Question 1
Difficulty: medium
How would you secure a machine learning pipeline from data ingestion through model deployment?
Sample answer
I’d treat the ML pipeline like any other production system, but with extra controls around data integrity, model behavior, and dependency risk. I’d start by securing data ingestion with source validation, schema checks, access control, and signed or hashed datasets where possible. During training, I’d isolate environments, restrict who can modify datasets or code, and log every experiment for traceability. I’d also scan third-party libraries and container images for vulnerabilities because ML stacks often pull in a lot of dependencies. Before deployment, I’d test the model for adversarial sensitivity, data leakage, and unsafe outputs, then gate releases behind approval and monitoring. In production, I’d monitor for drift, abuse patterns, unusual input distributions, and signs of prompt injection or model extraction. The key is building security into each stage rather than trying to bolt it on after the model is live.
Question 2
Difficulty: medium
Tell me about a time you identified a security risk in an AI system and helped fix it.
Sample answer
In a previous project, I noticed that an internal support chatbot was pulling from a large knowledge base without strong document-level permissions. That created a risk where users could potentially surface information they were not supposed to see, especially through creative prompts. I worked with the product and platform teams to define access rules at retrieval time instead of relying only on the model to behave correctly. We added authorization checks, filtered documents by user role, and introduced logging to detect suspicious query patterns. I also pushed for a red-team exercise to see how easily the bot could be coaxed into revealing restricted content. That testing uncovered a few gaps in the retrieval logic and prompt construction. We fixed those before launch and added ongoing monitoring. What I learned was that AI security is rarely just a model problem; it’s usually a system design problem.
Question 3
Difficulty: hard
How do you defend against prompt injection in an LLM-powered application?
Sample answer
I approach prompt injection as both a product design and a control problem. First, I assume the model cannot be trusted to distinguish instructions from malicious content on its own. So I minimize the amount of sensitive context it sees, especially from untrusted sources like web pages, emails, or user-uploaded documents. I separate system instructions from retrieved content, and I label or sandbox untrusted inputs so they’re never treated as higher-priority instructions. I also constrain the model’s capabilities: if it can call tools, I put strong authorization checks around each action and validate parameters before execution. On top of that, I use output filtering, logging, and anomaly detection to catch unusual behaviors. For high-risk workflows, I prefer human approval for sensitive actions. Prompt injection defenses work best as layered controls, not as a single clever prompt.
Question 4
Difficulty: hard
What steps would you take to assess whether a model is vulnerable to data leakage?
Sample answer
I’d start by defining what kind of leakage we care about: training data memorization, sensitive prompt exposure, or unintended disclosure through retrieval and logs. Then I’d test the system using controlled canary strings and sensitive-pattern probes to see whether the model reproduces specific data from training or from connected sources. If it’s an LLM application, I’d also inspect the retrieval pipeline, conversation history handling, and telemetry storage, because leakage often happens outside the model itself. I’d review whether logs are redacting secrets and whether embeddings or caches contain sensitive content. From there, I’d run adversarial prompts designed to coax out private information, and I’d measure response rates across different temperatures and context lengths. If I found issues, I’d recommend data minimization, stronger access controls, redaction, and retraining or fine-tuning with sensitive examples removed. I’d also put monitoring in place so leakage attempts are detected early, not after release.
Question 5
Difficulty: medium
Describe how you would evaluate the security of a third-party AI model or API before integrating it.
Sample answer
I’d evaluate it the same way I’d assess any external dependency, but with a stronger focus on data handling and behavioral risk. First I’d review the vendor’s security documentation: where data is stored, whether prompts are retained, what the retention period is, and how they isolate tenants. I’d check contractual details too, because compliance and incident response expectations matter. Then I’d test the API with a representative set of inputs, including malicious prompts, edge cases, and sensitive data patterns, to see how it behaves and what it logs. I’d want to know whether the model can be abused for exfiltration, whether it supports zero-retention modes, and how it handles rate limiting and abuse detection. I’d also review the SDK and integration code for secret management and dependency risks. If the service touches high-value or regulated data, I’d require a stricter approval process and possibly a proxy layer so we can enforce policy on top of the vendor API.
