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Safety, security and governance

English · Русский · AI Interview Atlas › Themes

Prompt injection, data exfiltration, guardrails, privacy, red-teaming, and the regulation and risk frameworks leaders are accountable for.

On this page: Both tracks (11) · AI Leadership (8)

Both tracks

  • An assistant reads external email, searches internal documents and sends replies. Where could an attacker redirect it, and how would you constrain the damage?
  • How would you organise safe deployment of an AI model into production?
  • What safeguards should surround an AI system that acts on a user's behalf?
  • Describe a situation where delivery pressure conflicted with security or safety concerns. How did you decide what to do?
    • Behavioral · Senior · Asked at: Anthropic 🗣 · ✍ Answer · Suggest a fix
    • Tests: Whether you make risk and accountability explicit under pressure.
    • A strong answer covers:
      • Describe the concrete hazard and what evidence was available then.
      • Explain alternatives, escalation and who could accept the remaining risk.
      • Report the result honestly and the control changed afterward.
    • Read: AI Risk Management Framework (NIST)
  • Design a system for detecting harmful content in Facebook and Instagram uploads.
  • A product team requests an exception to an AI safety control for one enterprise customer. How would you assess, authorise and time-limit the exception?
    • Applied scenario · Senior · 🧪 generated from job-posting themes · ✍ Answer · Suggest a fix
    • Tests: Whether exceptions retain an accountable owner, compensating controls and an expiry decision.
    • A strong answer covers:
      • Establish what the control protects against and who bears the risk if it is relaxed.
      • Prefer a narrower compensating control to a blanket exception, scoped to that customer.
      • Give the exception a named owner, an expiry date and a review that can refuse renewal.
    • Read: AI Act (European Commission)
  • Two partners at one law firm advise opposing sides of a deal. How would you isolate their AI workspaces?
    • System design · Senior · Suggest a fix
    • Tests: Whether access boundaries can be narrower than a customer tenant.
    • A strong answer covers:
      • Treat the matter, not the tenant, as the boundary: leakage across sides breaches client confidentiality.
      • Enforce it at retrieval time, and keep memory, caches, logs and tuning data per matter.
      • Probe for cross-matter leakage, audit every access, and track over-blocking of shared firm knowledge.
    • Read: Document-level access control (Microsoft Learn) · Architect multitenant solutions on Azure (Microsoft Learn)
  • Design safeguards for an open-ended character-chat service.
    • System design · Senior · Suggest a fix
    • Tests: Whether controls cover long conversations and escalation rather than isolated replies.
    • A strong answer covers:
      • Name harms that build over many turns: self-harm, sexualised content involving minors, emotional dependency.
      • Score the trajectory of the whole conversation, not single replies, and escalate to crisis resources or review.
      • Evaluate on long scripted conversations, counting missed escalations and needless interruptions of benign roleplay.
    • Read: Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations (Inan et al., arXiv) · AI Risk Management Framework (NIST)
  • When would you intervene during token generation rather than filter the completed answer?
    • Knowledge · Senior · Suggest a fix
    • Tests: Whether you weigh exposure, latency and partial-output risks.
    • A strong answer covers:
      • Intervene mid-stream when showing the text is itself the harm, such as leaked secrets or dangerous instructions.
      • Weigh latency against exposure: buffering delays the stream, while retracting shown text cannot undo it.
      • Measure harmful text shown before cutoff, false truncations of benign answers and added latency.
    • Read: Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations (Inan et al., arXiv) · How to use the Voice Live API (Microsoft Learn)
  • Design the safety architecture for a learned robot policy operating close to people.
  • Before launching an assistant that can use tools, how would you organise adversarial testing, and what would a clean result let you claim?
    • System design · 🧪 generated from job-posting themes · ✍ Answer · Suggest a fix
    • Tests: Whether adversarial testing is planned for coverage and tied to a decision, and whether its limits are stated honestly.
    • A strong answer covers:
      • Name the harms and both attacker positions first — a user misusing the product, and untrusted content the model reads — then choose cases that cover each.
      • Combine automated generation with people who did not build the system, and record every attempt rather than only the successes, so coverage is visible and can be re-run on the next version.
      • Tie findings to the release decision with owners and fixes, and separate a class of attack that is now blocked from one example that stopped working.
    • Read: OWASP Top 10 for LLM Applications (OWASP Gen AI Security Project) · AI Risk Management Framework (NIST) · Prompt injection (series) (Simon Willison)

