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Set a clear rule: a named person owns every AI-assisted change they accept and ship. That person must understand the code, verify it through your normal security and testing process, and record enough about the AI contribution for reviewers to assess it. Pair that accountability with approved tools, firm data boundaries, risk-based review, and ordinary license checks.
What an AI code policy should cover
Write one policy that applies across the ways AI can influence a change, rather than limiting it to code completion. State whether it covers chat-generated snippets, generated tests, agent-authored changes, AI review comments, and contributions to open-source repositories. Define which tools are approved, who can approve exceptions, and which repository or project rules take precedence.
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Use the policy as an engineering control, not a claim that every legal question has been resolved. The appropriate treatment of a contribution can depend on jurisdiction, contracts, the human contribution, and third-party material.
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Assign an individual contributor as the owner of each AI-assisted change. The owner should be able to explain what the code does, why it belongs in the project, and how it was verified. AI assistance does not transfer responsibility to the tool or its provider. Microsoft’s developer guidance puts it plainly: “The code your AI agent generates is code you ship, and you are accountable for everything in your app regardless of how it was written.” Microsoft’s security and responsible-AI guidance is written for Windows development, so apply its broader accountability principle without assuming every product-specific detail applies to all platforms.
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Set approved tools and data boundaries
Keep an approved-tool list and evaluate each service using its actual terms, account settings, and any negotiated agreement. A useful review considers data retention and training provisions, prompt controls, provenance or similarity features, auditability, integration with review and tests, and the risk of sending sensitive code outside your organization. These are comparison criteria, not evidence that one product is universally safer than another.
- Never enter credentials, secrets, or access tokens in prompts.
- Avoid real customer data and personally identifiable information; use synthetic or sanitized examples where possible.
- Explicitly decide whether proprietary source code may be sent to an external AI service, and under what conditions.
- Check the governing contract and current account settings instead of relying on a generic description of a service.
For example, GitHub’s Terms of Service describe AI Features terms, while noting that training and data-use provisions can differ between individual licenses and customer or volume agreements. Those terms should not be assumed to govern tools from other providers or a customer’s negotiated contract.
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Review AI-assisted code like shipped code
Generated code is untrusted input, not a shortcut around engineering controls. The human owner and reviewer should read it, test it, and use the same secure-coding expectations applied to human-written changes. Microsoft says AI tools “don’t remove the need for code review. They change what you’re reviewing, not whether you review.” GitHub’s terms likewise state: “You are responsible for reviewing, testing, and validating any Output before use.”
Apply your established review, static-analysis, testing, and issue-triage process. Record and resolve findings under the same rules used for other code. NIST SP 800-218A recommends that code review and analysis policies cover code for AI models and related components; it also recommends scanning AI models for malware, vulnerabilities, backdoors, and other security issues. The July 2024 final profile augments the Secure Software Development Framework (SSDF) version 1.1, and is intended to be used alongside it—not as a complete policy or legal standard. See NIST SP 800-218A.
Scale review to risk and uncertainty
Use the change’s impact, scope, exposure, and uncertainty to determine how much evidence and scrutiny it needs. These are suggested policy tiers; adapt them to your architecture and existing review requirements.
| Change profile | Suggested policy treatment |
|---|---|
| Small, low-impact completion within a well-understood area | Ordinary review, tests, and analysis required for that codebase; the owner still reads and understands the change. |
| Large, cross-module, or externally exposed change | Require more review evidence, appropriate tests, and focused analysis of interactions and failure modes. |
| Authentication, cryptography, authorization, payments, sensitive data access, or deployment boundaries | Require focused scrutiny by reviewers with relevant expertise, plus tests and security analysis appropriate to the system’s threat model. |
A tool’s confidence or a polished explanation is not a substitute for evidence. Reviewers should be able to see the relevant diff, test results, and material analysis findings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a proportionate disclosure and provenance record
Tell contributors what a pull request or change record must disclose. A practical record can say that AI materially contributed, identify the affected portions, name the tool or model when known, and summarize the verification performed. This gives reviewers useful context without treating every AI interaction as something that must be archived.
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Avoid requiring blanket prompt retention unless a specific, approved need justifies it. Prompts can contain confidential source code, personal information, or security-sensitive details; retaining them can create additional privacy and security risks. Keep records focused on the contribution and its verification, and define any retention rules through the organization’s existing data-governance process.
The GSA TTS AI-Assisted Contribution Policy is a detailed repository-level example covering accountability, disclosure, provenance, verification, data handling, security review, and licensing. Its maintainers explicitly state that it is repository-specific and is not official GSA policy or legal advice.
Preserve licensing and attribution obligations
Do not list the model as the author or treat generated output as automatically original, rights-free, or compliant with your project’s license rules. Preserve notices and licenses for identifiable third-party material, and run the same license-compliance checks used for other code. If output resembles existing material or its source is uncertain, follow your organization’s process for investigating and resolving that concern before shipping.
GitHub’s terms say it does not claim ownership of input or output, but also warn that output may resemble training data or be subject to third-party copyright or open-source terms; users remain responsible for determining whether a license is required. That is a statement about GitHub’s terms, not a universal legal conclusion for every AI tool or jurisdiction. The U.S. Copyright Office’s AI study page records publication of Part 2 on copyrightability of generative-AI outputs on January 29, 2025, and a pre-publication Part 3 on generative-AI training on May 9, 2025. Those dates do not settle the ownership or licensing status of every AI-assisted code contribution.
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Turn the blueprint into enforceable rules
- Define scope: list the contribution types covered, approved tools, exception approvers, and repository-specific requirements.
- Assign ownership: require a named human owner who understands each accepted change.
- Set boundaries: prohibit secrets and real customer data in prompts, and specify whether proprietary code may be sent to each approved service.
- Set review tiers: map change impact and uncertainty to reviewer expertise, tests, and analysis.
- Require a concise record: capture material AI assistance, affected areas, tool or model when known, and verification—not unnecessary prompt contents.
- Apply license checks: preserve third-party notices and run established compliance checks before merge or release.
- Revisit the rules: review tool terms, account settings, and the policy when services, contracts, or engineering risks change.
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