AI Security Testing and Red Teaming for Beginners

As artificial intelligence and large language models (LLMs) become central to modern software, securing these systems against novel threats is more critical than ever. This course introduces you to the core principles of AI security testing, helping you understand how attackers exploit AI models and how to defend them. You will transition from a beginner to a practitioner capable of identifying vulnerabilities in AI-enabled applications. You will learn the methodologies of AI red teaming, explore the unique threat landscape of machine learning, and understand how to plan structured security assessments. What you will learn: Understand the fundamental categories of AI security testing and how they differ from traditional software security; Identify critical vulnerabilities in LLMs, including prompt injection, data poisoning, and sensitive data disclosure; Learn the OWASP Top 10 for LLMs to align your testing with industry-standard security frameworks; Plan and execute structured AI red teaming exercises tailored for language models and AI-integrated applications; Apply security mitigation strategies to protect retrieval-augmented generation (RAG) systems and model APIs. The course starts with essential terminology and foundational AI security concepts before moving into practical testing methodologies and step-by-step red teaming scenarios. Designed specifically for beginners, software developers, and security enthusiasts, this text-based guide requires no prior experience in AI security or machine learning. Start your journey into AI security testing today and learn how to protect the next generation of intelligent applications.