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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Tech Today
- Sprache: Englisch
Securely harness the full potential of OpenAI’s artificial intelligence tools in Azure
Securing Microsoft Azure OpenAI is an accessible guide to leveraging the comprehensive AI capabilities of Microsoft Azure while ensuring the utmost data security. This book introduces you to the collaborative powerhouse of Microsoft Azure and OpenAI, providing easy access to cutting-edge language models like GPT-4o, GPT-3.5-Turbo, and DALL-E. Designed for seamless integration, the Azure OpenAI Service revolutionizes applications from dynamic content generation to sophisticated natural language translation, all hosted securely within Microsoft Azure’s environment.
Securing Microsoft Azure OpenAI demonstrates responsible AI deployment, with a focus on identifying potential harm and implementing effective mitigation strategies. The book provides guidance on navigating risks and establishing best practices for securely and responsibly building applications using Azure OpenAI. By the end of this book, you’ll be equipped with the best practices for securely and responsibly harnessing the power of Azure OpenAI, making intelligent decisions that respect user privacy and maintain data integrity.
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Seitenzahl: 376
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Title Page
Introduction
CHAPTER 1: Overview of Generative Artificial Intelligence Security
Common Use Cases for Generative AI in the Enterprise
Shared AI Responsibility Model
Regulation and Control Frameworks
Key Takeaways
References
CHAPTER 2: Security Controls for Azure OpenAI Service
On the Importance of Selecting Appropriate Security Controls
Comparing OpenAI Hosting Models
Evaluating Security Controls with MCSB
Using Azure Policy to Secure Azure OpenAI at Scale
Key Takeaways
References
CHAPTER 3: Implementing Azure OpenAI Security Controls
OWASP Top 10 for LLM Applications
Access Control
Audit Logging
Network Isolation
Encryption at Rest
Content Filtering Controls
Key Takeaways
References
CHAPTER 4: Securing the Entire Application
The Three-Tier LLM Application in Azure
Retrieval-Augmented Generation
Azure Front Door
Azure App Service
API Management
Storage Account
Cosmos DB
Azure AI Search
Key Takeaways
References
CHAPTER 5: Moving to Production
LLM Application Security Lifecycle
AI Security Posture Management
LLM Application in Your Cloud Security Architecture
Key Takeaways
References
Index
Copyright
Dedication
About the Author
About the Technical Editor
Acknowledgments
End User License Agreement
List of Tables
Chapter 2
Table 2.1: Comparison of ChatGPT and Azure OAI Security Controls
Table 2.2: Microsoft Cloud Security Benchmark Control Details for NS-6
Table 2.3: Controls of the Network Security Domain of MCSB
Table 2.4: Controls of the Identity Management Domain of MCSB
Table 2.5: Controls of the Privileged Access Domain of MCSB
Table 2.6: Controls of the Data Protection Domain of MCSB
Table 2.7: Controls of the Asset Management Domain of MCSB
Table 2.8: Controls of the Logging and Threat Detection Domain of MCSB
Table 2.9: Controls of the Incident Response Domain of MCSB
Table 2.10: Controls of the Incident Response Domain of MCSB
Table 2.11: Controls of the Endpoint Security Domain of MCSB
Table 2.12: Controls of the Backup and Recovery Domain of MCSB
Table 2.13: Controls of the DevOps Security Domain of MCSB
Table 2.14: Controls of the Governance and Strategy Domain of MCSB
Table 2.15: Logging and Threat Detection Controls in the Azure OpenAI Securi...
Table 2.16: Identity Management Controls in the Azure OpenAI Security Baseli...
Table 2.17: Logging and Threat Detection Controls in the Azure OpenAI Securi...
Table 2.18: Network Security Controls in the Azure OpenAI Security Baseline...
Table 2.19: Asset Management Controls in the Azure OpenAI Security Baseline...
Table 2.20: Backup and Recovery Controls in the Azure OpenAI Security Baseli...
Table 2.21: Endpoint Security Controls in the Azure OpenAI Security Baseline...
Table 2.22: Posture and Vulnerability Management Controls in the Azure OpenA...
Table 2.23: Privileged Access Controls in the Azure OpenAI Security Baseline...
Table 2.24: Selected Security Controls from the Azure OpenAI Security Baseli...
Table 2.25: MCSB Controls for Azure OAI Mapped to CIS and NIST
Chapter 4
Table 4.1: Threats Related to the Sample Three-Tier Application
Table 4.2: Selected Security Controls from the Azure Front Door Security Bas...
Table 4.3: Selected Security Controls from the Azure App Service Security Ba...
Table 4.4: Selected Security Controls from the Azure API Management Security...
Table 4.5: Selected Security Controls from the Azure Storage Account Securit...
Table 4.6: Selected Security Controls from the Azure Cosmos DB Security Base...
