Code Provenance for AI-Written Code: What It Records and Why Regulated Enterprises Need It

Defining code provenance for AI-written code

NIST defines provenance as the chronology of the origin, development, ownership, location and changes to a system or system component and associated data. It adds that provenance allows changes to be reported to specific actors, functions, locales or activities. That definition is general, and it applies directly to source code.

For AI-written code, code provenance is the record that answers a narrow question about a single change: how did this code come to exist? A usable answer names the coding assistant that produced it, the time it was produced, the licence terms that attach to it, the security conditions in force at generation, and the person who accepted it into the repository.

The question matters because AI-written code now enters repositories alongside human-written code. NIST SP 800-218A states that its practices do not distinguish between human-written and AI-generated source code, since all source code is assumed to need evaluation before use. That is a sound position for vulnerability review. It also means the framework text does not tell you which provenance fields an AI-generated change should carry. Teams have to define those fields themselves.

The three attributes: origin, licence and security context

No standard defines these three attributes under these names. They are a practical way to apply the general NIST definition to AI-written code.

Origin records where the change came from. At minimum this is the assistant or tool that generated it, the timestamp, and the human account that committed it. NIST AI 600-1 describes provenance metadata for generated content as including creators, date and time of creation, modifications and sources. Applied to code, modifications matter: a developer may edit a suggestion before committing, and the record should reflect that the output and the committed change may differ.

Licence records the terms associated with the code and links them to the specific change. Licence obligations attach to code regardless of how it was written, so a change without licence information leaves legal and procurement teams unable to assess it later.

Security context records the conditions under which the code was generated: which policy applied, what was allowed, redacted or blocked, and whether checks such as hidden-character detection ran. OWASP flags bidirectional override and zero-width characters as a risk in code, commits and agent output. A record of the checks that ran is evidence in its own right.

How code provenance differs from related records

Several existing records sound similar but answer different questions. The table separates them by what each one records and what it leaves open for AI-written code.

• Code provenance: Per-change record of origin, licence and security context at generation. Answers how a specific change came to exist.
• SBOM: A formal record of the components and supply chain relationships used in building software. A release-level artifact, not a per-change record of AI generation.
• Source control history: Tracks changes with accountability to an individual account. It records who committed, not whether or how AI generated the code.
• Build attestation: Generally describes how an artifact was built. It sits downstream of the code and does not describe how the source was written.
• Model and dataset provenance: Concerns the lineage of an AI model and its training data. A separate discipline, outside the scope of code provenance.

Capture at generation versus reconstruction after the fact

Provenance records can be produced in two ways. They can be captured when the assistant generates the code, or they can be reconstructed later from repository scans and commit history. The following comparison is architectural reasoning rather than a published benchmark.

Reconstruction works from what survives in the repository. Commit history shows the account and the diff. It does not show which assistant produced a block, what policy applied, or what was redacted before the prompt left the developer's environment. Inference from style or heuristics yields probabilities, which is a weak basis for audit evidence. Reconstruction also cannot recover context that was never stored.

Capture at generation records the facts while they are known. If the record is created in the execution path of the coding assistant, the same component that enforces policy can write down what it allowed, redacted or blocked. The evidence then describes enforced behaviour at runtime, not a later reading of the outcome.

Capture brings its own requirements. NIST SSDF asks that provenance data be protected for integrity and that recipients can verify it, so records should be tamper-evident. OWASP also notes that AI context can contain credentials, personal data and proprietary logic, and advises redacting these before recording captured traffic. A provenance system must not become a second place where sensitive data leaks.

Governance, accountability and where Trussed fits

Several frameworks point toward this capability without prescribing it. NIST SSDF asks organisations to collect, safeguard, maintain and share provenance data for all components of each software release, and to make it available to operations and response teams. NIST AI 600-1 includes the action to employ methods to trace the origin and modifications of digital content, and another to review generated code for downstream risk. These are voluntary framework actions, not legal obligations, and none of them names AI-specific code provenance fields.

Accountability is the other anchor. OWASP's guidance is that AI tools do not accept responsibility for code they generate, so every AI-generated change needs a human owner and explicit approval before merge. A provenance record should make that person identifiable. The same applies to AI agents in CI/CD pipelines that act on pull request events with access to secrets and repository write access: their changes also need attribution.

This guide covers code provenance only. Model provenance, dataset provenance and tracing of agentic workflows are separate topics.

Trussed ships code provenance, covering the origin, licence and security context of code. It enforces policy in the execution path of coding assistants, with actions that include allow, redact, block and escalate for human approval, and it produces audit-ready evidence from that enforcement. Deployment is self-managed in the customer cloud, with configurable retention and data residency.