Hiring nowResponsible AI governance and risk

AI Governance Lead

Also called AI Governance Officer · Head of AI Governance · EU AI Act 'AI Officer'

An AI Governance Lead builds the frameworks, inventories and policies that make AI risk visible and give each system a named owner. It matters because regulators, customers and boards now expect clear accountability.

What they do

They build and maintain an inventory of every AI system in use or in development, then classify each by risk. They write policies and set approval gates for the AI lifecycle. They run impact and fairness assessments with product and data science teams. They agree owners for each system and chase actions to closure. They track new regulation and translate it into plain requirements. They prepare evidence for audits, customers and regulators. They also report risk to senior leaders and the board, and say no when a launch is not ready.

What a strong hire delivers

Within the first months, a strong hire delivers a complete first inventory of AI systems with risk tiers and named owners. They put a working approval process in place that teams actually use. They publish a short, clear policy set. They run the first impact assessments on live systems and give leadership an honest view of the biggest risks and the gaps to close.

How you can tell they are good

Ask them to walk through a framework they built and what changed because of it. Ask how they classified a borderline system and who signed it off. Strong candidates speak plainly about models, data and failure modes, and about regulation. Be wary of people who recite principles but cannot show an artefact, a decision or a trade-off.

Why it is hard to fill

The title is not settled. The same work appears as officer, lead, head or a named regulatory role, so search by title misses people. The skills sit across risk, compliance, audit and machine learning, and few people have all three. Demand is high and many candidates are strong on policy but thin on technical detail, or the reverse. Good ones are usually already in post.

Where to find them

Look in risk management, compliance, internal audit and technology governance teams, especially in financial services, insurance, healthcare and other regulated sectors. Others come from privacy and data protection, or from responsible AI teams inside large enterprises and AI vendors. Titles include model risk manager, AI policy lead, privacy lead, assurance manager and responsible AI programme manager.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Governance Lead

Describe an AI risk framework or inventory you built. How did you get teams to follow it?
Look for a specific artefact, a clear scope and how they won adoption through light process and senior backing. Worry if they describe only a policy document, or if teams worked around it and they never noticed.
How would you decide whether a system counts as high risk when the regulation is unclear?
A strong answer covers use case, affected people, autonomy, data and impact, plus documenting the reasoning and escalating. Worry about rigid box ticking, or avoiding a decision until the rules are certain.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring an AI Governance Lead to set up how we manage AI risk. You will build the frameworks, inventory and policies that give every AI system a clear owner, and work with product, engineering, legal and leadership to keep our AI use safe, fair and defensible.

What you will do

Build and maintain an inventory of AI systems with risk classifications and named owners
Design and run AI risk frameworks, approval gates and review processes
Write clear, usable AI policies and guidance for teams
Lead fairness and impact assessments with product and data science
Track AI regulation and turn it into practical requirements
Prepare evidence for audits, customers and regulators
Report AI risk and open actions to senior leadership

What you will bring

Demonstrated work building or running a risk, governance or assurance framework
Working knowledge of how machine learning systems are built, tested and fail
Experience applying regulation to real products and decisions
Ability to explain technical risk clearly to non-technical leaders
Track record of getting teams to adopt a process without heavy friction
Experience running assessments and following actions to closure

Nice to have

Experience in a regulated sector
Background in privacy, audit or model risk
Familiarity with AI management and assurance standards

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

AI risk managementWorkingIdentify, assess, treat and monitor AI risks
AI governance and assuranceWorkingAssign accountability and verify lifecycle controls
Fairness and impact assessmentWorkingAssess unequal errors, access and downstream impacts
Technical communicationWorkingExplain evidence and limitations to relevant readers

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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