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SC-5009: Secure AI solutions in the cloud using Microsoft Defender for Cloud and Microsoft Entra
This intermediate course shows cloud security, platform and application teams how to secure AI workloads on Azure with Microsoft Defender for Cloud, Microsoft Foundry and Microsoft Entra. You’ll learn AI security governance, posture management, runtime protection and guardrails, along with private networking, logging, Key Vault and secure communications. It also covers identity security with RBAC, managed identities, Conditional Access and Identity Protection, so you can reduce risk, detect threats and govern AI deployments securely and compliantly.
Available in Instructor-Led or Virtual formats.
Duration
8 Hours | 1 Day
Modules
8 Modules
Course type
Microsoft
Course Overview
SC-5009 equips cloud security, platform, and application teams to secure AI workloads across Azure using Microsoft Defender for Cloud, Microsoft Foundry, and Microsoft Entra. The course covers AI security governance, posture management, runtime protection, guardrails, private networking, logging, Key Vault, and protected communications. It also develops identity security skills through RBAC, managed identities, Conditional Access, and Identity Protection, enabling teams to establish trust boundaries, reduce risk, detect threats, and govern secure AI deployments confidently and compliantly across secure cloud operations.
Before You Begin
- Basic knowledge of Azure services, resources, networking, and cloud concepts
- Understanding of Microsoft Entra ID, RBAC, managed identities, and authentication
- Familiarity with security posture, threat detection, logging, and private connectivity
Key outcomes
- Learn to enhance AI security through posture management, threat detection, governance, and workload protection capabilities.
- Understand how to implement filters, blocklists, validation controls, and policies for safer AI interactions.
- Learn to secure resource communication using private connectivity, diagnostics, and secret management practices.
- Develop skills to define permissions, roles, and secure access strategies for AI workloads.
- Learn to secure identities by managing access policies, detecting risks, and responding to identity-based threats.
Tools you’ll use
Microsoft Defender for Cloud
Microsoft Foundry
Microsoft Entra ID
Azure RBAC & Conditional Access
Microsoft Entra Identity Protection
Azure Key Vault and security logging tools
Skills you’ll gain
- Secure AI workloads in Azure
- Configure AI security controls and guardrails
- Manage identity, access, and Conditional Access
- Apply threat detection and risk management practices
What You’ll Learn
8 modules
1Module 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
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
2Module 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
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
3Module 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
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
4Module 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
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
5Module 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
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
6Module 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
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
7Module 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
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
8Module 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
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
Certification & Credentials
Certificate track
8 Hours | 1 Day
High-Quality Video Lessons
Digital courseware, e-books & audio access
Recorded sessions, accessible on tablet & phone
AI mentor support & module quizzes
Learning Resources
Videos & Engaging Visual Content
Podcasts & Audiobooks
Expert discussions and audio episodes covering key topics
E-Books
Downloadable guides
Module-Wise Quizzes
Interactive quizzes after each module to reinforce learning.
Additional Resources
Study materials, exam prep guides, sandbox labs, and community tutorials.
Frequently asked questions
Who should take this course?
This course is designed for cloud security, platform, and application teams working on securing AI workloads in Azure environments.
What are the prerequisites for this course?
Learners should have basic knowledge of Azure fundamentals, identity and access management, and cloud security concepts.
What security tools are covered in this course?
The course covers Microsoft Defender for Cloud, Microsoft Foundry, Microsoft Entra ID, Azure RBAC, Conditional Access, and Identity Protection.
What will I learn about securing AI workloads?
You will learn to configure AI security controls, guardrails, threat protection, identity management, and governance practices for Azure AI solutions.
How does this course help with AI security operations?
It helps teams establish secure AI deployments by managing risks, protecting identities, monitoring threats, and enforcing security policies across Azure.
| Duration | 8 Hours / 1 Day |
|---|---|
| Modules | 8 Modules |
| Course type | Microsoft |
| Category | Microsoft |
| Prerequisites | Basic knowledge of Azure services, resources, networking, and cloud concepts, Understanding of Microsoft Entra ID, RBAC, managed identities, and authentication, Familiarity with security posture, threat detection, logging, and private connectivity |
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