AI Cybersecurity: Red Teaming LLMs and RAG Pipelines

As artificial intelligence becomes deeply integrated into software systems, securing these technologies has become a critical priority for security professionals. This course introduces you to the emerging domain of AI cybersecurity, focusing on how large language models (LLMs) and retrieval-augmented generation (RAG) systems can be exploited and defended. You will transition from understanding basic AI concepts to analyzing complex security vulnerabilities unique to machine learning pipelines. By studying real-world threat vectors, you will learn how to think like an attacker to proactively identify weaknesses and implement robust defensive strategies. What you will learn: Understand foundational AI security concepts and the unique vulnerabilities of machine learning models; Identify common LLM attack vectors, including prompt injection, data poisoning, and model inversion; Analyze RAG pipeline architectures to detect data leakage and unauthorized access points; Apply modern security frameworks like the OWASP Top 10 for LLM Applications to real-world scenarios; Practice designing mitigation strategies to secure AI applications against adversarial manipulation. The course begins with essential definitions and foundational AI concepts before guiding you through step-by-step written walkthroughs of exploitation techniques and defensive alignment patterns. You will solidify your knowledge through conceptual exercises and written security reviews. This course is designed for cybersecurity beginners, software developers, and IT professionals looking to enter the field of AI security, with no prior machine learning experience required. Start your path in AI red teaming today and learn how to secure the future of intelligent applications.