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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesTo track AI-generated code in Git, record AI involvement when a change is prepared, bind that record to the exact repository and commit, and make sure collaborators can retrieve it. Git AI’s Authorship Log format is one Git-native option: it records AI-attributed lines and related conversation threads, attaching the log with Git Notes without rewriting the commit. Keep this source-level record distinct from build provenance, which describes how an artifact was produced.
How do I track AI-generated code in Git?
Start by deciding what your team needs to establish. “AI was involved” can mean that an assistant proposed a small edit, an agent created a file, or a person reviewed and substantially changed generated code. A useful record defines which kinds of assistance count and what evidence will be retained; a commit message alone may not capture line-level attribution or the conversation behind a change.
- Define the claim. Decide whether you need line-level AI contribution, commit-level participation, the human reviewer, source revision integrity, or a link from a release artifact back to its build inputs. These are different records, not interchangeable labels.
- Capture evidence as the change is made. Configure the editor, agent, or repository workflow to produce structured authorship data while preparing or committing the change. Contemporaneous records are more dependable than asking developers to reconstruct prompts later or applying an AI-code detector after the fact.
- Bind the record to the source revision. Retain the repository locator and exact commit or revision identifier. If the record identifies lines, interpret those ranges against the file at that revision: later edits can move or replace the lines.
- Choose a durable storage and sharing method. Git AI’s Authorship Log format can be attached to commits using Git Notes. Notes are separate metadata refs, so decide how your team will fetch, push, mirror, back up, and review them. Test that process across the clones and hosting systems your team actually uses.
- Retain review evidence and ordinary controls. Keep reviewer identity and the relevant pull request or change record alongside your normal tests, code review, branch protections, and security checks. Attribution records describe origin or process; they do not establish that code is correct or safe.
- Record releases separately when needed. For artifact traceability, retain build provenance that identifies the build context, inputs, and outputs. Verify the attestation and the builder’s trust assumptions rather than treating the existence of an attestation as proof of AI authorship.
Document the meaning of each field and the limits of the record. SLSA Source Requirements v1.2 emphasizes reliable history, attribution, and source evidence created alongside revision events; it does not prescribe a universal Git-specific implementation.
How can I tell which lines were written by AI?
A line-level authorship log is the most direct kind of evidence for that question, provided it was recorded and retained for the relevant commit. Git AI Standard v3.0.0 describes Authorship Logs as recording which lines in a commit were authored by AI agents, together with the conversation threads that generated them. That is an attribution record, not an independent forensic determination that a line must have been generated by AI.
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Line references are meaningful only in the exact committed file version they describe. They should not be read against the current branch after later edits, rebases, or file moves unless the record has been correspondingly updated or mapped. A commit-level note saying an assistant was used may be useful when line-level attribution is unavailable, but it makes a broader and less precise claim about the change.
No universal cross-vendor provenance format adopted across coding assistants and repository hosts is established by the standards and product documentation cited here. Git AI defines a specific format; SLSA’s source-provenance requirements set broader principles while leaving implementation to source-control systems.
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Which Git provenance approach fits the job?
| Approach | Evidence it records | Best suited to | Important limit |
|---|---|---|---|
| Git AI Authorship Log with Git Notes | AI-attributed lines in a commit and associated conversation-thread context | Inspecting which committed lines were attributed to AI | Tools must emit the data and teams must preserve and distribute the note refs; line ranges are revision-specific. (Git AI Standard v3.0.0) |
| Assistant code-referencing feature | Public-code matches and license details for qualifying suggestions | Investigating whether an eligible suggestion matches public code | It is a product-specific matching signal, not a complete record of AI activity or authorship. (GitHub Copilot code referencing) |
| Source-control provenance | Revision history, actors, source-control process, and relevant controls | Auditing source changes and revision integrity at an organizational level | Its usefulness depends on implementation, identity configuration, available attestations, and documented controls; SLSA does not require Git specifically. (SLSA Source Requirements v1.2) |
| Build provenance or artifact attestation | How a build produced an output and the inputs or dependencies it resolved | Connecting a released artifact with its build and source context | It answers a build question and does not, by itself, identify AI-authored lines. Verification also depends on trust in the builder. (SLSA Build Provenance; GitHub artifact attestations documentation) |
When comparing approaches, assess granularity, integrity, capture timing, identity and tool coverage, portability, metadata retention, verification effort, and whether human review is recorded as well as AI involvement.
Can GitHub Copilot show where generated code came from?
Copilot code referencing can show information about matching code in public GitHub repositories when a user accepts a qualifying inline suggestion. GitHub says that such matches typically occur in less than one percent of suggestions. That figure describes the typical frequency of public-code matches in Copilot suggestions; it is not a rate for AI-generated code tracked, accepted, or covered by provenance records.
The feature is limited: GitHub’s documentation says it does not check suggestions that were altered before acceptance or code written by the user. It can help investigate a qualifying public-code match, but it cannot tell a team every time AI contributed to a change. Do not use absence of a match as evidence that code was written by a person or that the assistant was unused.
Does build provenance show whether code was AI-generated?
No. SLSA Build Provenance concerns how a build platform produced an artifact, including its inputs and resolved dependencies. It can help connect an output to repository references and commit digests, but that build record does not establish which source lines involved an AI assistant.
Source authorship records and build attestations complement one another: the former address contribution to source, while the latter address artifact production. GitHub’s artifact-attestation documentation describes verifying attestations with the GitHub CLI and using SPDX or CycloneDX SBOM predicates in its documented flow. An attestation still needs verification, and its meaning depends on the builder and evidence it records.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I keep AI attribution attached to a commit?
With Git AI’s notes-based approach, the authorship log is attached as Git metadata rather than embedded by rewriting the commit. That separation preserves the commit’s history, but it means a team must operationally handle the relevant note refs. Define how they are fetched, pushed, mirrored, backed up, and surfaced in review, then confirm those steps work in the team’s actual repository setup. The format’s existence does not mean every host or clone will distribute the notes automatically.
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Keep the record interpretable over time: document the format, the scope of “AI-authored,” which tools emit it, how identities are represented, and which revision its line references describe. Pair it with reliable source history and attribution controls; SLSA Source Requirements v1.2 frames reliable history as a way for organizations and consumers to track software changes and attribute them to actors.
Quick Recap
What provenance does not prove
- Authorship is not correctness. A recorded AI contribution, verified identity, or build attestation does not replace tests, human review, or security analysis.
- A match check is not an authorship ledger. Public-code reference features cover only their qualifying cases and cannot provide a complete account of assistant use.
- Metadata that is not retained is weak audit evidence. Notes and other records need clear distribution, backup, and review practices to remain available to collaborators and auditors.
- There is no industry-wide coverage percentage established here. The Copilot public-match statistic is not a measure of how much AI-generated code in Git repositories has reliable provenance.
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