Indemnification
A contractual commitment by one party to cover the other's losses arising from a specified category of harm; effective against provenance risk only when its scope specifically names provenance misrepresentation or undisclosed AI use, not merely general intellectual property claims.
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.
A contractual commitment by one party to cover the other's losses arising from a specified category of harm; effective against provenance risk only when its scope specifically names provenance misrepresentation or undisclosed AI use, not merely general intellectual property claims.
A contractual promise by a vendor to cover certain legal claims (for example, training-data copyright claims) arising from use of its product. Real indemnification has caps, carve-outs, and conditions; open-source adoption typically comes with none.
A contract term under which one party agrees to cover another's losses from specified claims, for example a vendor covering a deployer's losses from a third-party training-data claim. Allocates who pays; does not make the underlying risk disappear.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The vendor's black box: forcing provenance answers from a supplier who has none · Poison, Leaks, and the Adversary, Certified AI Data Governance Professional (CADGP)
- GPAI upstream: what your foundation-model vendor owes you and what you must verify yourself · The EU AI Act: The Executive Map, Certified AI Governance Professional (CAIGP)
- Ecosystem and Open-Source Strategy · Technology, Platforms and Vendors, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where Indemnification 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.
Tiers vendors by use and impact, requests evidence instead of promises, tests in the customer's context, and plans monitoring and exit.
2 lessons on this page teach it as a primary outcome.
Writes requirements, runs a fair evaluation, pilots within limits, and refuses a demo as evidence.
2 lessons on this page teach it as a primary outcome.
Turns controls into enforceable obligations: data use, change notice, audit rights, incident duties, subcontractors, IP, exit and deletion.
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.
- AI Vendor Risk Managerneeds it at depth
- Third-Party AI Risk Analystneeds it at depth
- Third-Party Cyber Risk Managerneeds it at depth
- Chief Information Officer, technology leadership editionneeds it at depth
- Chief Audit Executive, AI audit editionneeds working knowledge
- Chief Compliance Officer, AI compliance editionneeds 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: NIST AI RMF GOVERN 6 and MANAGE 3; ISO/IEC 42001 Annex A supplier controls; CISA software acquisition guidance; GDPR Article 28.
One interview question about it
A business unit wants to buy an AI tool next week. What do you ask the vendor, what evidence do you require, and what would make you say no?
Asked of candidates for ai vendor due diligence and third-party risk. 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.
- Privacy and data protection carries it as a claim to back up: Processor and vendor review is routine privacy work.
- Cybersecurity and IT carries it as a claim to back up: Supplier security assessment is routine.
- Nonprofit program and grants oversight carries it as a claim to back up: Reviewing grant and vendor agreements for data terms is vendor review.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.