AI & Technical question

Early abuse signals suggest determined users are probing a new feature for cyber harm. Design the safeguards system end to end: what detections would you build, what eval set would you create to measure attack success and false positives, what interventions would you trigger, and how would you trade off safety, latency, and legitimate-user utility?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Ability to design an end to end safety detection and response system and to make explicit tradeoffs between safety, latency, and legitimate use.

State the assumed scenario: a specific feature, for example code execution or web browsing, showing early probing for cyber harm.
List detection layers: prompt level classifiers, behavioral pattern detection across a session, and account level signals like velocity or geography.
Define the eval set: curated red team attack attempts plus a legitimate use sample, so you can measure both attack success rate and false positive rate.
Define graduated interventions: soft warnings, output throttling, human review queue, and hard account suspension, tied to confidence thresholds.
Explain the safety, latency, and utility tradeoff: cheap classifiers run inline for speed, expensive review happens async so legitimate users are not blocked.
Describe how you would tune thresholds over time using the eval set and real incident data.

Gives a concrete metric pair, attack success rate down, false positive rate on the legitimate use sample controlled, as the north star of the eval.
Separates fast inline checks from slower async review so latency for the vast majority of legitimate users stays low.
Names escalation tiers instead of a single block or allow decision.
Assumes numbers for illustration, for example targeting under one percent false positive rate on benign traffic.

Chapters of the AI PM course that teach what this question tests.