When should organisations add extra controls for AI-written code?

Why This Matters for Security Teams

AI-written code becomes a security issue when it influences trust boundaries, not simply when it contains a syntax error. The practical risk is that generated code often looks plausible while quietly mishandling identity claims, session state, headers, or secrets. That is why extra controls are most justified when the code can change who is authenticated, what is authorised, or which downstream system is trusted.

Security teams tend to underestimate these cases because the code review passes at a glance and the defect only appears under a specific request path or deployment condition. Control thinking should therefore follow the impact, not the origin of the code. The NIST Cybersecurity Framework 2.0 is useful here because it reinforces risk-based governance, asset visibility, and protective controls rather than assuming all code receives equal scrutiny.

Organisations also miss that AI-assisted changes can introduce inconsistent assumptions across services, especially when one component trusts a value another component merely forwards. In practice, many security teams encounter AI-generated defects only after an authentication bypass, token leak, or privilege escalation has already occurred, rather than through intentional pre-production validation.

How It Works in Practice

Extra controls should be added by classifying the code according to what it touches, then applying a stronger assurance path to the highest-risk classes. The code does not need to be replaced or banned. It needs a more deliberate gate where the security consequence of failure is high.

• Apply manual review when AI-written code handles authentication, authorisation, session cookies, redirect logic, or proxy headers.
• Require secure coding validation when it reads or writes secrets, tokens, certificates, API keys, or key material.
• Test trust boundaries explicitly when the code consumes user input, model output, webhook payloads, or external service responses.
• Check for provenance and approval when the code affects deployment pipelines, infrastructure permissions, or privileged automation.
• Use targeted tests for injection paths, header spoofing, confused-deputy behaviour, and privilege escalation paths.

For AI-assisted development, the strongest pattern is a separate assurance gate for code that affects identity or authority. That gate should include secure review by a human who understands the control objective, plus tests that prove the code rejects malformed trust signals. The OWASP Top 10 for LLM Applications is relevant when AI systems themselves are involved, because prompt injection, insecure output handling, and excessive agency can all create bad code paths or unsafe integration logic.

Where the code is purely cosmetic or isolated from trust decisions, standard development controls may be enough. Where the code changes routing, access decisions, or credential handling, additional review, test coverage, and change approval should be mandatory. These controls tend to break down when generated code is copied into shared libraries without clear ownership because reviewers lose sight of which downstream services inherit the risk.

Common Variations and Edge Cases

Tighter review often increases delivery time and test burden, requiring organisations to balance speed against the risk of an avoidable security failure. That tradeoff is real, but it should be resolved by risk classification rather than by convenience.

There is no universal standard for every AI-written code change, and current guidance suggests a tiered approach. Low-risk UI logic, formatting helpers, and internal calculations may only need normal peer review. High-risk logic deserves stronger scrutiny when it affects identity, secrets, proxy behaviour, or any trust-derived value.

Edge cases appear when generated code is embedded in infrastructure-as-code, automation scripts, or backend services that act on behalf of users or other systems. In those environments, the line between application logic and privileged operations becomes thin, so the same snippet can move from low risk to critical risk depending on where it runs. The NIST AI Risk Management Framework helps teams structure that judgment around govern, map, measure, and manage activities.

Another common variation is code produced by an agentic workflow rather than a single prompt. When an AI agent can call tools, open pull requests, or alter configurations, additional controls should extend beyond source code review into agent permissions, output validation, and change provenance. Best practice is evolving here, but the principle is stable: the more authority the code or agent touches, the more independent assurance it needs.

Related resources from NHI Mgmt Group

• When should organisations add continuous controls for AI agents?
• When should organisations add containment controls to AI agent deployments?
• When should organisations add runtime controls for AI agents instead of relying on monitoring?
• When should organisations add runtime controls to AI applications?