Review Data Provenance

Data provenance is documented evidence of where data or content came from, how it was collected or created, and which material transformations, transfers, or decisions shaped its current form.

Provenance is the history that travels with information. It helps a reviewer trace the source, custody, edits, generation steps, and other changes rather than treating the current file as context-free.

Without lineage, teams cannot reliably evaluate authority, consent, licensing, freshness, integrity, privacy, or whether human and synthetic material have been mixed.

A training dataset records the original repositories, license terms, collection dates, filtering steps, generated additions, and each version used in a model run.

Provenance does not prove that data is accurate, lawful, or appropriate. It provides evidence needed to evaluate those questions.

Provenance tells the story of how the data got here.

* [[capability-provenance|Capability Provenance]] * [[training-data|Training Data]] * [[synthetic-data|Synthetic Data]] * [[evidence-selection|Evidence Selection]] * [[verification|Verification]]

* [W3C, PROV-O: The PROV Ontology](https://www.w3.org/TR/prov-o/) — web standard for representing provenance information about entities, activities, and agents. * [NIST AI 100-1, Artificial Intelligence Risk Management Framework](https://doi.org/10.6028/NIST.AI.100-1) — AI governance guidance emphasizing documentation, traceability, and data provenance.

* Contrast the lineage of information with the broader contributors captured by [[capability-provenance|Capability Provenance]].