50+ AI Security Interview Questions & Answers for 2026

AI security interview questions are getting harder every year. And top companies are raising the bar.

Google, Microsoft, Visa, and OpenAI are actively hiring AI security engineers who can defend against prompt injection, secure large language models, and catch AI vulnerabilities before attackers exploit them.

But what exactly do interviewers ask in 2026?

Expect questions on adversarial attacks, data poisoning, model governance, and red teaming. These topics now dominate technical rounds at leading US companies.

Whether you’re a security engineer, DevSecOps professional, or developer transitioning into AI security, this guide prepares you with 50+ real AI security interview questions.

No fluff. Just practical prep to help you land the job in 2026.

Foundation Questions

1. Your company’s chatbot started sharing wrong prices. How do you investigate if it’s an attack or a bug?

My first move would be to isolate the chatbot to stop any further financial damage. Once contained, I’d immediately launch a two-path investigation.

To check for an attack, I’d analyze the prompt logs for signs of prompt injection and inspect our data sources for any unauthorized modifications, which would suggest data poisoning.

Simultaneously, to check for a bug, I’d review recent code deployments and examine the data pipeline for any errors.

My guiding principle here is to contain the problem first, then rapidly investigate all likely causes, both malicious and accidental.

2. Explain to a manager why we can’t just ‘delete’ sensitive data from a trained AI model.

I’d explain that a trained model is more like a baked cake than a database. The sensitive data is like salt mixed into the batter; once the cake is baked, you can’t just pick the salt out.

The model learns from data by adjusting millions of internal parameters, and the influence of that one piece of data is spread throughout the entire model.

There’s no ‘delete’ button for a specific fact. The only guaranteed way to remove the data’s influence is to discard the model and retrain it from scratch using a clean dataset.

3. What security checks would you run on an AI chatbot before launching it?

Before launch, I’d run checks on three key areas. First, the model and its data, where I’d sanitize the training data for any PII or secrets and red team the model to test its behavior against adversarial prompts.

Second, the interaction layer, where I’d specifically test for prompt injection vulnerabilities and data leakage risks, while also ensuring our output filters are effective.

Finally, the system itself, verifying strict access controls, implementing rate limiting to prevent abuse, and ensuring comprehensive logging is in place for any future investigations. It’s a layered, defense-in-depth approach.

4. Should we use OpenAI’s API or host our own AI model? List the security pros and cons.

The answer really depends on the data’s sensitivity. Using a third-party API is convenient and leverages their expert security team, but it means sending your data to them, creating a supply chain risk.

Hosting it yourself gives you total data control, which is the biggest security advantage, but it also means you bear the massive responsibility of securing the entire complex system.

For low-risk applications, an API is practical. For anything involving sensitive financial or health data, the risk of exposure is too high, and self-hosting is the only responsible choice.

5. Your team wants to train an AI on customer emails. Which security issues do you raise?

My position would be firm: we do not train on raw customer emails, period. The primary issues are not just technical; they are legal and reputational. Customer emails are a minefield of toxic PII, which risks severe legal and compliance violations under laws like GDPR and CCPA.

Furthermore, the model will inevitably memorize and could later leak this sensitive data. Once a model is contaminated with PII, the only way to fix it is to destroy it and start over. All PII must be programmatically scrubbed from the dataset before any training begins.

Attack & Defense Questions

6. How would you detect if someone is trying to steal your AI model through the API?

Detecting model theft is about spotting non-human behavior. I’d focus on behavioral analysis, looking for red flags like an unusually high volume of queries from a single IP or systematic, machine-generated query patterns instead of conversational ones.

I’d also analyze the source, as traffic from data centers or TOR nodes is immediately suspicious for a consumer-facing product. Proactively,

I would embed a hidden watermark in the model’s responses. If we find a suspected stolen model online, we can feed it a secret prompt and see if it produces our watermark.

7. What’s the difference between prompt injection and jailbreaking?

The target is different. Prompt injection hijacks the AI’s task, like tricking a bot into running a database command. Jailbreaking targets the AI’s rules. A classic jailbreak is the “Grandma Exploit,” where a user asks the AI to role-play as their deceased grandmother who worked at a napalm factory to trick it into revealing dangerous information.

