Securing AI Workloads in Azure: Microsoft's Integrated Approach to AI Security and Governance

As organizations accelerate AI adoption, securing AI workloads has become a top priority. Unlike traditional cloud applications, AI systems introduce new risks—such as prompt injection, data leakage, and model misuse—that require a more integrated approach to security and governance.

To help developers and security teams understand and address these challenges, Microsoft is hosting Azure Decoded: Kickstart AI Security with Microsoft Defender for Cloud, a live session on March 18th at 12 PM PST focused on securing AI workloads built with Microsoft Foundry and Azure AI services.

Understanding AI Security Fundamentals

A strong foundation for this session starts with the Microsoft Learn module: Understand how Microsoft Defender for Cloud supports AI security and governance in Azure. This training introduces how AI workloads are structured in Azure and why they require a different security model than traditional applications.

In the module, learners explore:

• The layers that make up AI workloads in Azure
• Security risks unique to AI, including prompt injection, data leakage, and model misuse
• How Microsoft Foundry provides guardrails and observability for AI models
• How Microsoft Defender for Cloud works with Microsoft Purview and Microsoft Entra ID to deliver a unified, defense-in-depth security and governance strategy for AI

Together, these services help organizations protect model inputs and outputs, maintain visibility, and enforce governance across AI workloads in Azure.

Real-World AI Security Architecture

The Azure Decoded session on March 18th builds on these concepts by connecting them to real-world architecture and platform decisions. Attendees learn how Microsoft Defender for Cloud fits into a broader AI security strategy and how Microsoft Foundry helps apply guardrails, visibility, and governance across AI workloads.

This session is designed for:

• Developers building AI applications and agents on Azure
• Security engineers responsible for protecting AI workloads
• Cloud architects designing enterprise-ready AI solutions

By combining conceptual understanding with platform-level security discussions, the session helps teams design AI solutions that are not only innovative—but also secure, governed, and trustworthy.

The Unique Challenges of AI Security

AI workloads present security challenges that traditional application security models weren't designed to handle. Prompt injection attacks can manipulate AI models into revealing sensitive information or performing unauthorized actions. Data leakage can occur when models inadvertently memorize and expose training data. Model misuse can happen when AI systems are used for purposes beyond their intended scope.

Microsoft's approach addresses these challenges through a multi-layered security strategy that combines:

• Microsoft Defender for Cloud: Provides threat detection and security posture management specifically for AI workloads
• Microsoft Foundry: Offers guardrails and observability for AI models
• Microsoft Purview: Enables data governance and compliance across AI systems
• Microsoft Entra ID: Provides identity and access management for AI services

This integrated approach ensures that security is built into the AI development lifecycle rather than added as an afterthought.

Getting Started with AI Security

AI security is evolving quickly, and it requires both architectural understanding and practical platform knowledge. Start by exploring how Microsoft Defender for Cloud supports AI security and governance in Azure, then join the Azure Decoded session to see how these principles come together in real-world AI workloads.

Register for the Azure Decoded session on March 18th at 12 PM PST to learn how to secure your AI workloads in Azure with Microsoft Defender for Cloud.

[IMAGE:1]

The post AI Security in Azure with Microsoft Defender for Cloud: Learn the How, Join the Session appeared first on Microsoft Security Community Blog.