AI Leadership

  • How would you approach safety for a generative-AI consumer product?
    • Product case · Senior · Asked at: OpenAI † · ✍ Answer · Suggest a fix
    • Tests: Whether safety requirements reflect actual users and plausible harm.
    • A strong answer covers:
      • Choose a concrete use case, user population and abuse model.
      • Combine product limits, model evaluation, reporting and human escalation.
      • Measure both harmful completions and unnecessary refusals by user segment.
    • Read: AI Risk Management Framework (NIST) · OWASP Top 10 for LLM Applications (OWASP Gen AI Security Project)
  • Which unresolved alignment problem deserves the most attention, and why?
  • How would you weigh a performance improvement against reduced model interpretability?
    • Applied scenario · Senior · Asked at: Anthropic † · ✍ Answer · Suggest a fix
    • Tests: Whether the trade-off depends on use, consequences and available controls.
    • A strong answer covers:
      • Ask what interpretability serves here: explaining decisions to those affected, contesting outcomes, validation or debugging.
      • Weigh the gain against error consequences and against controls that do not depend on model internals.
      • Verify the gain on the slices that matter, with outcome monitoring and a simpler fallback model.
    • Read: Supervisory Letter SR 11-7 on guidance on Model Risk Management (Board of Governors of the Federal Reserve System) · AI Risk Management Framework (NIST)
  • Some annotators submit answers produced by a chatbot instead of doing the assigned work. How would you detect and address this?
    • Applied scenario · Senior · Suggest a fix
    • Tests: Whether you investigate annotation quality without assuming an infallible AI detector.
    • A strong answer covers:
      • Name the harm: labels stop reflecting human judgement and contaminate training and evaluation data.
      • Rely on gold questions, agreement and timing signals, not a detector whose false positives punish honest annotators.
      • Confirm cases before sanctions, re-label affected data, and fix incentives and task design that invite shortcuts.
    • Read: Prevalence and prevention of large language model use in crowd work (Veniamin Veselovsky et al., arXiv)
  • An AI research memo relies on a court decision that has been overruled. Where should the workflow catch this?
    • Applied scenario · Senior · Suggest a fix
    • Tests: Whether you address source validity as well as faithful citation.
    • A strong answer covers:
      • Separate two checks: the memo states the decision faithfully, and the decision is still good law.
      • Check each authority's later treatment with a citator at retrieval and again before sign-off.
      • Seed test memos with overruled cases to measure catch rate, and track flags lawyers learn to ignore.
    • Read: KeyCite (Thomson Reuters)
  • How would you design age assurance when minors receive a substantially different product experience?
    • System design · Senior · Suggest a fix
    • Tests: Whether you consider privacy, evasion and the consequences of classification errors.
    • A strong answer covers:
      • State what each error costs: a minor in the adult product versus an adult wrongly restricted.
      • Layer signals by risk, escalating to verification only when needed and keeping as little data as possible.
      • Measure errors by age band, evasion attempts and drop-off from friction, and offer an appeal path.
    • Read: Age assurance duties under the Online Safety Act (Ofcom)
  • Design safeguards for voice cloning, including consent, provenance and abuse response.
    • System design · Senior · Suggest a fix
    • Tests: Whether you treat consent as a verifiable lifecycle requirement.
    • A strong answer covers:
      • Verify the speaker's own consent, for example with a live spoken phrase, before creating a voice.
      • Bind consent to the voice model so revocation disables it, and watermark or log generated audio.
      • Review impersonation reports and takedown requests fast, measuring abuse caught and wrongful blocks.
    • Read: Text to speech transparency note (Microsoft Learn)
  • A generated clinical note includes a medication never mentioned during the visit. How would you detect and contain this safety incident?

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