This course is designed for cybersecurity professionals, security engineers, AI engineers, application security practitioners, and developers responsible for designing, securing, or integrating AI-enabled applications. This course is ideal for learners who already understand the fundamentals of AI and LLM-powered systems and want to focus on implementing practical defensive controls to secure real-world AI applications, agentic systems, and Retrieval-Augmented Generation (RAG) pipelines.
Throughout the course, you will learn how to apply security engineering principles to AI systems by identifying threats, modeling AI-specific attack surfaces, and understanding why traditional security controls such as input validation are often insufficient in AI environments. The course covers practical defensive techniques for securing AI inputs through prompt hardening, input sanitization, filtering, context isolation, and trust boundary enforcement, while also addressing the risks associated with unvalidated AI outputs, including sensitive data leakage, unsafe responses, and insecure automation. You will also learn how to secure advanced AI application components, including tool integrations, function invocation workflows, vector databases, embeddings, and RAG pipelines. Additional topics include structured output enforcement, output filtering, Data Loss Prevention (DLP), secure tool access controls, human-in-the-loop approval workflows, and RAG source hardening. By the end of the course, you will be equipped with the practical knowledge needed to design, implement, and test defensive security controls for modern AI-enabled systems.
