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AI-assisted code can be used in open-source projects, but it should pass the same substantive quality, security, and licensing review as other code. A workable approval policy makes that standard operational: it defines which code and repositories are covered, who reviews and approves changes, what provenance and compliance evidence is recorded, and when uncertain cases go to open-source specialists or counsel.
There is no universal rule that every AI-assisted line must be rejected, or that every contribution must carry the same disclosure label. The Linux Foundation says AI-generated content can be contributed to its projects, subject to the contributor checking tool terms and addressing third-party material; individual projects may set additional rules. Your organization should turn those principles into a process fitted to its products, distribution methods, and risk.
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What the policy needs to decide
An approval policy is not merely a statement that AI tools are allowed or prohibited. It is the set of rules and records that let an organization decide whether a particular change is fit to merge, ship, or contribute upstream. AI assistance does not transfer responsibility from the human contributor or remove ordinary contribution requirements.
OpenChain Specification 3.0 states, “A written open source policy shall exist that governs open source license compliance of the supplied software.” Its broader approach includes defined scope, responsibilities, competence, review, issue handling, and adequate resources. The policy can apply to a product, department, or organization, but it should say exactly what it covers and what it does not. Read the OpenChain License Compliance Specification 3.0.
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Use the policy to make four decisions explicit:
- Scope: Which teams, repositories, products, tools, dependencies, and contribution routes are covered?
- Approval bar: What must be true before AI-assisted material can be merged or distributed?
- Ownership: Which people review technical quality, security, and licensing, and who can approve an exception?
- Evidence: What records accompany a change and where are they retained?
Keep internal approval guidance distinct from legal advice. General policy sources do not settle who owns a particular AI output under a particular jurisdiction, what a provider’s contract means in a specific case, or whether an output infringes someone’s rights. Escalate unresolved legal questions to qualified counsel.
Define scope before choosing controls
Write the boundary of the program in plain language. State whether it applies to first-party source code, external components, AI-enabled development tools, products distributed outside the organization, internal-only software, and contributions made to upstream projects. Specify which legal entities, product lines, business units, and repositories are included. If separate rules apply to public contributions, identify them and link to the relevant project contribution requirements.
Scope matters because a code change used only in an internal prototype may involve different review steps from a modified open-source component shipped to customers. It does not mean that internal use is automatically free of obligations: the organization should determine its actual use and distribution circumstances rather than assume that a single policy covers every case in the same way. OpenChain allows organizations to define a program scope, while requiring that scope and its limits be declared.
Include dependencies in the scope, not only code written by employees. The Linux Foundation cautions that scanning only first-party code or only external dependencies leaves an incomplete compliance picture. A policy should cover both, including dependencies that enter through build and packaging processes where they are relevant to the product. See the Linux Foundation’s Open Source License Best Practices quick reference.
Set the approval criteria for every change
Use one baseline for AI-assisted and non-AI-assisted contributions. A reviewer should be able to approve the change based on its quality, security, provenance, and licensing status—not simply on whether a tool was involved. Add the specific evidence and disclosure needed to make those judgments possible.
- Technical fitness: The change meets the project’s ordinary functional, testing, maintainability, and code-review requirements. The human contributor can explain what the code does and why it is needed.
- Security: The change receives the security review appropriate to its risk, including review of sensitive areas such as authentication, cryptography, permissions, data handling, or externally exposed interfaces.
- Traceable origin: The change record gives reviewers enough information to understand whether AI was used and which portions were generated or materially assisted, according to the organization’s disclosure rule.
- Rights and licensing: There is no unresolved incompatible license or unexplained third-party copyrighted material. Required license text, copyright notices, and attributions are preserved.
- Terms and project rules: The contributor has checked applicable AI tool terms and repository contribution rules for restrictions that could conflict with the project’s license or intellectual-property policies.
- Human accountability: A named contributor has reviewed and understood the submission and accepts responsibility for the whole contribution, including the AI-assisted parts.
The Linux Foundation’s guidance says, “Code or other content generated in whole or in part using AI tools can be contributed to Linux Foundation projects.” It also tells contributors to check tool terms for contractual restrictions that conflict with project license or IP policies. If output appears to contain third-party copyrighted material, the contributor should establish that it can be used—such as through permission, an applicable license, or public-domain status—and provide notices and attribution where required. This is guidance for Linux Foundation projects, not a substitute for checking the target project’s own rules. Read the Linux Foundation guidance on generative AI tools for open-source development.
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Disclosure helps reviewers understand context; it is not itself proof that output is safe, infringing, or unsuitable. OpenInfra’s reviewer guidance says, “The disclosure label is not grounds for rejection; it is context.” Its policy recommends disclosure labels and similarity or licensing checks where available, while retaining human responsibility and project-specific requirements. Read the OpenInfra Foundation policy for AI-generated content.
