Model provenance
The documented, verifiable chain of custody for a dataset or model, including who created it, how, from what sources, and how its identity can be independently confirmed. Established through mechanisms like datasheets for datasets and platform organization-verification signals; the primary structural defense against poisoning attacks that rely on impersonating a trusted source. (see Topic 4.8)
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Data Governance Professional (CADGP).
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 documented, verifiable chain of custody for a dataset or model, including who created it, how, from what sources, and how its identity can be independently confirmed. Established through mechanisms like datasheets for datasets and platform organization-verification signals; the primary structural defense against poisoning attacks that rely on impersonating a trusted source. (see Topic 4.8)
The origin and ownership of the model a product depends on, including whether the core model is the vendor's own or a third party's, which upstream providers and sub-processors are in the chain, and where the training data came from. Undisclosed provenance means depending on a supply chain you cannot see.
The ability to verify that a downloaded or deployed model file is authentically what it claims to be, trained by the organization it claims, with no undisclosed modification along the way.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Data poisoning: how an attacker teaches your model on purpose · Poison, Leaks, and the Adversary, Certified AI Data Governance Professional (CADGP)
- The vendor interrogation: questions that expose what the sales deck hides · Shipping AI and Surviving the Incident, Certified AI Governance Professional (CAIGP)
- Security and Red-Teaming: Adversarial Attacks, Prompt Injection, Supply Chain · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Model 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.
Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.
One lesson on this page teach it as a primary outcome.
Tiers vendors by use and impact, requests evidence instead of promises, tests in the customer's context, and plans monitoring and exit.
One lesson 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.
- Chief Information Security Officer, AI security focusneeds it at depth
- AI Security Architectneeds it at depth · 8 open postings this week
- Cybersecurity Risk Analystneeds working knowledge
- Chief Information Officer, technology leadership editionneeds it at depth
- AI Vendor Risk Managerneeds it at depth
- Third-Party Cyber Risk Managerneeds it at depth
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: OWASP Top 10 for LLM Applications; Google SAIF; NIST AI RMF MEASURE 2; NIST AI RMF GOVERN 6 and MANAGE 3.
One interview question about it
What are the security failure modes specific to AI systems, and which conventional control covers none of them?
Asked of candidates for ai security fundamentals. 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.
- Cybersecurity and IT carries it as a claim to back up: Threat modeling and control design transfer; the AI-specific threats are new.
- A computer science or software engineering degree carries it as a claim to back up: Security fundamentals are a course you took, not yet a control you ran.
- A cybersecurity or information assurance degree carries it as work you can show: Security fundamentals are your coursework and your labs.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.