Responsible AI Security and Governance on AWS

As artificial intelligence becomes central to modern business, ensuring your models are secure, ethical, and compliant is more critical than ever. This text-based course introduces you to the core principles of building trustworthy AI systems on AWS. You will transition from understanding basic AI concepts to implementing robust security measures, risk management frameworks, and governance policies. Through structured reading and written scenarios, you will learn how to protect sensitive data, mitigate bias, and monitor AI deployments effectively. What you'll learn: Understand the foundational pillars of responsible AI, including fairness, explainability, and privacy; Configure AWS security controls to protect machine learning pipelines and model data; Apply governance frameworks to manage risk and maintain compliance with emerging AI regulations; Implement model monitoring and guardrails to detect drift, bias, and unauthorized access; Design secure data handling practices for training and deploying AI models. The course begins with essential terminology and ethical frameworks before guiding you through AWS-specific security configurations and compliance strategies. You will progress from theoretical concepts to practical, written case studies that simulate real-world governance decisions. This course is designed for beginners, cloud practitioners, and compliance professionals looking to understand AI security without needing a background in advanced machine learning. Start building a foundation in secure and responsible AI governance today.