The Threat Modeling AI/ML Systems course provides a comprehensive introduction to securing AI/ML applications. Participants will learn foundational knowledge on AI security, covering essential concepts and common threat vectors. The course explores structured methodologies like STRIDE for identifying and categorizing threats, and applies the 4-question framework to guide students in systematically assessing and mitigating risks in AI/ML systems. Additionally, the course features a hands-on demonstration with IriusRisk, showcasing how to effectively use this tool for automated threat modeling, mapping threats, and identifying countermeasures tailored to AI/ML applications.

Recommended Audience:
AI/ML Engineers and Data Scientists: Professionals seeking to understand and implement security practices in their AI projects.
Security Engineers and Analysts: Those focusing on AI/ML system vulnerabilities and aiming to protect against AI-specific threats.
Cybersecurity and IT Managers: Decision-makers responsible for managing AI/ML security strategies.
Compliance and Risk Managers: Individuals ensuring that AI/ML systems adhere to regulatory standards and best practices.
Students and Enthusiasts: Learners interested in foundational knowledge of AI/ML security frameworks and tools.

Acknowledgments:

Claire Allen-Addy for her creation and narration of the videos.

Brandon Green for the creation and explanation of the use cases.

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