AI Regulatory Counsel interview questions
What does a AI Regulatory Counsel interview ask?
One question per competency the role leans on, 10 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. EU AI Act obligations and timelines, core to the role
Classify a specific AI system under the EU AI Act and name the obligations that follow, including what applies now and what is deferred.
A strong answer shows: Classifies a system by role and risk tier, knows which obligations bind on which date after the Digital Omnibus, and what evidence conformity needs.
- 2. US federal and state AI regulation, core to the role
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.
- 3. Privacy law applied to AI, core to the role
Apply a privacy law you know to a model trained on customer records. Where is the legal basis, and where is the risk?
A strong answer shows: Applies GDPR, CCPA and sector rules to training data, inference, automated decisions, lawful basis, individual rights and cross-border transfer.
- 4. Contract terms that allocate AI risk, core to the role
Which contract terms would you insist on before a vendor's model touches customer data, and what happens when the vendor changes the model?
A strong answer shows: Turns controls into enforceable obligations: data use, change notice, audit rights, incident duties, subcontractors, IP, exit and deletion.
- 5. AI in hiring and employment decisions, required
An AI tool screens job applicants. What must be true before you allow it, and what do you check every quarter?
A strong answer shows: Knows the rules on automated employment decisions, bias audits and notices, and treats hiring AI as the highest-risk use case it is.
- 6. Regulatory change management, required
A new regulation lands. How do you decide what changes in your program by when, and how do you prove you noticed in time?
A strong answer shows: Spots a regulatory change, decides applicability, assigns actions, updates controls and keeps the implementation evidence.
- 7. AI risk and impact assessment, required
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.
- 8. Explainability, transparency and contestability, required
A customer asks why the model decided against them. What can you explain, what can you not, and how can they contest it?
A strong answer shows: Decides what a person affected by an AI decision must be told, how an output can be explained, and how they can challenge it.
- 9. How models work, at a governance depth, required
Explain how a large language model produces an answer, at the depth a governance decision needs and no deeper.
A strong answer shows: Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.
- 10. AI incident response and recovery, preferred
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.
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 10 you can already answer from proof.