Provenance
The account of an artifact's origin and life: who made it, when, what version this is, what changed between versions, and a way to show it has not been altered since. Provenance is what turns a document into evidence, because it lets you answer "who made this, and how do you know it is genuine" without saying "trust us."
Defined in 4 GAGE programs, which carry 10 distinct definitions of it. The wording above is taught in AI Governance: Applied Mastery.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
The account of an artifact's origin and life: who made it, when, what version this is, what changed between versions, and a way to show it has not been altered since. Provenance is what turns a document into evidence, because it lets you answer "who made this, and how do you know it is genuine" without saying "trust us."
The traceable origin and history of a piece of data or an AI system's training material: where it came from, who touched it, what license or consent governed its collection, and whether that chain can be shown credibly to someone who does not trust the organization producing it.
The ability to state, for a given agent, who built it, when, for what stated purpose, and under whose authority. Loss of provenance is the first of the primary's three named consequences of sprawl.
The origin and history of a source: whether it is genuine, whether it is the original rather than a re-report, and (for media) whether it is authentic rather than synthetic or doctored.
The documented history of a dataset, its origin, chain of custody, and changes over time. Good provenance makes data auditable and correctable; absent provenance is a risk signal.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- Source-to-Output Verification Protocols · Critical Thinking and Context Engineering, AI Literacy & Professional Conduct
- Data Literacy for AI: Datasets, Quality, and Pipeline Basics · Advanced AI Literacy, AI Literacy & Professional Conduct
- Data Poisoning and Model Security · AI Security Fundamentals, AI Literacy & Professional Conduct
- Agent Sprawl · Multi-Agent Governance, Agentic AI Governance: Applied Mastery
- Why the data person just became the most consequential role in the building · Taking the Data Chair, AI Data Governance: The Data Chair
- Training data governance: what may teach a model, decided before the model exists · Feeding the Machines, AI Data Governance: The Data Chair
- Datasheets and the AI bill of materials: the paperwork that travels with the data · Feeding the Machines, AI Data Governance: The Data Chair
- The provenance file: your data map an auditor could follow · Data Reality, AI Governance: Applied Mastery
- NIST AI RMF as an operating system: mapping your organization onto govern, map, measure, manage · The World's Rulebooks, AI Governance: Applied Mastery
- The evidence annex: wiring every artifact so the Module 13 audit finds a paper trail, not a scramble · Evidence Engineering, AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.