"How do you make an AI system secure and responsible — what are the biggest threats and how do you address them?"

The security follow-up to Session 9's production-design question. Interviewers ask this to check whether you think past "does it work" to "is it safe to operate" — a signal of senior-level thinking.

Miss one and they probe.

1. Threat surface — name the four AI-specific attacks by name, distinct from SQL injection/XSS
2. Defence layers — defence in depth, controls at every layer, no single fix
3. Responsible AI — fairness, transparency, accountability, privacy as design constraints, not a checklist
4. Bias & hallucination — both structural, not patchable with a better model
5. Governance — risk register, named owner, escalation path

"AI systems have a threat surface traditional API security doesn't cover — prompt injection, data poisoning, model inversion, and adversarial inputs are all unique to AI. The defence strategy is the same one we use in any complex system: defence in depth — validate and sanitise inputs at the gateway, harden the system prompt at the LLM layer, filter outputs before they reach users, log everything. Responsible AI means building fairness, transparency, accountability, and privacy in from the start. Bias enters through training data and reward signals and needs ongoing demographic auditing. Hallucination is structural — it can't be patched — so for high-stakes decisions I'd always have a human-in-the-loop checkpoint. I'd ship with an AI risk register, a named owner, and a documented escalation path, so when something goes wrong, we know exactly who owns it and what to do."

All five beats, in order — that's what separates this from a vague "we'd add some guardrails" answer.