective in greenfield services may perform poorly in regulated systems, older codebases, or teams with weak test coverage. Best practice is evolving, and there is no universal standard for what “improvement” must look like in every environment.

Edge cases matter. In safety-critical or highly regulated environments, security teams may care less about raw productivity gains and more about whether the tool reduces risky shortcuts, improves traceability, and supports stronger review evidence. In fast-moving product teams, the main concern may be whether the tool reduces alert fatigue and helps developers resolve findings inside the editor or pull request rather than bouncing them into separate tickets. If the organisation has weak branch protections, inconsistent code ownership, or minimal secure coding training, it can be difficult to attribute any improvement to the AI tool itself.

Where AI coding tools are connected to autonomous agents or code-generation workflows, identity and privilege boundaries become part of the assessment. The question is not only whether the output is secure, but whether the system has enough guardrails to prevent unsafe actions, overbroad access, or unreviewed changes. That is where AI governance and software assurance meet in practice, and why teams should treat the evaluation as an ongoing control check rather than a one-time pilot result. The NIST Cybersecurity Framework 2.0 remains a solid anchor for tracking whether those improvements are real and repeatable.

Related resources from NHI Mgmt Group

• How should security teams govern AI coding tools that create non-human identities?
• How should security teams govern Cursor-like AI coding tools in the enterprise?
• How should security teams evaluate AI tools that behave differently on each run?
• How should security teams evaluate AI agent authorization tools?