Securing AI Deployments with Model Armor

As generative AI and large language models become central to modern software, securing these systems against novel vulnerabilities is more critical than ever. Traditional security measures often fall short when dealing with unpredictable AI outputs and malicious user inputs. This text-based course guides you through the foundational concepts of AI safety, teaching you how to shield your deployments using Model Armor. You will gain the knowledge needed to identify common vulnerabilities and configure robust defense mechanisms to keep your applications safe and compliant. What you'll learn: - Understand core AI security concepts and the unique risks associated with large language models. - Identify common vulnerabilities such as prompt injection, jailbreaking, and sensitive data exposure. - Configure Model Armor templates to filter harmful inputs and secure system responses. - Implement content safety policies to detect and redact personally identifiable information. - Apply modern security patterns to protect AI-powered application interfaces. You will start by exploring fundamental AI security terminology and the threat landscape before moving on to step-by-step written guides on configuring and managing Model Armor. Through clear explanations and practical configuration examples, you will learn to integrate security layers seamlessly into your AI deployment workflow. Designed for beginners, developers, and cloud practitioners new to AI security, this course requires no prior experience with machine learning security tools. Start reading today to build safer, more resilient AI applications.