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To track AI-generated code in Git, capture authorship evidence as a change is prepared, tie it to the exact repository revision, and preserve it with the source history. Git AI’s Authorship Log format records AI-attributed lines and related conversation threads using Git Notes. Pair that source-level record with normal code review and, when you need to trace a release, separate build provenance. A build attestation alone does not show which lines were generated by AI.
How do I track AI-generated code in Git?
Start by deciding what your team needs to establish. “AI was involved” can mean that a tool suggested some lines, an agent prepared a change, or an AI-assisted change became part of a particular revision. Those are different claims, and no single record necessarily proves all of them.
Choose the evidence you need
- Line-level attribution: which committed lines were attributed to an AI tool, ideally with the related conversation context.
- Commit or revision history: which actors and source-control events are associated with a change, and which exact revision is under review.
- Human review: who reviewed or approved the change and what checks were applied.
- Release traceability: how a build produced an artifact and which source or dependencies it used.
These records complement one another; a review record is not a line-by-line authorship log, and a build record is not an AI activity log.
Capture the record when the change is made
Have the editor, coding agent, or repository workflow produce structured authorship data while the change is being prepared or committed. The Git AI Standard v3.0.0 describes authorship logs as records of AI-authored lines in a commit and the conversation threads that generated them. That makes the log useful for investigating a particular change rather than relying on someone’s memory later.
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Bind each record to the repository and exact commit or revision it describes. If the log uses line ranges, interpret them against the file version in that commit: later edits can move lines, so a range without its revision context can become misleading.
Keep the metadata with the source history
Git AI attaches its authorship logs using Git Notes, which carry metadata separately from the commit itself rather than rewriting commit history. That separation means the note has to be handled as part of your repository operations. Decide how collaborators fetch and push the relevant notes, how notes are included in mirrors and backups, and how reviewers can inspect them. The specification describes the attachment format; it does not establish a universal distribution setup that every host or clone handles by default.
Test that workflow across the clones and hosting systems your team actually uses before relying on notes for an audit. Document the log format, the meaning of its fields, and who or what is responsible for writing it.
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Keep review and release controls separate
Authorship metadata says something about contribution, not whether the code is correct or safe. Retain ordinary human review, tests, branch protections, and security checks. If you also need to connect a released artifact to its build inputs, create and verify build provenance as a separate record.
How can I tell which lines were written by AI?
A line-level authorship log is the most direct evidence in the sources covered here. Git AI’s Authorship Log is designed to associate AI-attributed lines with a commit and related conversation threads. Its usefulness depends on the log having been created and retained; it cannot reliably reconstruct missing records after the fact.
Without contemporaneous attribution, commit history may show that a change was made and by whom, but it does not necessarily identify which lines originated with AI. AI-code detectors or a developer’s later recollection are not substitutes for a structured record tied to the exact revision.
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The SLSA Source Requirements v1.2 emphasize reliable source history, immutable revision identity, attribution, and source provenance created alongside revision events. SLSA provides source-control provenance principles rather than a specific AI-line format, and it does not require Git specifically.
How do the main provenance options compare?
| Approach | Evidence captured | Useful for | Limits to plan for |
|---|---|---|---|
| Git AI Authorship Log with Git Notes | AI-attributed lines tied to a commit, with conversation-thread context | Auditing which lines in a committed change were attributed to AI | Tools must emit the log, and teams must preserve and distribute the notes. Line references apply to the exact committed file version. Confirm compatibility with the agents in use. |
| Assistant-provided code referencing | Public-code matches and license information for qualifying accepted suggestions | Investigating a possible match to public code | Product-specific and partial; it is not a complete AI activity or authorship log. |
| Source-control provenance attestations | Source revision history, actors, and information about the source-control process and controls | Organization-level revision integrity and auditability | Depends on the source-control implementation, identity configuration, available attestations, and documented controls. |
| Build provenance or artifact attestations | How a build produced an output and the inputs or dependencies it resolved | Connecting a released artifact to its build and source context | Answers a build question, not necessarily who or what authored source lines. Verification also depends on trust in the builder. |
When comparing approaches, check their granularity (line, commit, revision, or artifact), integrity, capture timing, identity and tool coverage, portability, metadata retention, verification burden, and whether they record human review as well as AI involvement.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsCan GitHub Copilot show where generated code came from?
GitHub Copilot code referencing can expose information about qualifying accepted inline suggestions that match code in public GitHub repositories. GitHub’s documentation says the match information is logged when a user accepts a matching inline suggestion. The feature is useful for investigating a potential public-code match, but it does not record every instance of AI assistance or identify all AI-authored lines.
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GitHub documents that Copilot suggestions typically match public code in less than one percent of suggestions. That figure describes the frequency of public-code matches, not the proportion of AI-generated code tracked, accepted, or legally problematic. The documentation also excludes altered suggestions and code written by the user from this code-referencing behavior.
For agent-generated changes, preserve both actor and review evidence. In its documented Copilot cloud-agent flow, GitHub says commits are authored by Copilot, co-authored by the requesting developer, signed, and reviewed by a human before merge. Those details describe that documented flow, not an assurance about every configuration or agent. Check your actual settings and retain the pull request and session evidence your process requires.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does build provenance show whether code was AI-generated?
No. SLSA Build Provenance describes how a build platform produced an artifact, including build inputs and resolved dependencies. It can help connect an output to its source and build context, but that alone does not establish whether AI generated particular source lines.
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Use source-level authorship records to support claims about AI involvement in code, and build provenance to support claims about how a released artifact was produced. GitHub’s artifact-attestation documentation describes a verification flow for attestations and SPDX or CycloneDX SBOM predicates; those records serve artifact and dependency traceability, not line-level AI attribution.
How do I keep AI attribution attached to a commit?
- Define the claim. Decide whether the record must show AI-attributed lines, AI participation at commit level, reviewer identity, source revision integrity, or the relationship between a build and artifact.
- Record it during the change. Configure the editor, agent, or repository workflow to write structured attribution as work is prepared or committed, rather than trying to infer it later.
- Bind it to an immutable identity. Include the repository locator and exact commit or revision identifier. Resolve line references against the file version in that revision.
- Preserve it for the people who need it. If using Git Notes, test note-ref fetch, push, backup, mirroring, and review practices across your actual systems. Make the format and its meaning understandable to collaborators and auditors.
- Retain human controls. Keep review, tests, branch protections, and security checks. Provenance supports claims about origin and process; it does not certify quality or safety.
- Attest the release separately when needed. Add build provenance to link an artifact to its build context and inputs, then verify it under your organization’s trust assumptions.
What provenance does not establish
- There is no established universal coverage claim. The cited sources do not establish a cross-vendor standard adopted across coding assistants and repository hosts. Git AI defines an authorship-log format; SLSA gives broader source-control principles while leaving implementation to source-control systems.
- A public-code match is not authorship coverage. Copilot code referencing can surface qualifying public-code matches, but it does not tell a team every time an AI suggestion was accepted.
- An attestation is not a code-quality verdict. Identity and provenance records do not replace testing, review, or security analysis.
- A missing record cannot be repaired with certainty by inference. The strongest source-level evidence is captured alongside the change and tied to its exact revision.
These distinctions follow the Git AI Standard v3.0.0, SLSA Source Requirements v1.2, SLSA Build Provenance, and GitHub’s documentation on Copilot code referencing, its cloud agent, and artifact attestations, accessed October 4, 2026.
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