AI Safety, Security, and Governance for Generative AI on AWS

Building generative AI applications is easier than ever, but ensuring they are safe, secure, and unbiased remains a critical challenge for developers. This text-based course guides you through the essential principles of responsible AI, helping you protect your applications, data, and users from emerging risks. You will transition from writing basic AI prompts to designing secure, governed generative AI workflows. Through detailed written explanations and clear configuration examples, you will learn how to identify vulnerabilities, mitigate bias, and enforce safety guardrails using industry-standard AWS tools and modern security practices. What you'll learn: - Understand foundational AI safety, fairness, and governance principles before deploying models. - Configure robust guardrails in AWS Bedrock to block harmful content and protect sensitive user data. - Detect and mitigate bias in model outputs using SageMaker Clarify evaluation workflows. - Identify and defend against modern security threats, including prompt injection and data leakage. - Implement governance frameworks and compliance standards tailored for large language models. - Practice evaluating model toxicity and alignment using structured text guides and configuration snippets. The course begins with key terminology and foundational concepts of responsible AI, then moves systematically into practical implementation patterns using AWS Bedrock and SageMaker. This course is designed for software developers, cloud engineers, and technical professionals who are new to AI security and governance; no prior machine learning safety experience is required. Start reading today to build generative AI systems that are safe, ethical, and secure.