A W3C web standard for recording where data and content came from, what processes created or modified it, and who was responsible — in a format machines can read and verify.
A W3C web standard for recording where data and content came from, what processes created or modified it, and who was responsible — in a format machines can read and verify.
W3C PROV-O (PROV Ontology, 2013) provides a vocabulary for representing provenance as a graph of entities (things), activities (processes), and agents (actors), with relationships such as wasDerivedFrom, wasGeneratedBy, wasAttributedTo, and used. It enables provenance assertions to be published as Linked Data, linked across systems, and reasoned over. In AI governance contexts, PROV-O can represent the provenance and lineage of training datasets, model artefacts, and AI-generated outputs — supporting the transparency and auditability requirements of EU AI Act technical documentation and NIST AI RMF guidance.
Data engineering teams building AI data pipelines can use PROV-O to generate machine-readable provenance records automatically — creating interoperable audit trails that satisfy governance requirements without additional documentation overhead.
Like a standardised digital chain-of-custody form that any authorised party can read and verify — PROV-O provides a common format for recording who created, modified, and used each data item.
A W3C web standard for recording where data and content came from, what processes created or modified it, and who was responsible — in a format machines can read and verify.
W3C PROV-O (PROV Ontology, 2013) provides a vocabulary for representing provenance as a graph of entities (things), activities (processes), and agents (actors), with relationships such as wasDerivedFrom, wasGeneratedBy, wasAttributedTo, and used. It enables provenance assertions to be published as Linked Data, linked across systems, and reasoned over. In AI governance contexts, PROV-O can represent the provenance and lineage of training datasets, model artefacts, and AI-generated outputs — supporting the transparency and auditability requirements of EU AI Act technical documentation and NIST AI RMF guidance.
Data engineering teams building AI data pipelines can use PROV-O to generate machine-readable provenance records automatically — creating interoperable audit trails that satisfy governance requirements without additional documentation overhead.