Secure Azure Machine Learning Workspaces with Entra ID

As organizations rapidly deploy artificial intelligence, securing AI assets and training data becomes a critical priority. Protecting your machine learning pipelines starts with robust, identity-centric control rather than relying solely on network perimeters. This course guides you through the foundational concepts of identity-based security using Entra ID and Azure Machine Learning. You will transition from basic security setups to configuring advanced access controls, ensuring your models, data, and compute resources remain secure. What you'll learn: - Understand the fundamentals of identity security and zero-trust principles within AI environments. - Configure managed identities to secure communication between Azure Machine Learning and cloud storage. - Apply Entra ID security groups and roles to enforce least-privilege access. - Implement Conditional Access policies to protect sensitive machine learning workspaces. - Set up audit logging to monitor workspace activities and detect potential security anomalies. You will begin by exploring essential security terminology and the core architecture of Azure Machine Learning. From there, the text-only lessons guide you through step-by-step configuration scenarios, complete with practical policy examples and configuration snippets to read, analyze, and apply. This course is designed for beginners in cloud security, data scientists, and cloud administrators who want to build a solid foundation in securing AI workloads. No prior advanced security experience is required. Start learning today to build secure, compliant, and resilient machine learning environments.