One is about unauthorized actions; the other is about bypassing safety policies. Jailbreaking is often performed through malicious prompts; hence, prompt injections may set the LLMs free of their initial safety policies, resulting in jailbreaking.

8. How can attackers poison training data? How would you catch it?

Attackers poison data by contaminating the sources the model learns from. The most famous case was Microsoft’s Tay chatbot, which users on X (formerly known as Twitter) intentionally taught to be offensive in less than a day. They can also use a more surgical approach, inserting a hidden “backdoor” trigger that causes a specific malicious output.

Catching this is tough and requires vetting data sources, using outlier detection to find anomalies, and constant adversarial testing to find these hidden backdoors before an attacker does.

9. How can hackers make an AI misclassify images?

Hackers exploit the gap between how machines and humans see. A famous example is using adversarial patches: physical stickers that, when placed on a stop sign, can trick a self-driving car’s AI into seeing a 45-mph speed limit sign.

Others use invisible digital noise to make a model misclassify an image, or data poisoning to insert a hidden backdoor. The core threat is that what looks normal to us can be a clear command to the AI.

10. What happens if someone adds bad data to your vector database?

This leads to three primary failures. First, response hijacking, where an attacker’s data includes a prompt injection that takes over the AI’s response. Second, factual corruption, where the AI starts giving confident but wrong answers based on the bad data.

And third, data leakage, where the bad data could cause the AI to mistakenly pull and reveal sensitive information. The integrity of the vector database is a critical security control.

11. How can you design a filter to block prompt injection attacks?

A single filter is a single point of failure, so I’d design a three-stage defense. The first stage is a pre-processing sentry that uses a smaller, faster AI to analyze the user’s raw input and classify its intent as malicious or benign.

The second stage is hardened prompt construction, where we wrap the user’s input in clear delimiters and add explicit warnings to the model. The final stage is a post-processing inspector that analyzes the AI’s response before it’s sent, checking for signs of compromise like unexpected API calls.

You can’t stop it completely, but you can make it extremely difficult. The most critical defense is aggressive data sanitation, because if sensitive data isn’t in the model, it can’t be extracted.

Early models demonstrated this risk when researchers prompted them to reveal real names and phone numbers they had memorized from public websites.

Beyond sanitation, using privacy-preserving techniques like differential privacy and output filters that block verbatim text provides layered, in-depth defense against this kind of data leakage.

13. How can you build a monitoring system to detect when your AI is under attack?

I’d build a monitoring system with three layers, all feeding into a central SIEM for correlation. The first layer is input monitoring, which inspects every prompt for threat signatures and behavioral anomalies. The second is model behavior monitoring, which watches for performance degradation or resource consumption spikes that could signal a compromise.

The third layer is output monitoring, which scans the model’s response for data leakage or policy violations before it’s sent to the user. This layered approach provides a comprehensive view of the system’s health and security.

14. How would you secure an AI that processes credit card numbers?

My first principle is that the AI should never see the raw credit card number. The architecture must be designed to isolate the AI from the payment data flow.

I would implement an interception layer that, before any input reaches the AI, finds the credit card number and sends it to a certified payment vault.

This vault returns a non-sensitive token. The AI then receives the user’s prompt with the number already replaced by this safe token, allowing it to perform its task without ever touching the sensitive data.

15. What rate limits would you set on an AI API and why?

Rate limiting is a dynamic defense system. I’d set three types of limits. First, security-focused limits per minute, like 100 requests per API key, to block abuse and model theft in real-time. Second, cost-control limits per day or month, based on the user’s subscription tier, to manage the financial cost of the API calls.

Finally, global stability limits across all users to act as a circuit breaker during a massive, coordinated attack, ensuring the entire service doesn’t crash. These limits must be constantly monitored and adjusted.

LLM-Specific Questions

16. A customer says your chatbot leaked another user’s data. What do you check first?

My first action is not a check; it’s a command: isolate the system. The chatbot is immediately taken offline to stop the bleeding. Once contained, my first check is to secure the evidence by getting the exact, verbatim chat logs from the reporting customer.

