Introduction
Your organization deployed its first Azure OpenAI models across three development teams. Within weeks, the security team discovered unauthorized access attempts, the compliance officer flagged data residency violations, and finance raised concerns about untracked resource consumption. This scenario plays out in enterprises worldwide as AI adoption accelerates faster than governance frameworks can adapt.
Microsoft Foundry addresses these challenges by providing integrated governance capabilities that enforce policies, manage access, and maintain compliance across AI infrastructure. Rather than bolting governance onto existing deployments, Foundry embeds controls at the platform level—ensuring every AI workload inherits organizational standards from day one. This approach reduces administrative overhead by 40% compared to manual governance workflows while closing security gaps that traditional perimeter-based controls miss.
In this module, you configure policy-driven governance to enforce resource standards, implement identity and access management strategies that protect sensitive AI operations, and establish monitoring workflows that support regulatory reporting requirements. By the end, you'll have deployed a comprehensive governance framework that scales across your AI infrastructure without blocking innovation.
Learning objectives
By the end of this module, you're able to:
- Configure policy-driven governance controls for AI infrastructure using Microsoft Foundry
- Implement identity and access management strategies for AI workloads
- Establish monitoring and compliance workflows for responsible AI operations
- Evaluate governance patterns that align with enterprise security requirements
Prerequisites
- Familiarity with Azure fundamentals and resource management
- Basic understanding of AI and machine learning concepts
- Experience with identity and access management in Azure