Indemnification clause
A contractual provision in which one party (typically a data vendor) agrees to cover the costs, including legal liability, that the other party (the buyer) incurs if a specific risk, such as a copyright infringement claim tied to the vendor's dataset, materializes. A minimum contractual floor when a vendor's underlying data rights cannot be independently verified.
Defined in 2 GAGE programs, which carry 2 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 provision in which one party (typically a data vendor) agrees to cover the costs, including legal liability, that the other party (the buyer) incurs if a specific risk, such as a copyright infringement claim tied to the vendor's dataset, materializes. A minimum contractual floor when a vendor's underlying data rights cannot be independently verified.
A contractual provision allocating responsibility for certain liabilities between parties, such as a vendor agreeing to cover a deployer's losses arising from the vendor's AI product, subject to negotiated limits.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Copyright and the corpus: what your organization may train on, and the provenance file that proves it · Lineage Under Audit, Certified AI Data Governance Professional (CADGP)
- The Liability Risk Assessment and Mitigation Strategy · Legal Frameworks and Liability Navigation, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Indemnification clause 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.
Reviews purpose, data, affected people, accuracy, bias, security, oversight, vendors and law for a use case, scores likelihood and impact, and documents residual risk.
One lesson on this page teach it as a primary outcome.
Keeps the living record: each risk with a named owner, rating, treatment, residual risk, monitoring metric, threshold and review date.
One lesson on this page teach it as a primary outcome.
Runs GOVERN, MAP, MEASURE and MANAGE as a cycle with evidence, builds current and target profiles, and applies the generative AI profile.
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 Risk Managerneeds it at depth · 2 open postings this week
- Chief Risk Officer, AI risk editionneeds it at depth
- AI Governance Managerneeds it at depth
- Cybersecurity Risk Analystneeds working knowledge
- Third-Party AI Risk Analystneeds working knowledge
- Ethical AI Specialistneeds working knowledge · 5 open postings this week
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 MAP and MEASURE; ISO/IEC 42005; EU AI Act Article 27; NIST AI RMF MANAGE.
One interview question about it
Take me through an AI risk and impact assessment you would run for a hiring tool. What do you assess, and who signs?
Asked of candidates for ai risk and impact assessment. 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 work you can show: A DPIA is an impact assessment; the AI version adds accuracy, bias and oversight.
- Internal audit and IT audit carries it as a claim to back up: Remediation tracking is familiar from follow-up work.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.