Security Fundamentals for Generative AI and LLMs

As generative AI and large language models become deeply integrated into modern software, securing these systems against novel threats is more critical than ever. This course provides a clear, foundational pathway to understanding the unique vulnerabilities that affect AI-driven applications. You will transition from a basic understanding of AI to confidently identifying, analyzing, and mitigating security risks in LLM implementations. Through written explanations and practical code-based security scenarios, you will learn how to design robust defenses for AI systems. What you'll learn: - Understand foundational generative AI concepts and the unique attack surface of large language models. - Identify critical vulnerabilities including prompt injection, training data poisoning, and insecure output handling. - Apply industry-standard security frameworks, such as the OWASP Top 10 for LLMs, to evaluate application risks. - Configure guardrails and input-filtering strategies to protect AI pipelines from malicious manipulation. - Implement data privacy and governance best practices to prevent sensitive data leakage in retrieval-augmented generation systems. The course begins with essential AI security terminology and core concepts before progressing to practical mitigation strategies and secure architectural patterns. You will work through written exercises and conceptual architecture reviews to reinforce your learning. This course is designed for cybersecurity beginners, software developers, and IT professionals looking to secure AI integrations, with no prior experience in AI security required. Start building safer, more resilient generative AI applications today.