Securing AI Systems: Practical Responsible AI Security

As artificial intelligence becomes central to modern software, securing these systems against unique vulnerabilities is more critical than ever. Traditional security measures are no longer enough to protect complex machine learning models and large language models from sophisticated exploits. This text-based course guides you through the foundational concepts of AI security, helping you identify risks and implement robust defensive strategies. You will move from understanding basic vulnerabilities to applying modern responsible AI frameworks that keep systems safe, reliable, and compliant. What you'll learn: - Understand the core principles of responsible AI and the unique threat landscape of machine learning systems. - Identify common vulnerabilities including prompt injection, data poisoning, and model extraction attacks. - Apply defensive engineering techniques to sanitize inputs and secure model APIs. - Implement guardrails and alignment strategies to ensure predictable and safe model outputs. - Evaluate AI systems using modern security assessment frameworks and vulnerability checklists. The course begins with essential definitions and foundational AI security concepts before guiding you through practical threat modeling and modern defense-in-depth strategies. You will learn through clear written explanations, real-world scenario analyses, and conceptual security exercises. Designed for beginners, developers, and security enthusiasts, this course requires no prior background in machine learning or advanced cybersecurity. Start reading today to build a strong foundation in protecting the next generation of intelligent applications.