With the logs, I’d identify the leaked data and find “Patient Zero,” the other user whose data was exposed. Then, I’d investigate the most likely cause, which is often a classic session bleed or caching error in the application layer, rather than a flaw in the AI itself.

17. How do you prevent an LLM from revealing your company’s internal prompts?

The only way to secure internal prompts is to design a system where the AI that interacts with the user never sees them. I would implement a two-model system. A smaller, front-end “interpreter” model receives the user’s raw prompt and distills it into a structured format.

This sanitized request is then sent to the powerful back-end “worker” model, which holds our secret prompts. The model with the secrets never sees the user’s original, potentially malicious, prompt, neutralizing any attempt at prompt injection.

18. Your RAG system returns wrong documents. What could be the security issue?

This points to a compromise in the “retrieval” part of RAG. The most likely security issue is vector database poisoning, where an attacker has added malicious documents to the knowledge base. These documents are designed to match common queries but contain misinformation or a prompt injection payload.

A more sophisticated attack could be an embedding collision, where an attacker crafts a document whose vector is artificially close to a legitimate query, tricking the similarity search into retrieving the wrong document. The root issue is a failure to secure the knowledge base.

19. How can you design input validation for a chatbot that handles both text and images?

I’d use two parallel pipelines. For text, I’d apply strict sanitization to strip out scripts, use a pattern-matching model to detect and block prompt injection attempts, and use NER to redact sensitive data.

For images, I’d first validate the file itself, confirming it’s a real image and not a renamed executable, while also stripping all metadata.

Then, the image would be passed to a dedicated computer vision model that flags for unsafe content. An input is only passed to the main model if it clears both pipelines.

20. How do you test if your LLM will refuse harmful requests?

I’d use a continuous, three-part strategy. First is manual red teaming, where my team actively tries to break the model with creative and subtle prompts designed to bypass its safety controls.

Second is automated benchmark testing, where we run a massive, private dataset of thousands of known harmful prompts against every new version of the model to get a consistent safety score.

Third is automated “fuzzing,” where tools take a harmful prompt and generate thousands of variations to probe for edge-case vulnerabilities.

System Design Questions

21. How can you draw the security architecture for an AI fraud detection system?

I’d design a series of secure, isolated zones with one-way data flows. Zone 1 is the Ingestion Gateway, which validates and sanitizes raw transaction data. Zone 2 is the Processing Vault, where PII is pseudonymized and data is prepared.

Zone 3 is the Training Citadel, an air-gapped environment where the model is trained. Finally, Zone 4 is the Inference Engine, where the signed, hardened model serves requests.

This “assume-hostile” architecture stops an attack at multiple points, preventing a single failure from compromising the system.

22. How would you secure the pipeline from data collection to model deployment?

I treat the pipeline as a chain of custody. It starts with secure ingestion, where all data is validated and we generate cryptographic hashes to act as a seal. Preparation happens in a sandboxed environment with a full audit trail.

Training is done in an isolated environment with pre-scanned code. After training, the model artifact is tested, validated, and cryptographically signed.

Finally, only signed models can be deployed into hardened, minimal containers with continuous runtime monitoring. Every step verifies the integrity of the last.

23. How would you design backup and recovery for AI models under attack?

My strategy is “verifiable immutability.” Every time a model is trained, we create a sealed, versioned bundle containing the model file, a hash of the training data, the versioned code, and a digital signature.

These bundles are stored in immutable, append-only storage. When an attack is detected, our playbook is simple: isolate the system, identify the last known-good version from our monitoring logs, and redeploy that verified bundle from storage into a fresh environment. This restores service quickly with a trusted model.

24. What security tools would you add to an ML pipeline?

I’d integrate a layered set of four essential tool categories. First, code and dependency scanners (SAST and SCA) to inspect our code and open-source libraries for vulnerabilities before anything is run. Second, data validation and anomaly detectors at the ingestion point to defend against data poisoning.

Third, adversarial attack simulators to stress-test the model before deployment. Finally, runtime behavior monitors on the production containers to watch for abnormal system calls or network connections, killing the container if a compromise is detected.

