Securing AI Deployments with Model Armor

As generative AI applications become central to modern software, securing these models against malicious inputs and sensitive data leaks is a critical priority. Safeguarding your AI deployments requires robust, real-time filtering and security policies that block threats before they reach your models. This text-only course guides you through the foundational concepts of Model Armor, a powerful security service designed to protect large language models and generative AI applications. You will understand how to configure safety boundaries, detect prompt injections, and implement content filtering to ensure secure and compliant AI operations. What you will learn: Understand the fundamental security risks in AI deployments, including prompt injection and jailbreaking; Configure Model Armor templates to establish consistent security policies across your AI applications; Implement content filtering to block safety violations and harmful outputs; Apply data masking techniques to protect personally identifiable information from being exposed to models; Analyze security logs and threat detection telemetry to continuously improve your AI security posture. The course begins with essential AI security concepts and threat vectors before guiding you through the step-by-step configuration of Model Armor policies. You will read through practical scenarios and structured examples to learn how to monitor, update, and enforce these security templates in real-world environments. This course is designed for beginner security practitioners, software developers, and cloud enthusiasts looking to secure AI workloads, with no prior experience with Model Armor required. Start reading today to build safer, more resilient AI deployments.