Offensive AI Security and AI Red Teaming Fundamentals

As artificial intelligence becomes deeply integrated into modern software, securing these systems against novel threats is more critical than ever. Traditional cybersecurity measures are no longer enough to protect complex machine learning models and large language models from targeted exploitation. This course equips you with the foundational knowledge to think like an attacker and systematically identify vulnerabilities in AI systems. Through clear, written explanations and practical code-based examples, you will understand how to evaluate AI models, test their defenses, and apply robust security guardrails to mitigate risks. What you'll learn: - Understand the fundamental terminology of offensive AI security and machine learning architectures. - Analyze common vulnerabilities including prompt injection, model evasion, and data poisoning. - Explore the OWASP Top 10 for Large Language Applications to identify critical weak points. - Apply basic red teaming methodologies to test model boundaries and safety guardrails. - Evaluate security risks inherent in modern Retrieval-Augmented Generation (RAG) systems. - Formulate foundational defense strategies to secure AI pipelines against adversarial attacks. The course begins with essential definitions and machine learning basics before guiding you through real-world attack vectors and hands-on defense concepts. You will progress from theoretical threat models to practical evaluation techniques using structured text lessons and clear conceptual breakdowns. 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 reading today to build the skills needed to secure the next generation of intelligent software.