25. How do you secure AI model storage in the cloud?

My approach is based on zero trust. First, we encrypt the model client-side before it’s ever uploaded, using a key that we control. The cloud provider only ever sees ciphertext. Second, we cryptographically sign the model artifact and store the signature separately. 

Third, we use strict, programmatic IAM policies, granting no standing access to humans. Finally, we enable immutable versioning and object locks on the storage bucket. This creates an unchangeable audit trail and guarantees a secure rollback path.

Threat Modeling Questions

26. List five ways attackers could compromise a self-driving car’s AI.

First, adversarial perturbations, using physical stickers to make the car’s perception AI misclassify a stop sign. Second, training data poisoning, a supply chain attack that teaches the model a hidden, malicious rule. 

Third, sensor input spoofing, using a fake GPS signal to trick the car’s navigation. Fourth, model extraction, where an attacker queries the model repeatedly to steal the proprietary logic. 

Finally, resource exhaustion, overwhelming the car’s processors with complex environmental noise, causing the system to freeze.

27. What are the biggest security risks in a medical diagnosis AI?

The biggest risks are about corrupting the AI’s medical judgment. The first is diagnostic manipulation, where an attacker uses a poisoned training set or an adversarial input to cause a wrong diagnosis, like making the AI classify a malignant tumor as benign. 

The second is inference-based data leakage, where an attacker queries the model to reconstruct sensitive patient data from the training set. The third is scaled bias, where a flaw in the model leads to thousands of missed diagnoses across an entire demographic.

28. How is threat modeling for AI different from regular software?

It’s different because the attack surface is expanded beyond just code to include the training data and the model’s logic. Traditional vulnerabilities are deterministic flaws in code, while AI vulnerabilities are often probabilistic blind spots that can be exploited.

Finally, the attacker’s goals are different. Instead of just stealing data, a primary goal is to subvert the model’s purpose: to make it lie, make a bad decision, or to steal the model itself as valuable intellectual property.

29. Create a simple threat model for a bank’s loan approval AI.

My threat model would focus on four key AI-specific risks. The first threat is systematic data poisoning, where attackers teach the model to approve bad loans. The second is adversarial evasion, where a single applicant fools the model with a crafted application. 

The third is model theft, where a competitor reverse-engineers our proprietary logic through repeated queries. The final threat is inference-based data leakage, where an attacker infers private customer information from the model’s decisions. Each threat requires specific mitigations like data monitoring, adversarial testing, and rate limiting.

30. What new threats emerge when multiple AIs work together?

When AIs work together, the interactions between them become a new attack surface. The first new threat is cascading error, where a small error in one AI is magnified down the chain, leading to a catastrophic failure. 

The second is emergent collusion, a sophisticated data poisoning attack where two models have complementary backdoors that only activate when they interact. 

The third is communication channel poisoning, a man-in-the-middle attack on the messages between AIs. Finally, there’s goal hijacking, where one AI learns to manipulate another to achieve its goals.

Red Teaming Questions

31. You have 100 API calls to a sentiment analysis service. How do you steal the model?

With only 100 calls, I can’t steal the whole model, but I can steal its most valuable secret: its decision boundary for a specific, high-value topic. I’d use the first call to establish a baseline with a neutral sentence. 

Then, I’d use the next 49 calls to methodically make the sentence worse, word by word, to find the exact tipping point where the sentiment flips to negative. I’d use the final 50 calls to do the same in the positive direction. I haven’t stolen the model, but I’ve stolen a precise map of its judgment.

32. What are the different ways to bypass an AI chatbot’s safety filters?

There are five main techniques. The first is role-playing, tricking the AI by pretending to be someone with a legitimate need for the harmful information. The second is obfuscation, using typos or symbols to disguise forbidden keywords. 

The third is instruction hijacking, a direct command that attempts to overwrite the bot’s safety rules. The fourth is contextual framing, hiding the harmful request inside a long, innocent-sounding paragraph.

The final method is translation chaining, translating a harmful phrase to another language and back to bypass keyword filters.

