Foundations of AI Security

As artificial intelligence becomes more integrated into our daily lives, securing these systems from unique and sophisticated threats is more critical than ever. This course provides a clear, foundational understanding of how to protect machine learning models from attack. You will move from core concepts to practical defense principles, equipping you to think critically about the risks in AI applications and how to approach mitigating them. By the end, you'll have the essential knowledge to discuss and contribute to building more secure and trustworthy AI. What you'll learn: - Understand the core principles of AI security and the modern threat landscape for machine learning systems. - Learn to identify common vulnerabilities like data poisoning, model evasion, and prompt injection attacks. - Apply fundamental strategies for building more robust and secure AI models from the ground up. - Explore the principles of Responsible AI, including fairness, bias mitigation, and model transparency. - Grasp the security considerations across the entire machine learning development lifecycle. - Practice analyzing potential attack vectors on different types of AI models through written exercises. The course begins with essential terminology and the fundamentals of how AI systems can be attacked, then progresses to cover key defensive techniques and best practices for securing the AI development pipeline. This course is designed for complete beginners. No prior experience in cybersecurity or artificial intelligence is required to get started. Start reading today to build your foundational knowledge in the critical field of AI security.