AI Security Essentials: Defending LLMs, MLOps, and Generative AI

As artificial intelligence and large language models become deeply integrated into modern software, securing these systems against novel vulnerabilities is more critical than ever. This course provides a clear, text-based pathway to understanding and mitigating security risks across the entire AI lifecycle. You will transition from a traditional security mindset to an AI-focused defense posture. Through written explanations, architectural breakdowns, and code-based security examples, you will learn how to identify vulnerabilities in data pipelines, secure machine learning infrastructure, and protect generative AI applications from real-world threats. What you'll learn: - Understand the foundational security risks unique to machine learning models and artificial intelligence pipelines. - Identify and mitigate generative AI vulnerabilities, including prompt injection, data poisoning, and model inversion. - Apply modern threat modeling frameworks specifically designed for LLMs and MLOps environments. - Configure secure data pipelines and access controls to prevent sensitive data exposure in Retrieval-Augmented Generation (RAG) systems. - Implement industry-standard security best practices, referencing guidelines like the OWASP Top 10 for LLMs. The course begins with core definitions and the unique threat landscape of artificial intelligence before guiding you through securing MLOps pipelines, hardening generative AI applications, and establishing robust threat modeling practices. This program is designed for software developers, security professionals, and technology enthusiasts looking to build a foundation in AI security, with no prior machine learning or data science experience required. Start reading today to build safer, more resilient AI-powered applications.