AI Security and Governance on AWS: Guardrails and Compliance

As generative AI becomes central to modern business, securing these powerful models against emerging vulnerabilities is more critical than ever. Organizations must balance rapid innovation with strict data privacy, compliance, and risk management. This text-based course guides you through the foundational principles of securing AI workloads specifically on AWS, showing you how to design robust security architectures that protect sensitive data, prevent model manipulation, and maintain strict governance. What you'll learn: Understand foundational AI security concepts, risk domains, and common vulnerabilities for large language models; Configure AWS Bedrock guardrails to block harmful content and prevent prompt injection attacks; Implement data governance and privacy controls to ensure training and inference data remain secure; Design secure retrieval-augmented generation architectures with proper access controls; Establish monitoring and auditing pipelines to track AI model behavior and maintain compliance standards. The course starts with essential definitions and risk frameworks before moving into practical configuration guides for AWS security tools. You will work through written scenarios and conceptual exercises designed to solidify your understanding of secure cloud-based AI deployment. This course is designed for beginners in cloud security, developers, and cloud architects looking to specialize in AI governance. No prior experience with AI security is required. Start reading today to build safe, compliant, and resilient AI systems on AWS.