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- Learn how to govern AI workloads using Microsoft Foundry by enforcing policies, managing costs, ensuring compliance, and applying responsible AI guardrails through hands‑on governance scenarios and exercises.
By the end of this module, you're able to:
- Explain how Microsoft Foundry supports AI infrastructure governance and compliance requirements.
- Configure governance policies and controls for AI workloads using Microsoft Foundry.
- Implement monitoring and auditing strategies that track AI resource usage and cost.
- Evaluate responsible AI practices and establish guardrails for production deployments.
- Learn to govern AI workloads using Microsoft Foundry by enforcing policy‑driven controls, securing identity access, monitoring compliance, and managing model lifecycle, quotas, and costs through practical, hands‑on exercises.
After completing this module, you'll be 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.
- Implement enterprise AI governance with Microsoft Foundry by understanding the governance framework, configuring policy‑driven controls, enforcing quotas and safeguards, and applying hands‑on exercises to balance compliance, security, cost management, and innovation.
After completing this module, you’ll be able to:
- Configure secure AI infrastructure access by assigning least‑privilege RBAC roles and enabling managed identities.Â
- Deploy and optimize Cosmos DB for AI conversations using scalable partition keys and time‑to‑live (TTL) policies.Â
- Implement production‑ready AI infrastructure that meets security audit requirements and supports global‑scale deployments.
- Discover classify AI assets enforce Azure Policy guardrails track data lineage and implement hands‑on governance controls securing compliant auditable AI deployments across infrastructure pipelines and sensitive data workflows.
By the end of this module, you're able to:
- Configure Microsoft Purview to discover and classify AI infrastructure assets
- Implement Azure Policy guardrails for AI resource provisioning and management
- Establish data lineage tracking for AI training datasets and model outputs
- Monitor AI workload compliance using Microsoft Purview audit logs and reports
- Design access controls that protect AI models and sensitive training data
- Design and deploy governed AI infrastructure by understanding governance frameworks, enforcing policies and access controls, implementing responsible AI safeguards, and completing hands-on exercises to ensure compliant, secure AI workloads.
In this module, you learn to:
- Evaluate AI governance requirements and align them with Microsoft Foundry capabilities
- Configure Azure Policy and role-based access controls for AI workloads
- Implement content filtering and responsible AI safeguards using Azure AI services
- Establish monitoring and audit trails for AI operations using Azure Monitor and Microsoft Purview
- Apply governance best practices for model lifecycle management and data protection
