AI governance & assurance lead
Turn risk decisions into checks people can carry out.
The outcome this role owns
AI systems have identifiable owners, documented decisions and controls that are tested in the actual workflow.
Four levels of maturity
Proposed framework · Evidence-based discussion · Not a validated assessment scale
Foundation
Describes an AI system, its users, data and plausible failure consequences.
Evidence to look for
A system record and risk map
Pilot
Creates a pilot risk assessment with owners, required evidence and a route for unresolved concerns.
Evidence to look for
A review showing evidence required before launch
Production
Runs recurring reviews, checks controls in practice and tracks incidents and corrective actions.
Evidence to look for
A tested control and tracked corrective action
Organisational
Sets proportionate assurance across a portfolio and changes controls when evidence or requirements change.
Evidence to look for
A portfolio review with escalation and closure decisions
A practical work sample
Present an AI assistant that summarises internal documents. Ask for an inventory record, the three most consequential failure scenarios and evidence that proposed controls work.
Use synthetic or public data, explain the evaluation criteria in advance and allow reasonable adjustments. Discuss the reasoning as well as the finished artifact.
What to question
A policy document is treated as proof that the live system follows the policy.
Related market signal: ai adoption
Broader AI use can create assurance work, but adoption rates do not measure governance vacancies or the availability of qualified specialists.
Highest observed shares among covered markets, 2025. These are context, not recommended hiring locations.
Eurostat source and definitions ↗
Salary context
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Explore related salary evidence ↗Framework sources
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