JPMorgan Chase 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.

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.

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.

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.

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 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.

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.