SC-500T00: Implement End to End Security Controls for Cloud and AI Workloads Lab

This lab includes MS Learn content which is fully integrated in the account portal and lab environment.

This lab builds the practical skills a Cloud and AI Security Engineer needs to secure Azure platform services and the AI workloads that run on top of them. Learners work in live Azure and Microsoft 365 tenants through the Azure portal, the Microsoft Entra admin center, Microsoft Purview, Azure AI Foundry, Copilot Studio, Microsoft Defender for Cloud, Microsoft Sentinel, and Microsoft Security Copilot, with Cloud Shell, the Azure CLI, Bicep, KQL, and ARM template deployments used throughout. The labs progress across four themes: identity and governance controls including Privileged Identity Management, Conditional Access, Key Vault, Azure Policy and custom RBAC; platform data and network security across storage, Azure SQL Database, network security groups, Azure Firewall and private endpoints; AI specific security covering Purview data risk posture, Entra agent identities, Foundry guardrails and AI gateway controls, and Defender for AI detections; and cloud operations spanning containers, web and API platforms, posture management, Sentinel automation, and Security Copilot. Security is the whole subject of this course rather than one thread inside it, and every lab pairs a preventive configuration with a verification step, whether that is a What If simulation, a blocked request, an IP flow verify result, a KQL audit query, or a Defender finding review. Several labs are deliberately review oriented, because interpreting posture data and escalating findings to the right owner is presented as core engineering work alongside configuration.

Labs are available in English.