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Govern AI-generated code as part of your normal software-development and supply-chain process: approve the tools, define what data they may receive, keep people accountable for changes they accept, run the usual security gates, and apply tighter controls when an agent can act or code affects critical systems. NIST and OWASP provide a practical baseline, not a single policy that every organization must adopt.
Start with a risk-based policy
AI coding tools range from assistants that suggest code to agents that can inspect repositories, run commands, modify files, open pull requests, or trigger workflows. Their risks differ according to the data they can access, the actions they can take, and the systems their changes affect. A useful policy governs those differences rather than treating all AI-generated code as either inherently unsafe or automatically trustworthy.
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Use established secure-development practices as the baseline. NIST’s Secure Software Development Framework (SSDF) 1.1 provides that foundation; NIST SP 800-218A, published July 26, 2024, adds an AI-focused profile and is intended to be used alongside SP 800-218. These are guidance for improving development practices, not a universal certification or a substitute for organization-specific requirements.
Write the policy so teams can answer four operational questions for every proposed use: what tool is approved, what information it may receive, what actions it may perform, and who reviews its output before it is merged or released.
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Approve tools and evaluate their data handling
Maintain an approved-tool list and a documented path for evaluating new coding assistants, agents, plugins, and Model Context Protocol (MCP) servers. Treat tools and MCP servers that have not been reviewed as untrusted dependencies: approve them before use, pin versions where practical, review changes, and grant only the privileges needed for the task. OWASP’s DevSecOps guidance identifies data leakage, prompt injection through code context, and untrusted tools or MCP servers as risks in AI-assisted development.
Evaluation should cover both the service and its integration in the development environment. Establish what the provider receives, how the product handles submitted context under the organization’s applicable terms, and whether it can take actions beyond suggesting code. Check whether context may include open files, project structure, terminal output, or other material beyond the text a developer explicitly submits. Assess local components and SaaS endpoints as well as inherited model-supply-chain risk, as recommended in OWASP AISVS Appendix C.
- Record the product, relevant configuration, approved use cases, owner, evaluation date, and approval status.
- Review plugins, extensions, MCP servers, and material version or permission changes rather than assuming approval of one component covers every integration.
- Define how teams request an exception or propose a new tool, including the evidence needed and the person authorized to decide.
Set data rules before rollout
Map permitted use to the organization’s existing data-classification rules. Specify which classifications may be used with each approved deployment, and identify when a restricted, enterprise, or self-hosted deployment is required. The right choice depends on the organization’s data, provider terms, and configuration; do not infer protection from a product label alone.
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- Prohibit sending credentials, access tokens, private keys, and other secrets to tools unless a specifically approved, controlled workflow requires it.
- List sensitive repositories, directories, and data types that are out of bounds for each tool configuration.
- Explain how to report accidental disclosure and how to revoke or rotate exposed credentials under the organization’s incident process.
Keep human accountability for accepted changes
The engineer who accepts a suggestion is responsible for understanding and validating it. A generated patch is a proposed change, not evidence that the change is correct, secure, compatible with the surrounding system, or appropriate for the intended use. NIST NCCoE’s DevSecOps guidance says AI-generated content should be monitored and validated by humans and that AI suggestions require rigorous scrutiny.
Require qualified human review before AI-assisted changes are merged. OWASP AISVS describes separation of duties for AI-generated changes as a stronger control: where the risk warrants it, the reviewer should be a different person from the engineer who accepted or prepared the change. Reviewers should assess the behavior and design of the patch, not just whether it compiles or whether its explanation sounds plausible.
Set elevated approval requirements for changes to authentication, authorization, cryptography, identity and access management (IAM) policy, CI/CD, deployment manifests, sandboxing, or network policy. The required approver and evidence should follow your existing risk and change-control process; the point is to raise scrutiny when a mistake could materially expand access, weaken a security boundary, or affect release operations.
Apply ordinary security gates to AI-assisted pull requests
Do not create a weaker path for code because a tool produced it, or assume every AI-assisted change needs a wholly separate review pipeline. Apply the relevant secure-development controls in the normal pull-request workflow. OWASP AISVS recommends security analysis on pull requests and qualified human review; its Appendix C identifies controls such as static and dynamic analysis, secret scanning, infrastructure-as-code scanning, and software composition analysis.
