Chief Risk Officer interview questions
What does a Chief Risk Officer interview ask?
One question per competency the role leans on, 9 in all, the core ones first. Interviewers are not testing whether you know the frameworks; they are testing whether you have run the practice. Answer each with a case, a decision and the evidence: what the situation was, what you decided and why, and what the evidence showed afterwards.
- 1. AI risk and impact assessment, core to the role
Take me through an AI risk and impact assessment you would run for a hiring tool. What do you assess, and who signs?
A strong answer shows: Reviews purpose, data, affected people, accuracy, bias, security, oversight, vendors and law for a use case, scores likelihood and impact, and documents residual risk.
- 2. AI risk register and treatment tracking, core to the role
How do you keep an AI risk register from becoming a list nobody reads? What makes a risk entry actionable?
A strong answer shows: Keeps the living record: each risk with a named owner, rating, treatment, residual risk, monitoring metric, threshold and review date.
- 3. Executive and board communication on AI risk, core to the role
Brief a board on an AI risk in two minutes. What do you say, and what do you leave out?
A strong answer shows: Turns technical uncertainty into a one-page decision: material risks, trends, exceptions, remediation, and what the board is being asked to accept.
- 4. Model risk management and independent challenge, required
Explain independent challenge of a model to someone who built it. What do you challenge, and what do you leave to the developers?
A strong answer shows: Classifies models by tier, sets validation requirements, challenges data, methodology and performance evidence, and reports aggregate exposure.
- 5. AI vendor due diligence and third-party risk, required
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?
A strong answer shows: Tiers vendors by use and impact, requests evidence instead of promises, tests in the customer's context, and plans monitoring and exit.
- 6. AI incident response and recovery, required
An AI system has just caused harm to a customer. Walk me through the first 48 hours.
A strong answer shows: Classifies AI incidents by severity, runs containment, preserves evidence, manages notification, and closes the loop with lessons learned.
- 7. AI resilience, continuity and exit planning, required
Your AI vendor shuts down next month. What was in your continuity and exit plan, and what did you fail to plan for?
A strong answer shows: Plans for a vendor failure, an unsafe model change or a suspended service: rollback, manual fallback, data export and safe decommissioning.
- 8. AI governance operating model design, required
Sketch the governance operating model you would set up for a company deploying its first customer-facing AI. Who decides, who reviews, and who can stop it?
A strong answer shows: Designs decision rights, committees, intake, approval tiers and escalation so routine uses move and consequential uses get reviewed.
- 9. US federal and state AI regulation, preferred
A US company operates in several states. How do you track which state AI laws apply to which systems, and what changes when a new one passes?
A strong answer shows: Tracks executive orders, OMB guidance, agency rules and the state patchwork, and knows which state laws reach hiring, insurance and consumer decisions.
Where the answers come from
Each question is graded on GAGE before any interviewer asks it: every topic is passed by explaining it back, and a passed explanation can be defended out loud. That record is the case you bring into the room. Check which of these 9 you can already answer from proof.