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Set a policy that treats AI-assisted code as code to be reviewed—not as automatically acceptable or automatically prohibited. Require the same technical, security, and license checks as other changes, plus enough disclosure and provenance information for reviewers to assess the contribution. Assign named approval owners, define when to escalate uncertain rights questions, and retain the decisions and compliance records needed for the way the software will be distributed.
AI assistance does not transfer responsibility away from the contributor or the organization. A workable policy makes that responsibility explicit and gives engineers a repeatable route from proposed change to approval.
What an AI-assisted open-source approval policy should do
An approval policy connects three decisions that are often mistakenly collapsed into one: whether a change is technically acceptable, whether its licensing and provenance are understood, and whether the organization is prepared to use or distribute it under the applicable conditions. AI assistance may affect what evidence reviewers need, but it does not replace any of those decisions.
Keep the policy operational. It should say which repositories and products it covers, who reviews each kind of risk, what information must accompany a change, how exceptions are handled, and where records are kept. It should also be communicated to contributors and supported with trained, adequately resourced roles. OpenChain’s License Compliance Specification 3.0 calls for a written policy governing open-source license compliance of supplied software, with defined responsibilities and processes for review and resolution (OpenChain License Compliance Specification 3.0).
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The policy can be scoped to a product, business unit, or the whole organization. State the scope and its limits plainly; a process for software distributed externally may differ from internal-use approval or from contributions made directly to an upstream project.
Define the policy’s scope before setting approval rules
Start by naming what the policy governs. “AI code” alone is too vague: reviewers need to know which software, contribution paths, and uses are in scope. Address first-party repositories as well as external components, because license compliance depends on both the code developed internally and the dependencies used with it.
- Covered entities and teams: identify the organization, product groups, engineering teams, and contractors whose changes must follow the process.
- Covered repositories and software: list the products, services, libraries, internal tools, or repository classes governed by the policy. State whether it covers software supplied to customers, internal deployments, or both.
- Covered AI assistance: decide whether the policy applies to code generation, material code completion, AI-assisted modifications, or other AI-produced content that becomes part of a software contribution. If the organization requires disclosure only for some uses, define the threshold so contributors can apply it consistently.
- Covered contribution paths: distinguish changes merged into company-controlled repositories from contributions submitted to upstream projects. Upstream projects may set their own disclosure and contribution requirements.
- Scope limits: identify teams, systems, or software not yet covered and who owns deciding whether they are brought into scope.
OpenChain allows a compliance program to have a declared scope and limits. That makes a bounded, well-operated policy preferable to a sweeping statement that the organization cannot consistently apply.
Set approval criteria that apply to every change
Write the non-negotiable acceptance conditions once, then make the review path proportionate to risk. An AI-assisted change should meet the project’s normal functional, quality, and security expectations. The human contributor must understand the submitted code and accept responsibility for the contribution, including AI-assisted portions. The change must also be acceptable under the AI tool’s terms and the destination repository’s rules.
For licensing and provenance, require that reviewers can identify and resolve concerns about third-party material. Do not merge with an unexplained license conflict, unexplained inclusion of potentially copyrighted material, or missing notice or attribution that the applicable terms require. Preserve copyright notices and license text where needed. A scanner match should be investigated, not treated as a final legal conclusion.
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The Linux Foundation says that content generated wholly or partly with AI tools can be contributed to its projects, while reminding contributors to check tool terms for contractual restrictions that conflict with project license or intellectual-property policies. It also says that where third-party copyrighted content appears, the contributor should confirm that it is permitted, licensed, or in the public domain, and provide notice and attribution as applicable (Guidance Regarding Use of Generative AI Tools for Open Source Software Development). This is guidance for Linux Foundation projects, not a blanket determination that every output can be used in every project or jurisdiction.
OpenInfra’s policy provides a useful model for handling disclosure: its reviewer checklist says, “The disclosure label is not grounds for rejection; it is context.” It recommends labels such as “Assisted-By” and accepts “Generated-By,” alongside similarity and licensing checks where available. These are project guidance examples, not universal legal requirements. The organization should define the labels and evidence it needs rather than assume every upstream project uses the same convention (OpenInfra Foundation Policy for AI Generated Content).
