Building Secure LLM Applications: Agents, RAGs, and Prompt Injection Defense

As Large Language Models (LLMs) become integrated into core business processes, new and critical security vulnerabilities emerge, especially concerning user input handling and data privacy. This course provides the foundational knowledge and practical techniques required to identify, mitigate, and test against the most critical risks facing modern LLM applications. Upon completing this course, you will be able to design, build, and deploy LLM-powered systems (such as Agents and RAG applications) that operate safely and predictably, even when facing adversarial inputs. What you'll learn: * Understand the OWASP LLM Top 10 vulnerabilities and how to apply threat modeling to common LLM architectures like RAG. * Implement robust defenses against prompt injection, indirect injection, and sensitive data leakage in deployed systems. * Configure LLM agents and function calls to prevent tool abuse and enforce strict limits on resource usage and cycles. * Practice using data validation methods and structured output techniques (like JSON Schema) to guarantee predictable model responses. * Apply data redaction and sanitization techniques to protect sensitive information flowing through the LLM pipeline. * Integrate basic security testing (red teaming) and validation gates into continuous integration and deployment (CI/CD) workflows. The course begins by defining key LLM security concepts and the current threat landscape before moving into the practical implementation of defensive coding patterns and policy enforcement for agents and RAG systems. This course is designed for developers, data scientists, and security professionals new to securing LLM-powered applications. No prior experience with LLM security is required, only basic programming knowledge. Start building trustworthy and resilient AI systems today.