शुरू करने से पहले आपको क्या जानना चाहिए
आप शुरू करें

शुरू होता है 4 June 2026 03:09

समाप्त होता है 4 June 2026

00 दिन
00 घंटे
00 मिनट
00 सेकंड
course image

Secure AI-ready infrastructure

Master secure AI infrastructure design with Microsoft Foundry, implementing governance, identity controls, network isolation, and content safety for enterprise-ready AI workloads.
Microsoft via Microsoft Learn

Microsoft

262 कोर्स


4 hours 14 minutes

वैकल्पिक अपग्रेड उपलब्ध है

Not Specified

अपनी गति से आगे बढ़ें

Free Online Course

वैकल्पिक अपग्रेड उपलब्ध है

अवलोकन

This course explains how to design secure AI platforms using Microsoft Foundry, applying centralized governance, managed identities, private networking, Azure OpenAI security controls, and container image protection to ensure compliant, production‑ready AI workloads across enterprise environments.After completing this module, you will be able to:

Configure Microsoft Foundry Hubs and Projects for secure AI development environments Implement Azure OpenAI Service and Cognitive Services with enterprise security controls Secure AI container images and deployments using Azure Container Registry Apply network isolation and identity governance to protect AI infrastructure This course explains how to design secure AI platforms using Microsoft Foundry, applying centralized governance, managed identities, private networking, Azure OpenAI security controls, and container image protection to ensure compliant, production‑ready AI workloads across enterprise environments.After completing this module, you will be able to:

Configure Azure AI Content Safety to detect harmful content in Azure OpenAI requests and responses Implement content filters and custom block lists to enforce organizational content policies Validate Azure OpenAI model outputs against security and compliance requirements Apply responsible AI governance patterns for production AI infrastructure This course teaches how to govern AI platforms using Microsoft Entra and Azure Machine Learning, covering security groups, Conditional Access, managed identities, enterprise application integration, and audit logging to continuously monitor, enforce, and improve identity‑centric security for AI workloads.After completing this module, you will be able to:

Configure Microsoft Entra security groups to organize AI team members and enforce least-privilege access Implement Conditional Access policies that protect Azure Machine Learning workspace access Integrate enterprise applications with Azure Machine Learning using service principals and managed identities Evaluate security posture and access patterns for AI infrastructure using Microsoft Entra audit logs This module equips you to configure Azure's foundational security controls for AI workloads. You'll 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.By the end of this module, you are able 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.

पाठ्यक्रम

  • Implement secure AI-ready infrastructure with Azure servicesIntroductionUnderstand Microsoft Foundry security architectureSecure Azure OpenAI and Cognitive ServicesSecure AI container images with Azure Container RegistryConfigure secure AI infrastructure in AzureModule assessmentSummary
  • Secure Azure OpenAI with content safety controlsIntroductionUnderstand Azure AI content safety architectureConfigure content filters and custom blocklistsDeploy content safety controls in AzureModule assessmentSummary
  • Implement identity-based security for Azure Machine Learning workspacesIntroductionConfigure Microsoft Entra security groups for AI teamsImplement Conditional Access policies for Azure Machine LearningIntegrate enterprise applications with Azure Machine LearningEvaluate security posture using Microsoft Entra audit logsConfigure secure access to Azure Machine LearningModule assessmentSummary
  • Implement security controls for Azure AI-ready infrastructureIntroduction: Secure infrastructure for AI workloadsConfigure Microsoft Entra ID security principalsImplement Azure governance scopes for AI resourcesApply Azure Policy as the primary governance mechanismExercise: Configure secure AI infrastructure in AzureModule assessmentSummary

विषय

Programming