- Run the scanners relevant to the repository and change, including static or dynamic analysis, secret scanning, infrastructure-as-code scanning, and software composition analysis where applicable.
- Block or escalate serious findings according to the organization’s severity policy. Permit exceptions only through a written, authorized process that records the rationale and disposition.
- Review dependency changes, new network calls, permissions, input handling, and security-sensitive behavior in the context of the application.
- Add human-authored negative and adversarial tests for important boundaries and failure cases. OWASP’s Secure Coding with AI guidance recommends independent adversarial and negative test cases.
Tests generated by the same assistant that proposed the implementation can help with coverage, but passing those tests does not establish that the code is secure. The tests may share the implementation’s assumptions or omit the cases that matter. Review test intent and add independent checks for boundary conditions, authorization failures, malformed input, and other risks relevant to the change.
Use tighter controls for agents and CI/CD access
An agent that can read, write, execute, or deploy should be governed according to the access and impact of those capabilities—not treated as a suggestion-only assistant. OWASP recommends treating agent access like equivalent access granted to a human. Scope credentials to the task, define allowed actions, require human approval for consequential operations, log activity, and provide a way to revoke access.
In CI/CD, avoid exposing secrets or broad write permissions to agents running in response to untrusted pull-request events. Limit which repositories, branches, files, commands, and external services an agent can use. Require explicit review when an agent changes files that execute during installation, build, test, or deployment. Scrutinize new network access and external downloads because changes in these areas can affect what code runs or what data leaves the environment.
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- Constrain the action surface. Use explicit allowlists for commands and operations; separate read access from write, merge, release, and deployment permissions.
- Gate high-impact actions. Require human authorization before consequential changes, merges, deployment actions, or access to sensitive resources.
- Monitor and recover. Keep an audit trail, make revocation practical, and include agent activity in the organization’s incident response process.
Preserve traceability without collecting more than needed
Keep enough information to connect AI-assisted work to the code and release artifacts so an incident can be investigated. OWASP AISVS proposes stable correlation identifiers that link prompt and response records through commit, build, and deployment, along with tamper-evident storage for relevant audit records.
Define the records to retain, who may access them, and how long they are kept under the organization’s privacy and retention rules. Traceability does not require indiscriminately storing every prompt or every item of project context; records themselves may contain sensitive information. Choose an approach that supports investigation while limiting collection and access appropriately.
Choose controls by data, autonomy, and impact
There is no single control package for every team or organization. Compare a proposed tool or use case across these dimensions, then set approval and review requirements accordingly:
| Decision dimension | What to assess | Governance implication |
|---|---|---|
| Data sensitivity | What repository content or other context may reach the provider, and how is it handled? | Restrict eligible data, configure exclusions, or require a deployment with controls appropriate to the data. |
| Degree of autonomy | Does the tool only suggest code, or can it read, edit, execute, open pull requests, or deploy? | Increase authorization, logging, approval, and revocation controls as the tool gains action capability. |
| Access scope | Which files, credentials, repositories, commands, and external services are available? | Reduce privileges to the task and make consequential permissions separately controlled. |
| Security evaluation | What review, analysis, and testing will validate the tool’s output and integration? | Use the applicable SDLC gates and add independent security tests for sensitive behavior. |
| Auditability | Can relevant activity be connected to commits, builds, and deployments? | Preserve appropriately protected records that enable investigation under retention rules. |
| Code and system sensitivity | Could a defect affect authentication, cryptography, deployment, policy, or other critical controls? | Require more qualified review and stronger approval for high-impact changes. |
| Operational fit | Can the controls work with existing repositories, CI/CD, and review processes? | Integrate governance into established gates so teams can apply it consistently. |
Review the policy as tools and incidents change
Assign an owner to revisit the approved-tool list, configurations, permissions, and exception decisions when products or integrations change and after relevant incidents. Use observed failures and security findings to adjust data rules, evaluation criteria, test coverage, and agent permissions. A governance program is useful only if teams can apply it in their actual development workflow and the organization can verify that its controls operate as intended.
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