Fundamentals of AI Agent Security

As AI agents become a core part of modern software, securing them against new types of vulnerabilities is essential. Without proper defenses, language models can be manipulated to leak data or execute unintended commands. This course provides a foundational understanding of AI application security. You will explore how to identify common threats and apply practical defense mechanisms to keep your systems safe. By focusing on fundamental concepts, you will gain the confidence to build resilient AI workflows. What you'll learn: • Understand core AI terminology and how language models process instructions • Identify and prevent prompt injection and jailbreaking attempts • Implement robust guardrails to validate inputs and filter model outputs • Secure Retrieval-Augmented Generation (RAG) pipelines against malicious data • Apply sandboxing principles to isolate and control AI agent actions • Design secure architectures for modern language model applications. The curriculum starts with essential definitions and the basic mechanics of language models before guiding you through specific vulnerabilities and defense strategies. You will read through detailed explanations and explore written code patterns to understand how security practices are applied in real-world scenarios. Designed for developers and software engineers who are beginners in AI security, this course requires no prior cybersecurity or advanced machine learning experience. Take the first step toward building secure and reliable AI applications today.