AI Security Fundamentals: Securing Machine Learning and LLMs

As artificial intelligence becomes central to modern software, securing machine learning models against emerging threats is more critical than ever. This text-only course introduces you to the essential principles of AI security, helping you protect intelligent systems from unique vulnerabilities. You will transition from a general tech enthusiast to a security-aware professional capable of identifying risks in machine learning pipelines. By reading real-world scenarios and analyzing vulnerability concepts, you will gain the foundational knowledge needed to safeguard AI deployments. What you'll learn: First, understand foundational AI security terminology and the machine learning lifecycle. Second, identify common vulnerabilities including data poisoning, model evasion, and extraction attacks. Third, learn modern LLM security risks such as prompt injection and insecure output handling. Fourth, explore defense-in-depth strategies to secure data pipelines and model training environments. Fifth, apply basic red teaming concepts and security testing methodologies to AI applications. Sixth, discover industry-standard frameworks and security guidelines for responsible AI deployment. The course starts with core security definitions before guiding you through threat modeling and mitigation strategies. You will read structured explanations and analyze practical security concepts step-by-step. This course is designed for beginners, developers, and security professionals new to AI, requiring no prior experience in machine learning security. Start reading today to build a strong foundation in modern AI security.