Security in the World of AI: Protecting Intelligent Systems

As artificial intelligence transforms modern industries, it also introduces entirely new security vulnerabilities that traditional cybersecurity frameworks are not fully equipped to handle. Understanding how to protect AI models and the data they rely on is an essential skill for anyone navigating today's technological landscape. This text-based course equips you with a foundational understanding of AI security, helping you recognize potential threats and implement defensive strategies. You will move from learning basic terminology to exploring real-world risk mitigation patterns. What you'll learn: - Understand core AI security concepts and the fundamental differences between traditional software security and securing machine learning pipelines. - Identify modern AI vulnerabilities, including prompt injection, model inversion, and data poisoning. - Analyze the risk landscape using current security frameworks designed for Large Language Models. - Apply zero-trust principles and secure data governance practices to AI system deployments. - Evaluate ethical implications and compliance standards surrounding data privacy in intelligent systems. The course begins with essential definitions and foundational security concepts before guiding you through practical threat modeling scenarios and defensive design patterns. You will read comprehensive explanations and analyze case studies to build your conceptual knowledge. Designed specifically for beginners, this course requires no prior background in cybersecurity or advanced machine learning. Start building your foundational knowledge of AI security today.