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- This course explains how to design secure AI platforms using Microsoft Foundry, applying centralized governance, managed identities, private networking, Azure OpenAI security controls, and container image protection to ensure compliant, production‑ready AI workloads across enterprise environments.
After completing this module, you will be able to:
- Configure Microsoft Foundry Hubs and Projects for secure AI development environments
- Implement Azure OpenAI Service and Cognitive Services with enterprise security controls
- Secure AI container images and deployments using Azure Container Registry
- Apply network isolation and identity governance to protect AI infrastructure
- This course explains how to design secure AI platforms using Microsoft Foundry, applying centralized governance, managed identities, private networking, Azure OpenAI security controls, and container image protection to ensure compliant, production‑ready AI workloads across enterprise environments.
After completing this module, you will be able to:
- Configure Azure AI Content Safety to detect harmful content in Azure OpenAI requests and responses
- Implement content filters and custom block lists to enforce organizational content policies
- Validate Azure OpenAI model outputs against security and compliance requirements
- Apply responsible AI governance patterns for production AI infrastructure
- This course teaches how to govern AI platforms using Microsoft Entra and Azure Machine Learning, covering security groups, Conditional Access, managed identities, enterprise application integration, and audit logging to continuously monitor, enforce, and improve identity‑centric security for AI workloads.
After completing this module, you will be able to:
- Configure Microsoft Entra security groups to organize AI team members and enforce least-privilege access
- Implement Conditional Access policies that protect Azure Machine Learning workspace access
- Integrate enterprise applications with Azure Machine Learning using service principals and managed identities
- Evaluate security posture and access patterns for AI infrastructure using Microsoft Entra audit logs
- This module equips you to configure Azure's foundational security controls for AI workloads. You'll start by configuring Microsoft Entra ID security principals that define *who* and *what* can access your AI resources—from data scientists needing interactive workspace access to managed identities enabling secure service-to-service communication.
By the end of this module, you are able to:
- Configure Microsoft Entra ID security principals for AI workload access control.
- Implement Azure governance scopes across subscriptions, resource groups, and AI resources.
- Apply Azure Policy as the primary governance mechanism for infrastructure compliance.
- Evaluate security controls for production AI infrastructure deployment.
