SC-5009: Secure AI solutions in the cloud using Microsoft Defender for Cloud and Microsoft Entra

Course structure

Module 1: Understand how Microsoft Defender for Cloud supports AI security and governance in Azure

• 1.1 Introduction 
• 1.2 Understand AI services in Azure 
• 1.3 Understand AI security risks in Azure 
• 1.4 AI guardrails and protections in Azure 
• 1.5 How Azure security and governance tools support AI workloads 
• 1.6 Summary 

Module 2: Protect AI workloads with Microsoft Defender for Cloud

• 2.1 Introduction 
• 2.2 Enable the AI workloads plan 
• 2.3 Review insights in the Data & AI security dashboard 
• 2.4 Assess and improve AI security posture with Cloud Security Posture Management (CSPM) 
• 2.5 Detect AI threats at runtime with Cloud Workload Protection (CWP) 
• 2.6 Investigate AI security alerts with prompt evidence in Microsoft Defender XDR 
• 2.7 Summary 

Module 3: Configure and manage guardrails in Microsoft Foundry

• 3.1 Introduction 
• 3.2 Understand guardrails and Microsoft Content Safety 
• 3.3 Understand safety controls in Microsoft Foundry 
• 3.4 Try out built-in guardrails 
• 3.5 Create and manage blocklists in Microsoft Foundry 
• 3.6 Configure and apply guardrails in Microsoft Foundry 
• 3.7 Choose and refine the right guardrails for your AI workloads 
• 3.8 Summary 

Module 4: Secure Microsoft Foundry environments

• 4.1 Introduction 
• 4.2 Control access to Microsoft Foundry with Microsoft Entra ID 
• 4.3 Manage access within Microsoft Foundry projects 
• 4.4 Secure Microsoft Foundry secrets with Azure Key Vault (preview) 
• 4.5 Isolate networks with managed virtual network and Private Link 
• 4.6 Enable diagnostic logging in Microsoft Foundry 
• 4.7 Summary 

Module 5: Understand identity architecture for AI workloads

• 5.1 Introduction 
• 5.2 Identity as the control layer for AI solutions 
• 5.3 Management plane and data plane access in AI workloads 
• 5.4 Authentication flows for AI endpoints in Microsoft Foundry 
• 5.5 Human and workload identities in AI workloads 
• 5.6 Role assignments and scope in AI environments 
• 5.7 Common identity misconfigurations in AI deployments 
• 5.8 Summary 

Module 6: Implement access management for Azure resources : Implement access management for Azure resources

• 6.1 Introduction 
• 6.2 Assign Azure roles 
• 6.3 Configure custom Azure roles 
• 6.4 Create and configure managed identities 
• 6.5 Access Azure resources with managed identities 
• 6.6 Analyze Azure role permissions 
• 6.7 Configure Azure Key Vault RBAC policies 
• 6.8 Retrieve objects from Azure Key Vault 
• 6.9 Summary and resources 

Module 7: Plan, implement, and administer Conditional Access

• 7.1 Introduction 
• 7.2 Plan security defaults 
• 7.3 Exercise – Work with security defaults 
• 7.4 Plan Conditional Access policies 
• 7.5 Implement Conditional Access policy controls and assignments 
• 7.6 Exercise – Implement Conditional Access policies roles and assignments 
• 7.7 Test and troubleshoot Conditional Access policies 
• 7.8 Implement application controls 
• 7.9 Implement session management and continuous access evaluation 
• 7.10 Exercise – Configure authentication session controls 
• 7.11 Microsoft Entra Conditional Access Optimization agent 
• 7.12 Summary and resources 

Module 8: Manage Microsoft Entra Identity Protection

• 8.1 Introduction 
• 8.2 Review identity protection basics 
• 8.3 Implement and manage user risk policy 
• 8.4 Exercise enable sign-in risk policy 
• 8.5 Exercise configure Microsoft Entra multifactor authentication registration policy 
• 8.6 Monitor, investigate, and remediate elevated risky users 
• 8.7 Implement security for workload identities 
• 8.8 Explore Microsoft Defender for Identity 
• 8.9 Explore the Identity Risk Management Agent 
• 8.10 Summary and resources