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 5 GAGE programs, which carry 15 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
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 record of where a piece of information came from and what has happened to it since: source system, extraction, who and when. The engineered form of the Sourced property. (see Topic 18.3)
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.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Source-to-Output Verification Protocols · Critical Thinking and Context Engineering, Certified AI Practitioner: Workplace Foundations
- Data Literacy for AI: Datasets, Quality, and Pipeline Basics · Advanced AI Literacy, Certified AI Practitioner: Workplace Foundations
- Data Poisoning and Model Security · AI Security Fundamentals, Certified AI Practitioner: Workplace Foundations
- Agent Sprawl · Multi-Agent Governance, Certified Agentic AI Governance Professional (CAAGP)
- Why the data person just became the most consequential role in the building · Taking the Data Chair, Certified AI Data Governance Professional (CADGP)
- Training data governance: what may teach a model, decided before the model exists · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- Datasheets and the AI bill of materials: the paperwork that travels with the data · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- The provenance file: your data map an auditor could follow · Data Reality, Certified AI Governance Professional (CAIGP)
- NIST AI RMF as an operating system: mapping your organization onto govern, map, measure, manage · The World's Rulebooks, Certified AI Governance Professional (CAIGP)
- The evidence annex: wiring every artifact so the Module 13 audit finds a paper trail, not a scramble · Evidence Engineering, Certified AI Governance Professional (CAIGP)
Where it shows up at work
A definition is where Provenance starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Collects, labels and preserves the evidence that a control operated, a decision was made, and a claim can be defended to an auditor or regulator.
3 lessons on this page teach it as a primary outcome.
Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.
3 lessons on this page teach it as a primary outcome.
Profiles data, sets rules and thresholds, monitors, assigns issues to owners, and knows when data is not fit to train or run a model.
2 lessons on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Auditorneeds it at depth · 9 open postings this week
- Privacy Analystneeds working knowledge
- Third-Party AI Risk Analystneeds working knowledge
- Chief Audit Executive, AI audit editionneeds it at depth
- Chief Data Officer, data leadership editionneeds it at depth
- Senior AI Compliance Analystneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Articles 11 and 12; ISO/IEC 42001 Clause 7.5; EU AI Act Article 10; NIST AI RMF MAP 4.
One interview question about it
What evidence would you have ready before an auditor asks about an AI system, and how do you produce it as a byproduct of the work?
Asked of candidates for evidence collection and audit-ready documentation. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Compliance and regulatory affairs carries it as work you can show: Audit-ready documentation is what you produce.
- Internal audit and IT audit carries it as work you can show: Evidence evaluation is the profession.
- Project and program management carries it as a claim to back up: Decision logs and repositories are familiar.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.