After reading this cheat sheet, you’ll be able to:

• Understand the differences between the public OpenAI API and Azure OpenAI Service—and why the Azure-native approach offers stronger enterprise controls.

• Identify the three key risk categories for Azure OpenAI deployments: service-level, cloud-level, and governance-related.

• Apply six proven security best practices, from securing API authentication to implementing AI-specific monitoring and logging.

• Leverage Azure’s built-in controls—like Customer Lockbox, managed identities, and content safety filters—alongside guardrails and governance frameworks.

• Build multi-layered defenses that address both generative AI–specific threats and broader cloud security gaps.

Is this cheat sheet for me?

This guide is for you if you:

• Deploy or plan to deploy Azure OpenAI models for production workloads.

• Manage AI security, governance, or compliance for your organization.

• Work in cloud architecture, DevSecOps, or platform engineering and need a concise yet actionable security reference.

• Want to meet compliance requirements without slowing down AI innovation.

Whether you’re a cloud security architect, AI engineer, compliance officer, or technical decision-maker, this cheat sheet will help you secure your Azure OpenAI workloads from end to end.

What's included?

Inside, you’ll find:

• A clear comparison of OpenAI API vs. Azure OpenAI Service, highlighting security, privacy, and customization differences.

• Risk breakdown across service-level, cloud-level, and governance layers.

• Six essential security practices covering API authentication, data encryption, RBAC, network isolation, responsible AI governance, and monitoring.

• Implementation tips with references to Microsoft and OWASP guidance.

• Azure-native and third-party tooling recommendations for continuous monitoring and AI-specific posture management.