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Choose a disclosure rule reviewers can use
Decide whether contributors must disclose all use of AI coding tools, only material assistance, or generation of submitted code. Avoid a rule so broad that reviewers cannot interpret it, such as requiring a disclosure for every autocomplete suggestion without defining how that affects review. Conversely, do not let a contributor’s silence prevent a reviewer from asking about origin when there is a concrete reason to do so.
Specify what counts as material assistance and how the record should identify affected portions. Possible policy elements include:
- Whether disclosure applies to generated code, substantive rewrites, tests, documentation, or other submitted content.
- Whether tool or model identifiers are required, and whether a version or access date is necessary for the organization’s records.
- Whether the contributor should summarize relevant prompt or background context, without requiring sensitive prompts or confidential data to be copied into a public change record.
- How the contributor indicates the approximate extent of generated or assisted content when that helps a reviewer assess provenance.
- What to do when an AI feature is embedded in an editor and the contributor cannot reliably attribute individual suggestions.
For example, OpenInfra recommends the labels Assisted-By and Generated-By, and suggests adding relevant prompt or background context and indicating how much of a change came from the tool. Treat those as examples from that project’s policy, not universal legal requirements; adopt labels only if they fit your repositories and contribution workflow.
Require an affirmative contributor confirmation that the submission has been reviewed and understood. A disclosure label does not replace that confirmation, technical review, license checks, or required notices. For external contributions, publish the project’s disclosure rule where contributors will see it before submitting a change.
Assign reviewers and escalation authority
Do not leave difficult licensing calls to whichever engineer happens to review a pull request. Name roles, the decisions each role owns, and a reachable escalation path. In a small organization, one person may hold several roles; the policy should still make each responsibility clear and ensure that legal expertise is accessible when needed.
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| Role | Primary responsibility | When to involve them |
|---|---|---|
| Engineering reviewer | Assess behavior, tests, maintainability, and whether the contributor understands the change. | Every change under the organization’s normal review rules. |
| Security reviewer | Assess security exposure and required mitigation for risk-sensitive code. | When a change touches security-sensitive functionality or crosses a defined risk threshold. |
| Open-source compliance lead or OSPO | Interpret process requirements, review license and provenance findings, and maintain compliance records. | Uncertain component identification, license compatibility, required notices, or process exceptions. |
| Legal counsel | Advise on uncertain rights, contracts, ownership, infringement, or license interpretation. | Cases the technical or compliance reviewers cannot resolve with documented evidence. |
| Approval board or designated senior approver | Authorize high-impact integrations or exceptions where the organization assigns that authority. | High-impact cases defined by policy, rather than every routine change. |
The Linux Foundation’s program guide gives an approval board reviewing open-source integrations before product inclusion as one possible model. That is not a requirement for every organization: a designated reviewer or compliance lead may be more proportionate for a smaller or lower-risk scope. Whatever model you choose, provide time, training, and access to expertise. OpenChain calls for assigned responsibilities, competence, adequate resourcing, legal expertise access, policy review, and resolution of compliance issues. See the Linux Foundation guide to creating an open-source program.
Record evidence with the change
Make the evidence record part of the pull request, change-management system, or linked compliance workflow. Capture enough to reconstruct the decision later without making every contributor complete an excessive form. A practical record can include:
- Repository, commit or pull request, affected product, and intended use.
- Whether AI tools were used; the tool or model identifier if required; and which portions were generated or materially assisted.
- Contributor confirmation that the output was reviewed and understood.
- Identified third-party material and the available provenance, permission, license, or public-domain basis for using it.
- License scanner results, relevant source or dependency records, and human resolution of uncertain or conflicting matches.
- Technical, security, and compliance approvals, plus exception decisions and their rationale.
- Required notices, attribution, and a link to the applicable SBOM or component record.
Set a retention location and access rule for these records. Do not assume every organization must retain prompts or publish model identifiers; choose evidence that is useful for the policy’s purpose and consistent with confidentiality and security needs. An approval record should show what was assessed and who decided, not merely preserve an unreviewed scanner output.
Inventory components, scan licenses, and preserve artifacts
AI-generated first-party code is only one part of a product’s open-source picture. Dependencies, copied or incorporated third-party material, build inputs, and distributed components can all affect compliance. A repeatable process inventories components and licenses, tracks review and approval, and preserves the artifacts required by the actual license and distribution situation.
Use scanning as evidence, not as the approver
Software composition analysis can help identify licenses and track dependencies across first-party source and external components. It can surface possible matches or issues for human review, but a scan result is not a legal conclusion and a clean result is not proof that no obligations exist. The organization’s named reviewer remains responsible for resolving uncertainty and approving the use.
Generate and retain component records
Generate a build SBOM in CI/CD when practical, and keep it connected to the build or release it describes. Preserve applicable license and copyright information and other compliance artifacts needed for distribution. SPDX provides a common format for communicating component, license, copyright, and security-reference information. The Linux Foundation describes a complementary stack: process standards such as OpenChain, SBOM data exchange such as SPDX, and scanning tools such as FOSSology. These layers do different jobs; none substitutes for an accountable approval decision. Learn about the Linux Foundation Open Compliance Program.
