This course focuses on securing AI workloads and services in Azure using Microsoft's cloud-native security tools. Students will learn how to apply security posture management, workload protection, identity and access controls, and Microsoft Foundry guardrails to protect AI applications, data, and infrastructure while supporting the secure and responsible use of AI technologies.

This course is designed for:

• Cloud Engineers responsible for deploying and managing Azure environments.
• AI Engineers developing and supporting AI-powered solutions.
• Platform Engineers responsible for securing cloud platforms and workloads.
• Security Engineers who design, implement, and monitor security controls for AI services.
• IT professionals who need to understand how Microsoft Defender for Cloud and Microsoft Entra can be used to protect AI workloads and environments.

Before attending this course, learners should have:

• A basic understanding of cloud computing concepts.
• Familiarity with Microsoft Azure fundamentals and core Azure services.

To get the most value from this course, learners should also have:

• Experience working with Microsoft Defender for Cloud.
• Knowledge of identity and access management (IAM) concepts.
• An understanding of AI workloads and Azure AI services.
• Familiarity with Microsoft Entra ID and access control principles.
• Knowledge of cloud security concepts, including Cloud Security Posture Management (CSPM) and Cloud Workload Protection (CWP).

After completing this course, students will be able to:

• Assess and improve the security posture of AI workloads using Microsoft Defender for Cloud.
• Configure workload protection to detect and respond to threats in AI environments.
• Secure Microsoft Foundry projects using built-in guardrails, safety controls, and governance features.
• Implement identity and access controls for AI services using Microsoft Entra.
• Control and monitor access to AI resources using role-based permissions and least-privilege principles.
• Investigate and respond to AI-related security incidents using Microsoft Defender XDR.
• Protect AI data, models, and infrastructure with cloud-native security controls.
• Configure secure networking, secret management, and logging for AI environments.
• Apply security best practices to support the secure and responsible use of AI technologies.

Module 1: Protect Microsoft Foundry Solutions with Microsoft Defender for Cloud

AI Security and Governance with Microsoft Defender for Cloud

• Understand AI services in Azure
• Identify common AI security risks
• Explore AI guardrails and protection mechanisms
• Understand how Azure security and governance tools support AI workloads

Protect AI Workloads with Microsoft Defender for Cloud

• Enable the AI workloads plan
• Review insights in the Data & AI Security Dashboard
• Assess and improve AI security posture using Cloud Security Posture Management (CSPM)
• Detect runtime threats using Cloud Workload Protection (CWP)
• Investigate AI security alerts and prompt evidence using Microsoft Defender XDR

Configure and Manage Guardrails in Microsoft Foundry

• Understand guardrails and Microsoft Content Safety
• Explore safety controls in Microsoft Foundry
• Test built-in guardrails
• Create and manage blocklists
• Configure and apply guardrails to AI workloads
• Select and refine guardrails to meet security and compliance requirements

Secure Microsoft Foundry Environments

• Control access to Microsoft Foundry using Microsoft Entra ID
• Manage project-level permissions and access
• Secure secrets using Azure Key Vault (Preview)
• Isolate networks with Managed Virtual Network and Private Link
• Enable diagnostic logging and monitoring