Securing AI Deployments with Model Armor

As generative AI applications become central to modern software, securing them against malicious inputs and data leaks is more critical than ever. Developers and security professionals must understand how to safeguard AI models from exploitation without degrading user experience. This text-based course guides you through protecting your AI workloads using Model Armor. You will gain a solid understanding of AI security risks and learn how to implement robust filtering policies to keep your deployments safe and compliant. What you'll learn: Understand foundational AI security concepts and common vulnerabilities like prompt injection; Configure Model Armor to filter toxic content, hate speech, and sensitive data; Implement data loss prevention strategies to stop accidental leakage of confidential information; Analyze model inputs and outputs to detect malicious user behavior; Integrate security policies seamlessly into your cloud-based AI workflows. The course begins with essential security definitions and the core architecture of Model Armor. From there, you will read through step-by-step implementation scenarios, exploring how to define safety thresholds and handle policy violations through clear, written explanations and configuration examples. This course is designed for beginner security engineers, cloud architects, and software developers new to AI safety, with no prior experience with Model Armor required. Start reading today to build safer, more resilient artificial intelligence systems.