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AI Hub / AI Provenance
AI PROVENANCE · LINEAGE · EVIDENCE

Know exactly what produced an AI artifact and what it affects.

AI provenance is the evidence chain behind a model, dataset or application. Zippri represents lineage as connected signed states so teams can trace an artifact backward to its inputs and forward to the benchmarks, releases or deployments that depend on it.

AI provenancemodel lineageAI supply chainML lineageAI evidence graph
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From notes to an evidence graph

A provenance graph can include repository identity, immutable revisions, Git commits, CAS manifests, environment capsules, dependencies, checkpoints, verification and release state. This provides a stronger basis for audit and reproducibility than a manually edited description.

Trace backward

Find the inputs, revisions and environments behind a result.

Trace forward

See releases, deployments and dependent assets affected by a change.

Detect staleness

Do not treat old evidence as current after material identity changes.

Impact analysis when something changes

When an upstream dependency or artifact changes, downstream evidence may become stale. Zippri can use the lineage graph to identify which verification, benchmark, release or deployment decisions need attention.

Signed snapshots and currentness

Provenance is useful only if teams can distinguish the evidence that matched an earlier artifact from the evidence that matches the current artifact. Zippri keeps that current/stale distinction explicit.

Common questions about AI Provenance

What is AI model provenance?

It is the traceable record of the sources, data, code, environments, transformations and decisions associated with an AI artifact.

Is provenance the same as model versioning?

No. Versioning identifies states; provenance explains the relationships and evidence that connect those states.

Why does provenance matter for production AI?

It helps teams reproduce results, investigate changes, verify dependencies and avoid deploying an artifact under evidence that belonged to an earlier state.

Related AI Hub topics

AI VerificationVerify AI artifacts against current evidence, issue signed verification receipts and govern Trust Passports without converting self-claims into approval.AI Model RepositoryVersion AI source, models, checkpoints and datasets with real Git plus encrypted content-addressed storage, semantic artifact indexing and signed repository history.AI DatasetsManage AI datasets, corpus revisions, metadata, storage, provenance and downstream impact alongside the models and training runs that use them.AI BenchmarkingRun reproducible AI benchmarks against immutable revisions with case-level evidence, signed results, worker execution and currentness checks.AI GovernanceApply server-side AI lifecycle policies, human approval gates, audit history, cost controls and fail-closed evidence rules across training, release and deployment.
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