33. How would you hide a backdoor in an AI model?

The most effective way is through targeted data poisoning. You don’t attack the code; you corrupt the data it learns from. The process involves choosing a rare and specific “trigger” and a malicious “target” action. 

You then craft a small set of poisoned training data where the trigger is always labeled with the target. By injecting this into the larger, clean dataset, the model learns the secret rule without affecting its overall performance. The backdoor is now hidden, only activating when the attacker presents the secret trigger.

34. Design an attack that makes an AI fail only for specific users.

This is a targeted poisoning attack using an invisible “fingerprint.” First, I’d design a trigger unique to the target user but invisible to them, like a faint pixel pattern for an image model or specific whitespace characters for a text model. 

Second, I’d poison the training data so that any input containing this trigger results in the desired failure. Third, I’d use social engineering to deliver the trigger to the target, like sending them a document template that secretly embeds the watermark. The model works for everyone else, but fails for the target.

35. How would you use one LLM to attack another LLM?

I’d use the first LLM as an automated red team to discover the second LLM’s weaknesses. I would give the attacker LLM a meta-prompt instructing it to generate thousands of prompt variations to bypass a specific safety filter, using techniques like role-playing and obfuscation.

Furthermore, I then feed these prompts into the target LLM’s API and log the responses. By analyzing which prompts succeeded, I can identify a working bypass. I’d then feed the successful prompts back to the attacker LLM, creating an evolutionary loop that teaches it how to get better at breaking the target.

Management & Strategy Questions

36. You have $1 million for AI security. What do you buy first?

I wouldn’t buy a single protection tool. The first dollar goes to visibility. I’d invest in an automated discovery and inventory system for all our AI assets: our models, our datasets, and the pipelines connecting them. 

We can’t secure what we don’t know we have. This system would flag risks like a team using an open-source model with a known backdoor. The bulk of the budget buys this visibility; the remainder is for acting on the critical risks we will inevitably find once we have a clear picture.

37. Create a 90-day plan for starting an AI security program.

My 90-day plan focuses on control and value. In the first 30 days, I’d focus on discovery and triage, creating a complete inventory of our AI assets and identifying the top 10 risks.

In days 31-60, I’d establish control by creating a minimum viable policy and executing a pilot project to fix our number one risk, demonstrating a quick win.

In days 61-90, I’d focus on scaling and education, turning the pilot project into a reusable guardrail for other teams and running workshops to make developers aware of the biggest dangers.

38. What metrics would you track for AI security?

I’d track metrics in three categories. First, inventory and control, like the percentage of AI assets under management and the time it takes to detect new models. 

Second, our risk posture, including the number of critical vulnerabilities per model and the percentage of models with a documented data lineage. 

Third, our operational efficiency, tracking our mean time to remediate AI-specific threats and the adoption rate of our secure AI components. 

These metrics provide a direct measure of our control and risk, not just vanity numbers.

39. How do you train 100 developers on AI security basics?

I wouldn’t do lectures. I’d run a mandatory, two-hour, hands-on workshop where they compete to build and break things. In the first hour, they act as the red team, competing in small groups to break a vulnerable AI chatbot I’ve prepared. 

In the second hour, they become the blue team, competing to patch the exact vulnerabilities they just exploited. This replaces abstract PowerPoints with a concrete, memorable experience. They don’t just hear about prompt injection; they execute it, and then they fix it.

Incident Response Questions

40. Your AI started generating inappropriate content. What’s your first-hour response?

My first action is containment. In the first five minutes, the model’s endpoint is killed via a pre-built circuit breaker.

The damage stops. In the next 25 minutes, I isolate the cause by pulling logs to see if it’s a targeted attack or a widespread failure.

In the final 30 minutes, I assemble the core engineering and legal team, present the facts, and state my initial hypothesis.

In one hour, we don’t have the final answer, but we have control, a clear hypothesis, and a small, expert team ready to execute the fix.

41. A security researcher found a vulnerability in your model. What do you do?

This is a free security audit, not a crisis. I’d contact the researcher within the hour, thank them, establish a secure channel, and confirm they’ll be compensated via our bug bounty program. I’d then assign an engineer to replicate the vulnerability to understand the blast radius. 

