VeritaPath records how a record got from origin to here: every read, write, transformation, and export, linked in order, each event signed by whoever caused it. When the audit asks how, you have the receipts.
Lineage isn't new. Lineage you can *prove* — with signatures, inclusion proofs, and public checkpoints — is what changes when an auditor, a regulator, or a courtroom asks "how did we get here?"
Every touch on a record emits a small, signed event: who, what, when, and a hash of the before/after state. SDK for Node, Python, and Go; HTTP endpoint for everything else.
Each event references the previous one by hash. Rewriting history invalidates every event downstream — and the downstream events have already been countersigned.
Given any record, reconstruct its full path. Export a human-readable timeline or a court-ready evidence bundle with inclusion proofs and checkpoint signatures.
# Emit a lineage event from any pipeline stage. $ veritapath emit \ --record customer:42 \ --action anonymize \ --prev-hash sha256:a1f7… \ --actor etl-worker@us-east-1 event-id ev_01HS2P… signed-at 2026-04-21T09:14:02Z OK linked to chain: customer:42 (hop 4 of open path) # Trace a record's full path, end to end. $ veritapath trace customer:42 --since 2026-01-01 → 2026-01-14 ingest by api-gateway → 2026-01-14 normalize by etl-worker → 2026-02-03 enrich by geo-service → 2026-04-21 anonymize by etl-worker → 2026-04-21 export by analytics ✓ 5 events, 5 signatures, 0 gaps, chain intact # Export a bundle suitable for an audit or a subpoena. $ veritapath bundle customer:42 --out evidence-customer-42.zip ✓ 5 events, inclusion proofs, checkpoint #00042, witness sigs × 3
GDPR/CCPA data-flow evidence, SAR responses, and consent-withdrawal trails that hold up in a regulatory review.
Training-data provenance so every model is traceable back to source records, transforms, and filtering decisions.
Post-mortem lineage: where did this record come from, who touched it, and was anything upstream compromised?
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