Securing Generative AI: Foundations of LLM and Prompt Security

As organizations rapidly adopt generative AI, securing these intelligent systems has become a critical priority for developers and security professionals alike. Traditional security frameworks fall short when dealing with the unpredictable nature of Large Language Models (LLMs). This text-based course provides a clear, foundational pathway to understanding the unique security risks of generative AI and how to mitigate them. You will transition from a beginner to a practitioner capable of designing secure AI integrations and protecting applications from emerging threats. What you'll learn: - Understand foundational generative AI concepts and the unique security challenges they introduce; - Identify and mitigate common vulnerabilities, including prompt injection, data leakage, and model poisoning; - Apply OWASP Top 10 principles specifically tailored for LLM applications; - Secure data pipelines, vector databases, and retrieval-augmented generation (RAG) architectures; - Implement zero-trust security practices and robust input/output validation mechanisms. The course begins with core definitions and the fundamentals of generative AI security before moving into analysis of vulnerabilities and defensive design patterns. You will study practical scenarios and code-level defenses to build resilient AI-driven systems. Designed for software developers, security analysts, and IT professionals new to AI security, this course requires no prior AI experience. Start reading today to build secure, resilient, and trustworthy generative AI integrations.