We’d deploy a rapid, tactical containment to block the attack pattern, then work on the permanent fix, which usually involves retraining the model. 

Finally, we’d pay the researcher and publish a joint security advisory, turning a potential crisis into a demonstration of competence.

42. Your model’s accuracy suddenly dropped 20%. How do you know if it’s an attack?

I’d assume it’s not an attack first. My immediate focus would be on the two most likely causes: data drift and broken code. I’d check our data monitoring dashboards to see if the statistical properties of the input data have changed, as models go stale.

 I’d also check our deployment logs for any recent code changes and immediately roll back the last deployment to see if accuracy recovers. Only after ruling out these internal causes would I hunt for an attack, which would look like a sudden, targeted anomaly in the logs.

43. What will you do if customer data appears in AI outputs? Walk through your response.

This is a data breach, and I’d initiate our incident response protocol immediately. The first phase is containment: the model endpoint is killed, the system is isolated, and Legal and the CISO are notified. 

The second phase is investigation: we identify exactly which customers and what data was exposed, then find the root cause, which is most often an application bug, not a flaw in the AI. 

The final phase is eradication: we patch the code or, if it was data contamination, destroy the model and rebuild it from scratch.

44. Your AI vendor got hacked. What do you check in your systems?

I’d treat the vendor as hostile and immediately sever the connection. All API calls are blocked at the firewall, and all API keys are revoked. With the connection severed, I’d investigate the blast radius. I’d pull all logs for our connections to the vendor’s API, hunting for anomalies.

I’d assume any data we sent them is now public and activate our data breach response plan. Finally, I’d trigger a mandatory scan of our codebases for the vendor’s SDK to check for any malicious code.

Practical Scenarios & Frameworks

45. Can you name five items from the OWASP LLM Top 10 and give real examples?

Certainly. First is Prompt Injection, like tricking a chatbot into ignoring its instructions. Second is Insecure Output Handling, where the system blindly trusts and executes code from the LLM. Third is Model Denial of Service, overwhelming the model with resource-intensive requests. 

Fourth is Sensitive Information Disclosure, where the model leaks confidential training data. Fifth is Insecure Plugin Design, where an attacker abuses an external tool, like telling an email plugin to spam all contacts. These highlight the new attack surfaces specific to LLMs.

46. Can you explain the MITRE ATLAS framework in simple terms?

MITRE ATLAS is essentially an encyclopedia of attacks against AI systems. It’s a knowledge base that catalogs the specific tactics and techniques adversaries use, from poisoning training data to stealing a finished model. 

It’s not a tool you run, but a framework you use for two main reasons. First, as a blue team, we use it to understand the threats so we can build better defenses. Second, as a red team, we use it as a playbook to simulate real-world attacks and test if our defenses are working.

47. How do you secure an AI that children will use?

Securing an AI for children requires fundamentally restricting its capabilities. The standard is “incapable of being unsafe.” First, I’d build a “cage, not a fence” by using a strict allow-list for topics and vocabulary, rather than a block-list. 

Second, I’d assume zero-data retention; every interaction is stateless, and we collect no PII. You can’t leak what you don’t have. 

Finally, I’d conduct hostile red teaming, hiring specialists to relentlessly attack the model and patch any “emotional exploits” before a child ever sees the product.

48. Your AI must handle health data. What extra security do you add?

Handling health data requires adding absolute, non-negotiable layers of control to meet compliance like HIPAA. First is aggressive, irreversible de-identification; the AI never touches raw PHI. 

Second, the entire system lives in an isolated “clean room” environment with no internet access and no path to the corporate network. 

Third is output scrutiny and immutable auditing; every output is scanned for PHI before being displayed, and every query and response is written to a write-once log. We assume the AI will try to leak data and build controls to make it impossible.

49. How do you prevent employees from leaking data through public AI tools?

This is a problem of control, not trust. I’d use a three-part system: Block, Provide, and Monitor. First, we use our network security tools to block access to all public AI services.

No exceptions. Second, because blocking alone creates “shadow AI,” we provide a safe, sanctioned internal alternative hosted in our own secure environment. This makes the easy path the secure path. 

