Model Armor Essentials: Securing AI and LLM Deployments

As generative AI and Large Language Models (LLMs) become central to modern software, securing these systems against novel vulnerabilities like prompt injection and data exfiltration is more critical than ever. This text-only course introduces you to the foundational concepts of AI security and guides you through protecting your deployments using Model Armor. You will transition from understanding basic AI risks to actively configuring security filters that safeguard your applications. By studying detailed explanations and structured configuration examples, you will gain the confidence to implement robust safety boundaries around your AI models. What you'll learn: Understand foundational AI security concepts, including the OWASP Top 10 risks for LLMs; Identify common vulnerabilities such as prompt injection, jailbreaking, and sensitive data leakage; Configure Model Armor templates to filter harmful inputs and outputs in real time; Apply content safety policies to detect and redact personally identifiable information (PII); Integrate Model Armor security layers into your existing AI application workflows. The course begins with essential security terminology and threat landscapes before moving into step-by-step written guides on configuring and testing safety policies. You will learn through practical scenarios and clear configuration examples designed for immediate application. This course is designed for beginner developers, security enthusiasts, and AI practitioners who want to learn the basics of AI deployment protection, with no prior AI security experience required. Start reading today to build safer, more resilient AI applications.