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Match artifacts to how the software is used
Do not produce a generic artifact bundle without checking obligations. Review whether the organization distributes source or binaries, modifies open-source components, combines software in ways that trigger additional obligations, or must provide attribution. Depending on the applicable licenses and circumstances, artifacts may include attribution notices, source code, build or installation scripts, license copies, copyright and modification notices, component bills of materials, and SPDX documents. OpenChain Specification 2.1 lists these types of compliance artifacts; it does not mean every project needs every item. See the OpenChain Specification 2.1 artifact guidance.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use escalation, exceptions, and remediation deliberately
A useful policy tells people what to do when evidence is missing or a check raises a concern. Define who can pause a merge or release, who can resolve the issue, and what must be recorded before work resumes. Examples of escalation triggers include an unclear license match, possible third-party copyrighted text or code, tool terms that appear to conflict with project terms, an incompatible-license concern, or a security finding outside the reviewer’s authority.
For each exception, document the approving role, scope of the exception, supporting evidence, rationale, mitigation or remediation plan, and expiry or review date. Do not let an exception silently become a permanent alternative policy. If a concern cannot be resolved, hold the change from the affected integration or distribution path until the responsible reviewer determines the next step.
Review the policy when AI tool terms change, a target project changes its contribution rules, product distribution changes, or the organization’s open-source compliance program changes. OpenChain calls for a process to review and update policy and supporting tasks and to resolve non-compliant cases; maintain a route for reporting and closing identified issues.
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Choose an implementation proportionate to your scope
The right workflow depends on the organization’s size, product risk, distribution model, and existing compliance program. The official materials describe complementary process, data-exchange, and scanning layers; they do not establish a current best vendor or comparative benchmark. The options below are implementation patterns, not vendor rankings.
| Implementation pattern | Useful fit | Controls to include | Trade-off to manage |
|---|---|---|---|
| Designated reviewer | A narrow scope with an identifiable engineering and compliance owner. | Contributor disclosure, ordinary peer review, license and provenance checks, named escalation contact, retained change record. | Lower process overhead, but dependent on the reviewer’s availability and expertise. |
| Compliance-supported workflow | Multiple repositories or products with recurring dependencies and distribution. | Central component inventory, scanning, CI/CD SBOM where practical, documented notice handling, compliance review for uncertain findings. | More consistent records, but requires workflow integration and ongoing ownership. |
| Board approval for defined high-impact cases | Integrations or exceptions with organization-defined high impact or elevated risk. | Documented thresholds, technical and security review, compliance and legal escalation as needed, approval rationale and conditions. | Stronger centralized oversight, but can slow routine work if the thresholds are too broad. |
Compare process options by whether they cover both source and dependencies, expose provenance and uncertain matches, fit developer and CI/CD workflows, produce and archive SBOMs and notices, support escalation to qualified reviewers, and can be staffed sustainably. The Linux Foundation positions process standards, SBOM formats, and scanning as complementary parts of organizational compliance—not competing substitutes.
Adaptable policy language and rollout checklist
The following is a starting point, not a legal form. Adapt it to the organization’s scope, repository rules, distribution obligations, and counsel’s advice.
AI-assisted contributions are permitted within the scope of this policy when they satisfy the same technical, security, open-source licensing, and contribution requirements as other contributions. The submitting human contributor must review and understand the entire change, identify AI-generated or materially assisted portions as required by the repository workflow, and check applicable tool terms. Contributors must flag suspected third-party material and provide available provenance and licensing information. Changes with unresolved rights, license, security, or project-policy questions must be escalated to the designated reviewer and must not proceed through the affected approval or release path until resolved. Required notices and compliance records must be retained with the relevant component or release record.
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Before announcing the policy, make sure the workflow is executable:
- Set scope. List covered products, repositories, teams, tools, internal uses, distributions, and upstream contribution paths; state exclusions.
- Publish rules where work happens. Add the policy to engineering documentation and link it in contribution instructions, pull-request templates, and compliance procedures.
- Name owners and backups. Assign technical, security, open-source compliance, and legal escalation responsibilities, including coverage when the primary reviewer is unavailable.
- Design the record. Add fields for applicable disclosure, contributor confirmation, scan findings, approvals, exceptions, notices, and SBOM or component references.
- Train contributors and reviewers. Explain how to disclose assistance, recognize uncertain provenance or license findings, preserve required notices, and request help.
- Exercise the process. Walk a representative change through ordinary review and a separate scenario with an uncertain license or possible third-party match; fix unclear ownership or workflow gaps.
- Maintain the policy. Review it after relevant changes to tools, project rules, products, distribution, or compliance operations, and close identified non-compliance issues.
A policy succeeds when an engineer can tell what to disclose, a reviewer can see the evidence and make the decision within their authority, and unresolved issues have a clear destination. Treat AI assistance as one provenance factor in a dependable open-source approval program—not as a shortcut around it.
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