AI Security Interview Questions - AI Security Training And Certification

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AI Security Interview Questions - AI Security Training and Certification - 2026

16h ago 16:42

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Enroll now in the Certified AI Security Professional (CAISP) course by Practical DevSecOps! This highly recommended certification is designed for the engineers , focusing intensely on the hands-on skills required to neutralize AI threats before attackers strike.
The CAISP curriculum moves beyond theoretical knowledge, teaching you how to secure AI systems using the OWASP LLM Top 10 and implement defenses based on the MITRE ATLAS framework.
You will explore AI supply chain risks and best practices for securing data pipelines and infrastructure. Furthermore, the course gives you hands-on experience to attack and defend Large Language Models (LLMs), secure AI pipelines, and apply essential compliance frameworks like NIST RMF and ISO 42001 in real-world scenarios.
By mastering these practical labs and successfully completing the task-oriented exam, you will prove your capability to defend a real system.
This episode draws on a comprehensive guide covering over 50 real AI security interview questions for 2026, touching upon the exact topics that dominate technical rounds at leading US companies like Google, Microsoft, Visa, and OpenAI.
Key areas explored include:
Attack & Defense Strategies: You will gain insight into critical attack vectors such as prompt injection, which hijacks an AI's task, versus jailbreaking, which targets the AI's safety rules (e.g., the "Grandma Exploit").
Learn how attackers execute data poisoning by contaminating data sources, illustrated by the famous Microsoft’s Tay chatbot incident. Understand adversarial attacks, such as using physical stickers (adversarial patches) to trick a self-driving car’s AI into misclassifying a stop sign, and the dangers of model theft and vector database poisoning.
Essential defense mechanisms are detailed, including designing a three-stage filter to block prompt injection using pre-processing sentries, hardened prompt construction, and post-processing inspectors.
Furthermore, you will learn layered defenses, such as aggressive data sanitation and using privacy-preserving techniques like differential privacy, to stop users from extracting training data from your model.
Secure System Design: The discussion covers designing an "assume-hostile" AI fraud detection architecture using secure, isolated zones like the Ingestion Gateway, Processing Vault, Training Citadel (air-gapped), and Inference Engine.
Strategies for securing the entire pipeline from data collection to model deployment involve treating the process as a chain of custody, generating cryptographic hashes to seal data integrity, and ensuring only cryptographically signed models are deployed into hardened containers.
Security tools integrated into the ML pipeline should include code/dependency scanners (SAST/SCA), data validation detectors, adversarial attack simulators, and runtime behavior monitors. When securing AI model storage in the cloud, a zero-trust approach is required, including client-side encryption, cryptographic signing, and strict, programmatic IAM policies.
Threat Modeling and Governance: Explore how threat modeling for AI differs from traditional software by expanding the attack surface to include training data and model logic, focusing on probabilistic blind spots, and aiming to subvert the model's purpose rather than just stealing data.
We cover the application of frameworks like STRIDE to AI

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13 episodes

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