What are the first controls to add when developers start using AI coding tools?

Start with inventory before you add guardrails

The first control is visibility. Before teams can secure AI coding tools, they need a clear inventory of which assistants, plugins, IDE extensions, CLI tools and agent features are approved, where they run, and which repositories or environments they can touch. That inventory should also define what data developers may paste into prompts, especially source code, secrets, customer data and incident material.

A practical starting point is an approved-tool list paired with a simple data-use rule set. If a tool is not on the list, it is shadow AI and should not be used for code generation or review. If a tool is approved, the policy should still say which classes of data are prohibited in prompts and which require explicit review before use.

For a useful policy baseline, pair the tooling inventory with an approved-use standard such as AI Acceptable Use Policy Template, so the organisation can define permitted tools, data rules and human-review expectations in one place.

Once the inventory exists, the next decision is scope. Tools used in local IDEs, terminal sessions and CI jobs do not all need the same trust level. A coding assistant that can open files, suggest dependencies and write commits deserves stricter controls than a chat-only helper that never sees production code.

Put code review gates in front of merge and deployment

The next control is to treat generated code as untrusted until it passes the same security checks as any other code, plus a few AI-specific ones. The direct answer already points to the right order: review first, then merge or deploy only if the code passes secret scanning, dependency review and authorisation testing.

Secret scanning matters because AI tools often reproduce patterns from context, examples or public code and can surface credentials, tokens or configuration values that should never enter the repository. Dependency review matters because generated code can introduce new packages, version drift or unsafe transitive dependencies without a human intentionally choosing them. Authorisation testing matters because a model can produce code that compiles and runs but bypasses access checks, overreaches on permissions or exposes sensitive functions.

Keep the review rule simple: if the generated change can create access, move data, call external services or invoke privileged actions, it must be checked before merge. That catches the most common failure modes early, when the blast radius is still small.

For development teams that want a practical testing baseline, OWASP Cheat Sheet Series gives implementation guidance that maps well to authentication, secrets handling, input validation and session control in code review workflows.

Why the first controls should focus on leakage, dependency and privilege

AI coding tools tend to fail in three repeatable ways: they expose secrets, they introduce risky dependencies, and they generate code that assumes too much authority. Those are the earliest control points because they are common, testable and easy to enforce in CI/CD without waiting for a major incident.

The biggest mistake is to start with a long policy document and no pipeline enforcement. If developers can still merge generated code with embedded credentials, unvetted packages or broken access checks, the organisation has only documented the risk, not reduced it. The right sequence is inventory, data rules, automated scanning and authorisation testing, then broader governance once the basics are stable.

Teams that use AI coding tools across IDE, terminal and CI should also standardise where prompts and context can come from, because otherwise the same tool may be safe in one workflow and unsafe in another. Guidance from AI Coding Agents Security Guide is useful here because it focuses on secrets in context, over-scoped tokens and sandboxing for coding assistants and agents.

Risk and Threat Considerations

AI coding tools expand the attack surface by making code generation faster than review, which can let unsafe patterns enter the pipeline before anyone notices. The most material risks are secret exposure, supply-chain drift through new packages, and privilege mistakes in code that calls APIs, cloud services or admin functions.

Failure mechanism: A developer pastes sensitive context into the tool, or the tool generates code that hard-codes secrets, adds an unvetted dependency, or weakens an access check, and the pipeline accepts it because the change looks syntactically valid.

Impact: The result can be credential leakage, unauthorised access, malicious package introduction, or production exposure caused by code that was machine-generated but never security-reviewed with the same discipline as human-written code.

Practitioner Guidance

What to prioritise: Start with the controls that are cheapest to enforce and most likely to catch real failures, namely approved-tool inventory, prompt data rules, secret scanning and dependency review. Those four controls usually deliver more risk reduction than a broad governance programme that is not wired into the pipeline.

What to verify: Confirm that generated code cannot move to merge or deployment unless the same security gates run every time, including checks for leaked secrets, added dependencies and access-control regressions. If those gates are optional, the control set is not yet real.

Practitioner takeaway: The first job is not to ban AI coding tools, it is to make their use observable, bounded and security-gated before generated code can affect production.

Related resources from NHI Mgmt Group

• How should platform teams adapt documentation for developers using AI coding tools?
• What breaks when developers use native AI coding tools outside browser controls?
• Should organisations reduce senior developers when adopting AI coding tools?
• Why do AI coding tools still need strong review and test controls?