Category guide

AI code provenance tools

AI code provenance tools and platforms record which AI agent wrote which code and preserve it as evidence. They are distinct from AI content detectors (which guess whether code looks AI-generated), code-quality scanners (which check whether code is good), and supply-chain provenance like SLSA and SBOM (which prove how a build artifact was assembled). This guide maps the category so you can tell which job each one actually does.

Last updated June 4, 2026

The landscape by job

Tools that get grouped together actually do different jobs. Sorting them by the question they answer makes the category legible.

AI code provenance & attribution platforms
Record which agent wrote which line, signed and auditable.
  • AgentDiffdetails →
    Git-native AI code provenance platform: line-level, cross-agent, ed25519-signed, with a merge gate.
AI content detectors (a different job)
Estimate whether code looks AI-generated, after the fact.
  • Copyleaks, GPTZero, Originality.ai
    Probabilistic detection of AI-written text. Does not record which agent authored a line, and is unreliable on source code.
Code quality & security analysis
Detect bugs, smells, and vulnerabilities in the code itself.
  • Sonar / SonarQubedetails →
    Static analysis of quality and security. Not an attribution tool.
  • Semgrep, Cycode
    Vulnerability and policy scanning of source.
Supply-chain provenance (build/artifact layer)
Prove how a build artifact was produced and assembled.
  • SLSA, SBOM, Sigstore
    Standards for build provenance, dependency inventory, and artifact attestation. Complementary to source-line authorship, not the same job.
  • JFrog, Wiz
    Platforms expanding into governance for artifacts and AI components — artifact layer, not which agent wrote which line.
AI usage analytics
Measure how engineers use AI tools across a team.
  • Maestrodetails →
    Behavior and session analytics in a hosted dashboard.
Single-vendor telemetry
Report one vendor's own AI usage.
  • GitHub Copilot audit log
    Copilot-only; excludes local session data such as prompts.
  • git blamedetails →
    Attributes the committer, not the AI agent or model.

What to look for

  • Line-level, not just commit-level or file-level attribution.
  • Cross-agent coverage — every AI tool your team uses, in one record.
  • Tamper-evidence — records signed with keys you control.
  • Data sovereignty — evidence stored in your own infrastructure, not a vendor database.
  • Enforcement — a merge gate, not only a report after the fact.
  • Low-friction — produced automatically at commit time and legible to an auditor.

Frequently asked questions

What are AI code provenance tools?+

Tools that record which AI agent wrote which code and preserve it as verifiable evidence. They differ from code-quality scanners and AI usage analytics, which answer 'is this code good?' and 'how is AI used?' rather than 'who wrote this, with proof?'

What should I look for in an AI code provenance tool?+

Line-level and cross-agent attribution, tamper-evident signing with keys you control, data that stays in your own infrastructure, an enforcement gate rather than just a dashboard, and low-friction capture that is legible to auditors.

Is a code quality scanner the same as a provenance tool?+

No. A scanner like Sonar evaluates whether code is good. A provenance tool like AgentDiff records who wrote it. They solve different problems and are often used together.

Related
AI code provenance guide →AgentDiff vs. Sonar →AgentDiff vs. Maestro →Glossary →

See line-level provenance on a real repo.

AgentDiff records which agent wrote which line, signs it, and keeps it in your git history. Open the live dashboard or book a walkthrough.

Book a demo →Open dashboard