Question 6
Difficulty: medium
How would you respond if a product team wanted to launch an AI feature quickly, but you believed the security review was incomplete?
Sample answer
I’d try to be direct without becoming a blocker for the sake of blocking. My first step would be to explain the specific risks in business terms: what could go wrong, how likely it is, and what the impact would be if it happened in production. I’d separate issues into must-fix items and items that can be accepted temporarily with compensating controls. If the launch is truly urgent, I’d propose a narrow rollout, feature flags, limited data exposure, and extra monitoring so we can reduce blast radius while the remaining fixes are completed. I’d also document the decision clearly so leadership understands the tradeoffs. In these situations, I’ve found it helps to offer a path forward rather than just saying no. Good security engineering is about enabling safe delivery, not slowing everyone down. If the risk is unacceptable, though, I’d be firm and escalate appropriately.
Question 7
Difficulty: medium
What logging and monitoring would you put in place for an AI system in production?
Sample answer
I’d log enough to investigate abuse and incidents, but not so much that we create a new privacy problem. At a minimum, I’d capture request metadata, model version, user or service identity, tool calls, policy decisions, latency, and error states. For text inputs and outputs, I’d be careful about redaction and sampling, especially if the system handles personal or confidential data. I’d want dashboards that show spikes in unusual prompts, repeated failed attempts, unexpected tool usage, rate-limit events, and output policy violations. For LLM systems, I’d also monitor for prompt injection indicators, retrieval anomalies, and content that suggests data exfiltration. Alerts should be tied to response playbooks, not just noise. When possible, I’d correlate model events with application and infrastructure logs so we can trace a suspicious behavior end to end. The goal is to detect abuse early, support investigations, and prove that controls are working over time.
Question 8
Difficulty: hard
How do you think about adversarial ML threats such as evasion, poisoning, or model extraction?
Sample answer
I think of adversarial ML as a set of different attack classes that require different defenses. For evasion, I focus on robust input validation, anomaly detection, and testing the model against realistic adversarial examples before release. For poisoning, the priority is data provenance, access control, and review processes around training data and feedback loops, because compromised data can quietly degrade a model over time. For model extraction, I’d look at rate limiting, output throttling, watermarking where appropriate, and behavioral monitoring to detect systematic querying. I also think it’s important to match the defense to the value of the asset. A public classifier may need a different control set than a proprietary model trained on sensitive data. In practice, the biggest win usually comes from reducing trust in the input pipeline and tightening observability. No single control stops every adversarial threat, so I build layered protections and test them regularly.
Question 9
Difficulty: easy
Tell me about a time you had to explain a complex AI security issue to non-technical stakeholders.
Sample answer
I once had to brief a leadership group about why a new AI assistant could not be released with unrestricted access to internal documents. The technical concern was about retrieval access control and prompt injection, but I knew that framing it in those terms would not help most of the audience. So I translated the issue into a simple scenario: if the assistant can read more than the user is allowed to see, then the model can unintentionally become a disclosure path. I explained that the risk was not that the model was “smart” or “unsafe,” but that it was connected to data and tools without enough guardrails. I used a few concrete examples of what could leak and what the business impact would be. That helped the team understand why we needed role-based filtering and staged rollout. We aligned on a safer launch plan, and the project stayed on schedule with much lower risk.
Question 10
Difficulty: hard
What would you do if you suspected an AI model in production was being manipulated or abused by attackers?
Sample answer
I’d treat it like a live security incident and move quickly to contain the risk while preserving evidence. First, I’d confirm the signals: unusual prompt patterns, suspicious tool calls, spikes in failed requests, or outputs that indicate abuse. Then I’d reduce exposure by rate limiting, disabling risky tools, or switching the system into a safer fallback mode if needed. I’d coordinate with incident response, product, and infrastructure teams so we have one clear response path. At the same time, I’d preserve logs, request histories, model versions, and relevant configuration so we can understand the attack and not lose forensic detail. Once the immediate threat is contained, I’d identify root cause: was it prompt injection, credential misuse, data poisoning, or something in the app layer? After that, I’d patch the weakness, retest the control, and update monitoring so we catch similar activity earlier next time. Speed matters, but so does learning from the event.