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Approval is stronger when each reviewer has a bounded responsibility. A developer should not be expected to resolve every technical, security, and legal question alone. Name the role or team that owns each review, provide a route to qualified legal expertise, and ensure the process has enough capacity to be used in practice.
| Role | What the role decides | Escalate when |
|---|---|---|
| Engineering reviewer or maintainer | Whether the change is understandable, correct, maintainable, tested, and consistent with repository contribution rules. | The contributor cannot explain material generated code, the change lacks adequate tests, or technical behavior remains unclear. |
| Security reviewer | Whether the change introduces or alters security-sensitive behavior and whether the required security review is complete. | The change affects a sensitive component, creates unresolved risk, or requires specialist threat or security analysis. |
| Open-source compliance lead or OSPO | Whether license and provenance evidence is adequate, required notices are identified, and the organization’s process has been followed. | A license match is uncertain, obligations may conflict, or distribution and component records are incomplete. |
| Legal counsel or qualified legal reviewer | Questions requiring legal interpretation, including uncertain rights, contractual terms, or potential third-party infringement. | Rights or license questions cannot be resolved from established policy and reliable evidence. |
Set escalation thresholds in advance. For example, specify that unresolved license incompatibility, uncertain ownership or permission, or a suspected substantial third-party match cannot be cleared solely by an engineering approver. The precise threshold should reflect the organization’s products, jurisdictions, and risk tolerance; the cited policy materials do not settle jurisdiction-specific copyright ownership or the terms of any particular AI provider.
For high-impact integrations, an approval board can provide a formal decision point. The Linux Foundation’s program guide gives an example of requiring review-board approval before open source is integrated into a product. A smaller organization or lower-risk scope may instead designate a compliance reviewer and escalation contact. Choose a model the organization can staff, and document who can make the final decision (Creating an Open Source Program).
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Require useful evidence with each change
The evidence record should let another reviewer understand what changed, how AI was involved, what checks were run, and why the change was approved. Keep it proportionate: not every completion needs a lengthy narrative, but a material generated contribution should not be indistinguishable from an unreviewed code paste.
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- AI assistance disclosure: whether a tool was used and, if required by policy, the tool or model identifier and the portions generated or materially assisted. Define what counts as material assistance.
- Human confirmation: a contributor statement that they reviewed, understand, and take responsibility for the submitted code.
- Provenance findings: identified third-party material, relevant license or permission evidence, and unresolved similarity findings or their resolution.
- Review record: technical, security, and compliance approvals as applicable; exceptions; and the rationale for decisions.
- Distribution records: required notices and attributions, with a link to the relevant component record or SBOM when one exists.
If the repository or organization requires a particular disclosure label, put it in the contribution instructions and show examples. OpenInfra’s “Assisted-By” and “Generated-By” labels can help communicate whether a tool assisted or generated content, but the project’s own rules govern which label is appropriate. OpenInfra also suggests adding relevant prompt or background context and indicating how much of a change came from the tool. Do not collect prompts automatically as a universal rule: set a specific retention and access approach appropriate to the organization’s needs and the information those records may contain.
Disclosure is useful context, not a substitute for review. A label by itself does not establish that code is original, licensed compatibly, secure, correct, or acceptable under tool terms. Conversely, the presence of an AI label should not itself be treated as proof of a defect.
Build license and component checks into the workflow
License review must account for how software is used and distributed. The relevant questions can change depending on whether software is supplied as source or binary, whether open-source components are modified, whether combinations trigger additional obligations, and which attribution requirements apply. OpenChain identifies these as common licensing situations that an organization’s procedures should address.
Use software composition analysis to inventory and track licenses in both first-party source and external dependencies. A scan limited to dependencies misses part of the picture; a scan limited to first-party source misses another part. The Linux Foundation’s quick reference guide cautions that both sides matter and recommends generating a build SBOM in CI/CD when practical, while retaining license, copyright, and other compliance artifacts required for distribution (Open Source License Best Practices – Quick Reference Guide).
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SPDX is a format for communicating software component, license, copyright, and security-reference information. It can make component information easier to exchange and preserve, but an SBOM does not itself decide whether a proposed integration is approved. In the Linux Foundation’s description of the compliance stack, OpenChain represents process standards, SPDX supports SBOM data exchange, and FOSSology is an example of a scanning tool. These layers have different jobs and complement one another (Open Source Compliance for Organizations).
| Layer | What it contributes | What it does not decide |
|---|---|---|
| Compliance process | Defined policy, roles, review, approval, issue resolution, and records; OpenChain is the cited process-standard example. | Whether a particular scan result or legal question is resolved without review. |
| Component data exchange | A common way to communicate component, license, copyright, and security-reference information; SPDX is the cited format. | Whether the organization accepts a component or satisfies every obligation by generating an SBOM. |
| Scanning and inventory tools | Help identify components and license indicators for human review; FOSSology is a cited example. | Final approval, legal interpretation, or the resolution of every uncertain match. |
Choose tooling based on whether it covers source and transitive dependencies, how it surfaces provenance and uncertain matches, how it fits developer workflows and CI/CD, and whether it can produce and archive SBOMs and required notices. Also consider staffing, escalation access, and the scope of the program. The cited official materials describe complementary layers; they do not establish a current vendor ranking or comparative benchmark. Tools support the decision, but the organization’s named approver owns it.
