Add introduction

Introduced a new module on AI governance with Microsoft Foundry, covering challenges, learning objectives, and prerequisites for effective AI infrastructure management.

Author: DivyaGundreddy

learn-pr/wwl-azure/explore-ai-governance-ai-ready-infrastructure/includes/1-introduction.md

@@ -0,0 +1,20 @@
+Your organization just launched three new AI projects: a customer service chatbot, a fraud detection system, and a product recommendation engine. Within two months, your finance team flags unexpected Azure OpenAI charges totaling $47,000—triple the approved budget. At the same time, your compliance officer discovers that two development teams deployed AI models in regions that violate your data residency requirements. Your security team adds another concern: several AI endpoints lack the required content filtering policies, creating potential liability exposure.
+
+This scenario reflects a common challenge as organizations scale AI initiatives. Innovation accelerates, but without governance controls, teams face cost overruns, compliance violations, and security gaps. Microsoft Foundry addresses these challenges by centralizing governance across your AI infrastructure, enabling you to define policies once and enforce them consistently across all AI workloads.
+
+In this module, you learn how Microsoft Foundry helps administrators and AI engineers implement governance controls that balance innovation with responsible AI practices. You configure policies that enforce tagging standards, restrict regional deployments, control costs through budget alerts, and establish responsible AI guardrails. By the end of this module, you understand how to create a governance framework that supports rapid AI experimentation while maintaining compliance, cost predictability, and security standards.
+
+## Learning objectives
+
+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
+
+## Prerequisites
+
+- Familiarity with Azure fundamentals and resource management concepts
+- Basic understanding of AI and machine learning workload characteristics
+- Experience navigating Azure portal or using Azure CLI for resource configuration