600 practice questions covering adversarial ML, prompt injection, agentic AI security, and NIST, MITRE & OWASP framework
Sub Category
- Network & Security
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Objectives
- 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
Pre Requisites
- 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.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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