Protect Microsoft Foundry solutions by using Microsoft Defender for Cloud

  • Learning Path
  • 4 Modules

At a glance

As AI workloads become central to business operations, they introduce new security challenges that traditional cloud tools don't fully address.

In this learning path, you learn how to:

  • Understand AI workload risks and how Microsoft Defender for Cloud identifies and protects AI assets
  • Enable the AI Workloads plan and use Cloud Security Posture Management (CSPM) to discover and remediate misconfigurations
  • Use Cloud Workload Protection (CWP) to detect runtime threats targeting AI components
  • Investigate AI security alerts in Microsoft Defender XDR
  • Configure and manage guardrails in Microsoft Foundry to prevent unsafe or policy-violating model behavior

Prerequisites

  • Experience managing Azure subscriptions, workloads, and Defender for Cloud plans
  • Familiarity with Microsoft Foundry and how AI workloads are deployed in Azure
  • Understanding of basic cloud security principles, including posture management, access control, and incident investigation

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Modules in this learning path

Understand how Microsoft Defender for Cloud supports AI security and governance in Azure

Microsoft Defender for Cloud plays a central role in securing AI workloads across Azure. Learn how Microsoft Defender for Cloud supports AI security across Azure. Explore the layers of an AI workload, the unique risks AI systems introduce, and the guardrails that protect model inputs and outputs. See how Microsoft Purview, Microsoft Entra ID, and Microsoft Foundry work together to support a unified security and governance strategy.

Protect AI workloads with Microsoft Defender for Cloud

Microsoft Defender for Cloud helps secure AI workloads by combining discovery, posture management, and runtime protection in one platform. You'll learn how to enable the AI workloads plan, review insights in the Data & AI security dashboard, assess posture using Cloud Security Posture Management (CSPM), detect runtime threats with Cloud Workload Protection (CWP), and investigate incidents in Microsoft Defender XDR. These capabilities work together to identify configuration gaps, detect suspicious behavior, and provide end-to-end visibility across your AI environments.

Configure and manage guardrails in Microsoft Foundry

Microsoft Foundry guardrails help secure AI workloads by applying configurable safety controls that evaluate both prompts and responses. You'll learn how to understand built-in safety models, test and refine guardrails, create blocklists, configure content filters, and validate that protections work as intended. These capabilities help organizations prevent unsafe or policy-violating interactions, protect sensitive data, and maintain trust in AI-assisted applications.

Secure Microsoft Foundry environments

To secure Microsoft Foundry environments requires layered protections that control access, safeguard credentials, isolate network communication, and maintain visibility across connected resources. The approach includes defining access boundaries with Microsoft Entra ID and project roles, and integrating Key Vault for secret management. It also uses managed virtual networks, Private Link, and diagnostic logging to maintain privacy, visibility, and compliance. These practices create secure, traceable AI environments that support collaboration without compromising protection.

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