Securing AI Solutions on AWS: Compliance and Governance

As artificial intelligence becomes central to modern business, securing AI models and protecting sensitive data has never been more critical. Organizations must navigate complex regulatory landscapes while deploying AI workloads safely in the cloud. This text-only course provides a comprehensive introduction to security, compliance, and governance for AI solutions on AWS. You will transition from understanding basic cloud security concepts to implementing practical governance frameworks that protect your data and align with global regulatory standards. What you'll learn: Understand foundational AI security terminology, including threat vectors specific to machine learning models; Configure secure and private data transfers within AWS using Virtual Private Clouds and private endpoints; Apply governance frameworks to track model lineage, manage access control, and ensure ethical AI usage; Align AI deployments with major regulatory compliance standards and data privacy laws; Implement zero-trust security architectures tailored for AI workloads and data pipelines; Practice identifying vulnerabilities in AI models through structured written scenarios and exercises. The course begins with core terminology and compliance fundamentals before guiding you through secure data ingestion, model protection, and ongoing monitoring strategies on AWS. Through clear written explanations and practical design patterns, you will learn how to build secure-by-design AI systems. This course is designed for cloud practitioners, security analysts, and developers who are new to AI security on AWS. No prior experience with AI governance is required, though a basic familiarity with cloud concepts is helpful. Start reading today to build trust and security into your cloud-based AI initiatives.