Introduction to AI Red Teaming: Securing Machine Learning Systems

As artificial intelligence and machine learning integrate into modern software, securing these systems against novel threats is more critical than ever. This text-based course introduces you to the core principles of AI red teaming, helping you understand how attackers exploit AI models and how to defend them. You will transition from understanding basic security concepts to proactively identifying vulnerabilities in large language models (LLMs) and machine learning pipelines. What you'll learn: - Learn the foundational terminology of AI security and the core differences between traditional red teaming and AI red teaming. - Understand common vulnerabilities in machine learning systems, including data poisoning, evasion attacks, and model inversion. - Practice identifying LLM-specific risks such as prompt injection, jailbreaking, and data exfiltration. - Explore modern security frameworks, including the OWASP Top 10 for Large Language Applications. - Apply threat modeling techniques to secure retrieval-augmented generation (RAG) architectures and AI-driven workflows. - Design basic defensive strategies to mitigate risks and improve model alignment. Starting with fundamental definitions and threat landscapes, the course guides you through written explanations and realistic scenarios, analyzing real-world attack vectors and defensive countermeasures. This program is designed for cybersecurity beginners, developers, and IT professionals looking to enter the field of AI security with no prior machine learning experience required. Start reading today to build your foundational skills in AI red teaming and secure the future of technology.