AI Governance, Risk & Compliance Interview Questions

A framework, a certifiable standard and a law. They overlap heavily and substitute for each other not at all.

Tiers are decided by use, not by model. And for high-risk providers, Article 15 makes AI security a legal requirement.

Naming Govern, Map, Measure and Manage takes five seconds. Saying what each one produces in your organisation is the answer.

You cannot govern what you have not listed. The register is the list; the routing is what makes it worth keeping.

A startup cannot afford a governance department. It can afford two weeks that close the risks that would end the company.

Yes, it is certifiable, and it is built to sit next to 27001. The follow-up is what a certificate does not prove.

Two layers: documentation, copyright and transparency for every GPAI model provider, then evaluation, incident reporting and cybersecurity once a model crosses into systemic risk.

Location of the office is not the test. Where the system is placed, used, or where its output is used, is.

Banning AI tools moves usage to personal accounts. The answer that works gives people a sanctioned path that is easier than the shadow one.

A policy that says 'AI systems shall be secure' changes nothing. A release gate that blocks deployment until the red-team regression suite passes changes everything.

The core RMF is generic; the profile makes it specific to generative AI. The scored answer names the twelve risks, shows how they map onto the four functions, and picks the ones a security team owns.

Most model cards describe the model's best day. The scored answer adds what a reviewer needs: intended use, limits, evaluations with conditions, data provenance and security-relevant behaviour.

Three GDPR questions that shape every AI system handling EU personal data. The scored answer gets the legal structure right and turns each into engineering work.

A policy saying humans review decisions proves nothing. The scored answer specifies the logs, metrics and tests that show reviewers can and do change outcomes.

When everyone owns AI risk, nobody does. The scored answer puts one accountable owner on each decision and makes the specialist teams responsible for the work they are best at.

AI vendors add questions a standard security questionnaire never asked. The scored answer covers training use, retention, subprocessors, model changes and incident terms, and verifies rather than trusts.

The obligations most consumer AI products meet first. The scored answer separates provider and deployer duties, gives the dates, and turns each into a product requirement.

Regulatory reporting has to plug into ordinary incident response. The scored answer knows who reports what to whom and by when, and is honest about the open question on timing.

Six months is possible for a well-run organisation with a narrow scope. The scored answer sets the scope first, reuses an existing management system, and schedules the audits backwards from the date.

Seven characteristics that frame most AI governance conversations. The scored answer names them precisely and shows how security supports, and trades off against, the others.

Fairness has several incompatible definitions, so the audit starts with a decision, not a metric. The scored answer picks measures for the context, gets the data to compute them, and audits the whole decision process.

The same model in a different context carries different obligations and harms. The scored answer names what each sector adds on top of baseline AI security.

Regulation does not tell you how to red-team, but it decides what you must be able to show. The scored answer maps programme outputs to the evidence each framework expects.

Slides on prompt injection change little. The scored answer teaches through the developers' own code, gives them paved roads, and measures behaviour change.

The one governance answer that goes stale. The scored answer gives the timeline as amended in 2026, the fine tiers, and a process for tracking changes rather than memorised certainty.

Three frameworks, three different questions: what can go wrong in what we build, how attackers actually operate, and how the organisation manages the risk. Using one for another's job is the common mistake.

'Prompt injection' is a technique, not a risk entry. Entries that get funded describe a scenario, an asset, a consequence in business terms, an owner and a measurable treatment.

A central committee reviewing every AI feature either blocks the company or gets bypassed. The model that works centralises standards and platforms, tiers by risk, and gives teams paved roads they want to use.

There is no single 'AI incident' notification rule. The incident is usually a personal-data, contractual or sector incident that happens to involve AI, and the clock may already be running.

A composite safety score asks the reader to trust you. An assurance case states specific claims, shows the argument and evidence for each, and lists what would make it false.

Security asks who can steal the data. Privacy also asks what the agent itself collects, infers, remembers and passes on, about the user and about everyone in their inbox who never agreed to anything.

Frontier labs promise to add safeguards when models cross capability thresholds. A large share of those safeguards are security controls, and someone has to build and prove them.

Mythos-class models and Gemini's Flash Cyber variants did not ship to everyone. Knowing why, and what joining a vetted-access programme commits you to, is now a governance interview staple.

Signed provenance and invisible watermarks are often lumped together as 'AI detection'. They prove different things and fail in different ways, which is the whole answer.

There is no single US AI law, and the state laws keep changing. The scored answer knows which ones are in force, which were replaced, and which security obligations they actually create.

Frameworks for AI risk are plentiful; baselines that say what security controls an AI system needs are newer. The scored answer knows what each one is, how binding it is, and how to turn it into a control set.

Reconcile a compliance dashboard with actual organizational scope. Preserve the original report, expose unassessed systems and assign evidence collection without inventing a new incident.

Extend model discovery into a graph of data, identities and tools. Identify the missing evidence needed to assess an agent’s authority and separate posture from behavioral evaluation.