The specialization is designed as a structured learning journey, beginning with the foundational understanding of AI security threats and progressing to advanced defense mechanisms. The first course in the path introduces learners to adversarial attack vectors, such as poisoning, tampering, and supply chain risks, laying the groundwork for securing AI systems. The next stage delves into more complex threats, including evasion attacks, model theft, and generative AI vulnerabilities, equipping participants with techniques to protect deployed systems. The final course focuses on large language models and enterprise-grade AI security, covering advanced threat modeling, privacy-preserving techniques, and operational security frameworks. Through this progression, learners develop a comprehensive skill set in identifying, analyzing, and mitigating adversarial risks across the AI lifecycle.
This specialization is based on the course, Adversarial AI Attacks, Mitigations, and Defense Strategies, by John Sotiropoulos.
Applied Learning Project
Applied practice activities integrated throughout the courses provide structured opportunities for learners to apply key concepts and methods in realistic contexts. Through guided analysis, reflection, and skill application, participants engage with authentic challenges aligned to the subject matter and develop practical competence in solving domain-relevant problems.
