Securing AI Deployments with Model Armor

As generative AI becomes central to modern software, securing these models against emerging vulnerabilities is more critical than ever. This text-based course introduces you to the fundamentals of protecting AI deployments from risks like prompt injection, data exfiltration, and toxic content. Through clear, written explanations and structured examples, you will learn how to configure and deploy robust guardrails for your AI applications. You will transition from understanding basic security concepts to implementing proactive defense strategies that keep your models safe and compliant. What you'll learn: - Understand the core security risks unique to large language models and generative AI. - Configure Model Armor to detect and mitigate prompt injection attacks. - Prevent sensitive data leakage by setting up content filtering and redaction policies. - Analyze safety settings to block toxic, hateful, or inappropriate model outputs. - Apply security templates to standardize safety measures across multiple AI endpoints. - Monitor and audit model interactions to maintain continuous compliance and threat visibility. The course begins with foundational concepts of AI security and risk vectors, guiding you step-by-step through the setup, configuration, and practical application of Model Armor guardrails. This course is designed for beginners, developers, and security enthusiasts looking to understand AI safety; no prior experience with AI security tools is required. Start reading today to build safer, more resilient AI applications.