Skip to content

Commit f3eb68c

Browse files
Added introduction
This module introduces Microsoft Foundry's governance framework for AI projects, outlining its benefits and learning objectives.
1 parent bd69096 commit f3eb68c

1 file changed

Lines changed: 23 additions & 0 deletions

File tree

  • learn-pr/wwl-azure/run-governed-ai-workloads-microsoft-foundry/includes
Lines changed: 23 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,23 @@
1+
Your organization just approved 15 new AI projects across five business units. Each team wants to experiment with Azure OpenAI, deploy custom models, and spin up GPU clusters—all while your IT governance team scrambles to answer urgent questions: How do we prevent runaway costs? Who approved that production deployment without security review? Are we violating data residency requirements?
2+
3+
Microsoft Foundry addresses these challenges by providing a unified governance platform that enables rapid AI experimentation while maintaining enterprise controls. Instead of forcing teams to submit tickets and wait days for approvals, Foundry automates policy enforcement at the point of resource provisioning. Your developers get self-service access to preapproved AI services, while your governance team gains real-time visibility into compliance status and spending patterns across all AI workloads.
4+
5+
This module walks you through implementing Microsoft Foundry's governance framework in a realistic enterprise scenario. You configure policies that balance innovation velocity with security requirements, establish approval workflows that route high-risk requests to appropriate stakeholders, and build compliance dashboards that demonstrate regulatory adherence to auditors. By the end of this module, you have hands-on experience implementing the governance controls that Fortune 500 companies use to scale AI adoption responsibly.
6+
7+
## Learning objectives
8+
9+
By the end of this module, you're able to:
10+
11+
- Configure Microsoft Foundry governance policies for AI resource provisioning
12+
- Implement compliance monitoring and reporting for AI infrastructure
13+
- Establish secure access controls and identity management for AI workloads
14+
- Evaluate governance metrics and optimize policy effectiveness
15+
16+
## Prerequisites
17+
18+
Before starting this module, you should have:
19+
20+
- Familiarity with Azure Policy and Azure Resource Manager concepts
21+
- Basic understanding of identity and access management with Microsoft Entra ID
22+
23+
- Experience with cloud governance principles and compliance requirements

0 commit comments

Comments
 (0)