Preserve the compliance artifacts that distribution requires
Do not assume every project needs every possible artifact. Select and retain records according to the applicable licenses, the software’s distribution circumstances, and the organization’s process. OpenChain Specification 2.1 lists examples that can include attribution notices, source code, build and installation scripts, license copies, copyright notices, modification notices, component bills of materials, and SPDX documents (OpenChain Specification 2.1).
Connect those artifacts to the release or product they concern. A record should make it possible to find the component information and approval rationale for a shipped build, rather than leaving compliance evidence scattered across an engineer’s local files or a closed review thread. Define an owner and retention location for notices, scan results, SBOMs, approvals, and exceptions.
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A policy becomes usable when contributors can follow it without guessing which team to contact. The sequence below can be adapted to a repository’s pull-request workflow or a formal release process.
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- Classify the change. Identify the repository, product, intended use, distribution path, and whether the change is covered by the policy. Route out-of-scope cases to the named policy owner rather than assuming they are exempt.
- Record AI involvement. Follow the project’s required disclosure convention. Identify the tool or model if policy requires it, describe the affected portions, and provide enough context for review.
- Review and test the code. The human contributor and engineering reviewer assess behavior, tests, maintainability, and repository rules. The contributor must be able to explain the material code and accept responsibility for the full change.
- Run the appropriate security review. Send security-sensitive or otherwise risk-qualified changes to the named security reviewer using the organization’s established thresholds.
- Check provenance and licenses. Review relevant scan results, investigate uncertain matches, assess tool terms and contribution rules, and identify any required notices or attribution. Do not treat a tool’s output as a final legal determination.
- Escalate unresolved issues. Hold the change from approval where a material rights, compatibility, or security question remains. The designated compliance lead or counsel determines the next review step according to the escalation rules.
- Approve and preserve the record. Capture the approvals, rationale, exceptions, and applicable component or release artifacts in the specified system.
- Revisit the decision when circumstances change. Reassess when distribution, modifications, component combinations, applicable project rules, or relevant tool terms change.
OpenChain calls for processes to identify, track, review, approve, and archive component and license information, and for a process to review and update the policy and its supporting tasks. This makes the approval route part of a wider compliance program, rather than a one-time AI-specific checklist.
Set exceptions, remediation, and policy review rules
Exceptions should be explicit, bounded, and reviewable. Name who may grant one, what evidence they need, whether legal or security input is mandatory, how any non-compliant condition will be remediated, and when the exception expires. Record the affected component or change, the risk accepted, the decision owner, and the expiration or review trigger.
Do not let an exception silently become the default path. If a case cannot meet the normal criteria, record whether the organization will remediate, remove or replace the affected material, seek additional permission, or decline the integration. OpenChain calls for processes to resolve non-compliant cases and to review and update the compliance policy and supporting work.
Review the policy when AI tool terms, upstream contribution rules, product distribution, or the organization’s compliance program changes. Also revisit whether the stated scope remains accurate and whether the named reviewers have the time and expertise to perform their duties. The policy should be accessible to engineering teams and supported with training, not just approved by leadership and left in a document repository.
Example policy outline to adapt
The following outline is a starting structure, not legal advice or a substitute for counsel’s review of the organization’s products, jurisdictions, tool contracts, and licenses.
- Purpose and scope: covered organizations, repositories, products, use cases, AI assistance, and contribution paths; exclusions and scope owner.
- Acceptance criteria: technical, quality, security, provenance, license, notice, AI-tool-term, and project-rule requirements.
- Disclosure and records: what contributors disclose, the labels or fields accepted, required evidence, and the system of record.
- Roles and approvals: engineering, security, compliance, and legal responsibilities; thresholds and escalation contacts.
- Inventory and artifacts: source and dependency scanning, SBOM expectations, notices, license materials, and retention responsibilities.
- Exceptions and remediation: authorized approvers, required rationale, remediation path, expiration, and monitoring.
- Training and review: how the policy is communicated, who maintains it, and what changes trigger a review.
For a small project, some roles may be assigned to the same person, but the decisions still need to be addressed. For a larger product organization, separate reviewers and a board may be appropriate. The key is to make ownership, evidence, escalation, and recordkeeping clear enough that two teams facing the same kind of change will not invent incompatible approval rules.
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