What you'll learn

AI features are being connected to real product data, internal APIs, support workflows, and cloud services. That creates a new review challenge for security teams: the greatest risks often lie not in the model itself, but in how the application wraps the model, provides it with context, trusts its output, and enables it to call tools.. In this course, Securing AI Applications, you’ll gain the ability to analyze an AI application architecture and turn it into a practical security test plan. First, you’ll explore the core building blocks of LLM, RAG, and agent-based systems, including prompts, retrieval layers, vector stores, tools, and connectors. Next, you’ll trace user input and application data through retrieval, augmentation, tool calls, logging, and external dependencies. Finally, you’ll learn how to prioritize security tests by impact, with clear success criteria and scope boundaries. When you’re finished with this course, you’ll have the skills and knowledge of AI application security needed to review an AI-enabled product, explain where the main risks live, and produce an architecture-driven test plan that a security team can actually use.