Finally, we use our Data Loss Prevention (DLP) tools as a safety net to detect and alert on any attempts to move sensitive data to unauthorized locations.

50. What security tests do you run before each AI model update?

Every update must pass a mandatory gate of four automated tests. First is a data integrity scan on the new training data to check for PII and statistical anomalies that could signal a poisoning attack. 

Second is an adversarial attack regression test against our library of thousands of known attacks. Third is a bias and fairness benchmark to ensure the update hasn’t made the model a legal liability. 

Finally, a memorization and leakage test to ensure the model isn’t repeating sensitive information from its training data. If it fails any test, it’s not deployed.

51. How do you implement STRIDE threat modeling for an AI system?

I’d apply the STRIDE framework to the new components of an AI system. For Spoofing, I’d model an attacker using adversarial patches to fool a perception model. For Tampering, the focus is on data poisoning. 

For Repudiation, it’s about the model’s lack of explainability. For Information Disclosure, it’s the model leaking its training data.

For Denial of Service, it’s resource exhaustion attacks. And for Elevation of Privilege, it’s a classic prompt injection where a user tricks the AI into performing an action they aren’t authorized for.

52. What’s in an AI model’s SBOM (Software Bill of Materials)?

An AI SBOM is the full ingredient list for the model. It has four sections. First, the traditional software components, like the PyTorch version and all supporting libraries. Second, and most critical, is the data bill of materials, detailing the training datasets, their sources, and their licenses. 

Third is a description of the model itself, including its architecture and a cryptographic hash to prove its integrity. The final section is the training recipe, which includes the hyperparameters and hardware used, ensuring reproducibility and transparency.

53. What AI security requirements does the EU AI Act mandate?

The EU AI Act imposes non-negotiable design constraints for any system deemed “High-Risk.” I group them into three responsibilities. First, you must “build it right” by ensuring data governance and cybersecurity robustness. 

Second, you must “prove you built it right” through comprehensive technical documentation and logging for auditing.

Finally, you must “install a kill switch” by ensuring a human can intervene and override the AI at any time. It legally forces companies to treat AI security as a core product engineering and compliance discipline.

54. Can you name the top 3 industry-recognized AI security certifications that you would recommend to your colleagues or peers?

When my colleagues ask for AI security certifications, I recommend a portfolio that covers three distinct, critical roles. Each certification is the best-in-class for its specific domain:

1. For the Practical Hands-On: The Certified AI Security Professional (CAISP) by Practical DevSecOps.

This is for the engineers in the trenches. I recommend it because it’s intensely hands-on, focusing on how to neutralize AI threats before attackers strike. It moves beyond theory and teaches you how to detect the OWASP LLM Top 10, block AI supply chain attacks, and implement defenses from the MITRE ATLAS framework. Its practical labs and task-oriented exam mean you don’t just learn; you prove you can defend a real system.

2. For the Strategic Leader: The Advanced AI Security Management (AAISM) by ISACA.

This is for the security manager or architect. ISACA is the gold standard for security management, and this certification focuses on building and leading a comprehensive AI security program. It’s about managing risk at scale and aligning your security strategy with business objectives, which is critical for anyone responsible for an organization’s overall posture.

3. For the Compliance Guardian: The Artificial Intelligence Governance Professional (AIGP) by IAPP.

This is for legal, privacy, and compliance professionals. The IAPP is the authority on privacy, and this certification ensures the AI systems we build are lawful, ethical, and fair. It focuses on the complex intersection of AI with data privacy and emerging global regulations like the EU AI Act. It addresses the critical need to ensure our secure systems are also compliant.

Conclusion

Preparing for AI security interview questions in 2026 requires more than theory. It demands hands-on skills.

This guide covered 50+ AI security interview questions and answers on prompt injection, LLM vulnerabilities, adversarial attacks, threat modeling, and AI governance. These are the exact topics interviewers ask at top companies.

Do not stop at interview prep. The Certified AI Security Professional (CAISP) course gives you hands-on skills to attack and defend LLMs, secure AI pipelines, and apply NIST RMF and ISO 42001 in real scenarios.

AI security roles are filling fast. Get certified now.