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What You'll Learn

Explain the adversarial ML taxonomy from NIST AI 100-2e2025
including evasion
poisoning
privacy
and generative AI-specific attacks,Analyze prompt injection
sensitive information disclosure
and other LLM-specific risks per the 2025 OWASP LLM Top 10,Evaluate agentic AI and multi-agent security risks
including goal hijacking
tool misuse
and cascading agent failures,Apply defensive strategies and governance practices to real-world AI security incidents across regulated industries

Requirements

Basic familiarity with machine learning concepts and general IT security principles (authentication
access control
common attack types) is helpful but not required. No prior AI security experience or certification is necessary — this course builds understanding progressively from foundational taxonomy through advanced agentic and governance topics
so beginners with a general technical background can follow along.

Description

AI Security has become one of the fastest-moving areas in cybersecurity, and understanding it deeply — not just superficially — is now a core expectation for security engineers, ML practitioners, and IT leaders working with AI systems. This course delivers 600 rigorously researched, scenario-based practice questions across six full-length tests, each modeled on how AI security concepts actually show up in real interviews, red-team engagements, and day-to-day governance decisions. The course is grounded in primary sources: NIST AI 100-2e2025 (the foundational adversarial machine learning taxonomy), MITRE ATLAS (real-world adversary tactics and techniques), the 2025 OWASP Top 10 for LLM Applications, and the OWASP Top 10 for Agentic Applications 2026. It covers the full arc of AI security — evasion and poisoning attacks against predictive AI, privacy attacks like model extraction and membership inference, prompt injection and LLM-specific risks, agentic AI and multi-agent security, and the governance practices that mature AI-security programs actually rely on. Every single question includes a detailed explanation for every answer option — not just the correct one — so you understand exactly why each choice is right or wrong. Questions build progressively within and across tests, using realistic organizational scenarios spanning healthcare, finance, government, critical infrastructure, and more. Sample question from Test 2 (Evasion & Poisoning Attacks): "Why is the Fast Gradient Sign Method considered a foundational but genuinely limited evasion technique compared to an iterative approach like Projected Gradient Descent?" — with four fully explained answer choices. Whether you're preparing for a security interview, building toward an AI security role, or deepening your understanding of this critical field, this course provides thorough, honest, and technically accurate preparation.

Who this course is for:

This course is for security engineers
ML/AI practitioners
and IT professionals preparing for roles involving AI security
red-teaming
or governance. Its well suited to anyone studying for interviews touching on adversarial machine learning
LLM security
or agentic AI risk
as well as security leaders who need a working vocabulary for evaluating AI vendors
building incident-response processes
or briefing leadership on AI-related risk. No prior AI security certification is assumed — the course builds from foundational concepts through advanced
real-world scenarios.