Table 4.7: Selected Security Controls from the Azure AI Search Security Base...
Chapter 5
Table 5.1: Asset Management Control Domain of MCSB
Table 5.2: Incident Response Control Domain of MCSB
Table 5.3: Privileged Access Control Domain of MCSB
Table 5.4: Posture and Vulnerability Management Control Domain of MCSB
List of Illustrations
Chapter 1
Figure 1.1: A representative three-tier generative AI application
Figure 1.2: Shared responsibility model for cloud computing
Figure 1.3: Shared responsibility model for AI
Figure 1.4: Classification of AI risk in the EU AI Act
Figure 1.5: NIST AI RMF core
Chapter 2
Figure 2.1: Microsoft Cloud Security Benchmark in Defender for Cloud
Figure 2.2: Azure Policy evaluation flow
Figure 2.3: Azure Policy noncompliance evidence
Chapter 3
Figure 3.1: OWASP Top 10 for LLM applications
Figure 3.2: Activity log event details
Figure 3.3: Inbound network control
Figure 3.4: Outbound network controls
Figure 3.5: Generating an encryption key in Azure Key Vault
Figure 3.6: Creating a custom content filter
Chapter 4
Figure 4.1: Three-tier LLM application in Azure
Figure 4.2: Threat model of the three-tier sample application
Figure 4.3: Sample application with revised Azure services
Figure 4.4: Azure options for RAG
Figure 4.5: Revised application architecture with RAG
Figure 4.6: Configuring resource logs for Azure Front Door
Figure 4.7: Configuring the Front Door log scrubbing feature
Figure 4.8: Microsoft-managed rules of Front Door WAF
Figure 4.9: Creating a custom Front Door WAF rule
Figure 4.10: Enforcing the built-in authentication in App Service
Figure 4.11: Network isolation of Azure App Service
Figure 4.12: API Management access and network controls
Figure 4.13: Configuring allowed resource instances for Storage Account
Figure 4.14: Configuring encryption scopes for Storage Account encryption at...
Figure 4.15: Configuring periodic backups for Cosmos DB
Figure 4.16: Resource firewall of Azure AI Search
Figure 4.17: Configuring CMK encryption for AI Search index
Chapter 5
Figure 5.1: Security-scanned Meta Llama model in the AI Studio model catalog...
Figure 5.2: AI-generated image verified using Content Credentials
Figure 5.3: Cloud discovery
Figure 5.4: A sample graph from the executive report
Figure 5.5: Discovered apps
Figure 5.6: Viewing the details of a discovered application (ChatGPT)
Figure 5.7: Customizing the Defender for Cloud Apps risk score metrics
Figure 5.8: Manage application
Figure 5.9: Customizing an alert in Defender for Cloud Apps
Figure 5.10: Security recommendation details for Defender for Cloud AI workl...
Figure 5.11: Creating an exemption for a Defender recommendation
Figure 5.12: Security alert details page in Defender for Cloud
Figure 5.13: Supporting evidence for the alert
Figure 5.14: Take action on an alert
Figure 5.15: Inspecting of resource logs from the alert
Figure 5.16: Incident view
Figure 5.17: Prompt evidence setting
Figure 5.18: The subscription hierarchy of Microsoft Enterprise-Scale landin...
Figure 5.19: Our LLM application deployed to an Azure landing zone
Guide
Cover
Title Page
Copyright
Dedication
About the Author
About the Technical Editor
Acknowledgments
Introduction
Table of Contents
Begin Reading
Index
End User License Agreement
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Securing Microsoft Azure OpenAI
Karl Ots
Introduction
Even for an industry that never seems to sit still, the massive surge in generative AI adoption that followed the launch of ChatGPT in November 2022 felt breathtaking. Two months and 100 million users later, it had become the most popular piece of software ever used. The commercial success of this consumer product ushered in a new era of hopes and dreams for AI, which had been reduced to somewhat of a niche for decades.
Fast-forward to today. While some of these hopes and dreams have certainly come true, we have also learned the harsh truths of what it means to apply this new technology to practice. To get the most value out of these systems, we need to ground these models with our own data from our crown jewel data sources and apply at least all the security controls we would for our other cloud applications. While some may see this as disillusionment, I see this as maturity. Instead of talking in ifs, buts, and hencewiths, we are asking the crucial question: how do we secure generative AI applications?
This book is my personal attempt at answering the how of generative AI security, specifically in the context of Azure OpenAI. To write this book, I have drawn from my experience as a consultant working with many different companies across the world, all of them with a different set of requirements, capabilities, and digital maturity.
I hope you will take to heart the security methodologies and implementation details described in this book. We do not yet know whether all companies will become AI companies in the same way all companies are becoming software companies. But what is already certain is that if yours is on the way to doing so, you have taken a significant leap in securing that future by deciding to read this book.
