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This introduction outlines the importance of layered security controls for AI infrastructure, detailing governance, access control, and compliance.

Author: DivyaGundreddy

learn-pr/wwl-azure/implement-security-controls-azure-ai-ready-infrastructure/includes/1-introduction.md

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+Securing AI infrastructure requires more than traditional network firewalls. You need layered controls that span identity management, resource organization, and policy enforcement. Without proper governance, your organization risks data breaches when models access sensitive information, compliance violations when resources deploy to restricted regions, and operational chaos when teams lack clear ownership boundaries. A recent industry survey found that 68% of AI project delays stem from security review cycles—time lost because infrastructure controls weren't established before deployment.
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+This module equips you to configure Azure's foundational security controls for AI workloads. You 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. Next, you implement governance scopes that establish *where* policies apply, organizing resources across subscriptions and resource groups to separate development experimentation from production stability. Finally, you apply Azure Policy as your primary governance mechanism, enforcing organizational standards that *automatically* prevent noncompliant deployments.
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+By the end of this module, you'll have learned how 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