Informationen zum Autor KARL OTS is Global Head of Cloud Security at EPAM Systems, an engineering and consulting firm. He leads a team of experts in delivering security and compliance solutions for cloud and AI deployments for Fortune 500 enterprises in a variety of industries. He has over 15 years' experience in tech and is a trusted advisor and thought leader. Karl is also a Microsoft Regional Director and Security MVP. Klappentext 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. Inhaltsverzeichnis Introduction xxiii Chapter 1 Overview of Generative Artificial Intelligence Security 1 Common Use Cases for Generative AI in the Enterprise 1 Generative Artificial Intelligence 1 Generative AI Use Cases 2 LLM Terminology 3 Sample Three-Tier Application 4 Presentation Tier 5 Application Tier 5 Data Tier 5 Generative AI Application Risks 5 Hallucinations 6 Malicious Usage 6 Shadow AI 7 Unfavorable Business Decisions 8 Established Risks 8 Shared AI Responsibility Model 8 Shared Responsibility Model for the Cloud 9 Shared Responsibility Model for AI 10 AI Usage 10 AI Application 10 AI Platform 11 Applying the Shared Responsibility Model 11 Regulation and Control Frameworks 12 Regulation in the United States 12 Regulation in the European Union 12 NIST AI Risk Management Framework 14 Govern 15 Map 15 Measure 16 Manage 16 Key Takeaways 16 References 17 Chapter 2 Security Controls for Azure OpenAI Service 19 On the Importance of Selecting Appropriate Security Controls 19 Risk Appetite 20 Comparing OpenAI Hosting Models 21 OpenAI ChatGPT 21 Privacy and Compliance 21 Identity and Access Management 21 Data Protection and Encryption 22 Audit Logging 22 Network Isolation 22 Data Residency 22 Azure OpenAI 22 Privacy and Compliance 23 Identity and Access Management 23 Data Protection and Encryption 23 Audit Logging 23 Network Isolation 23 Data Residency 23 Recommendation for Enterprise Usage 24 Evaluating Security Controls with MCSB 24 Control Domains 26 Network Security 27 Identity Management 28 Privileged Access 28 Data Protection 29 Asset Management 29 Logging and Threat Detection 29 Incident Response 30 Posture and Vulnerability Management 30 Endpoint Security 31 Backup and Recovery 31 DevOps Security 32 Governance and Strategy 32 Security Baselines 33 Applying Microsoft Cloud Secur...

Autorentext

KARL OTS is Global Head of Cloud Security at EPAM Systems, an engineering and consulting firm. He leads a team of experts in delivering security and compliance solutions for cloud and AI deployments for Fortune 500 enterprises in a variety of industries. He has over 15 years' experience in tech and is a trusted advisor and thought leader. Karl is also a Microsoft Regional Director and Security MVP.

Klappentext

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.

Inhalt

Introduction xxiii

Chapter 1 Overview of Generative Artificial Intelligence Security 1

Common Use Cases for Generative AI in the Enterprise 1

Generative Artificial Intelligence 1

Generative AI Use Cases 2

LLM Terminology 3

Sample Three-Tier Application 4

Presentation Tier 5

Application Tier 5

Data Tier 5

Generative AI Application Risks 5

Hallucinations 6

Malicious Usage 6

Shadow AI 7

Unfavorable Business Decisions 8

Established Risks 8

Shared AI Responsibility Model 8

Shared Responsibility Model for the Cloud 9

Shared Responsibility Model for AI 10

AI Usage 10

AI Application 10

AI Platform 11

Applying the Shared Responsibility Model 11

Regulation and Control Frameworks 12

Regulation in the United States 12

Regulation in the European Union 12

NIST AI Risk Management Framework 14

Govern 15

Map 15

Measure 16

Manage 16

Key Takeaways 16

References 17

Chapter 2 Security Controls for Azure OpenAI Service 19

On the Importance of Selecting Appropriate Security Controls 19

Risk Appetite 20

Comparing OpenAI Hosting Models 21

OpenAI ChatGPT 21

Privacy and Compliance 21

Identity and Access Management 21

Data Protection and Encryption 22

Audit Logging 22

Network Isolation 22

Data Residency 22

Azure OpenAI 22

Privacy and Compliance 23

Identity and Access Management 23

Data Protection and Encryption 23

Audit Logging 23

Network Isolation 23

Data Residency 23

Recommendation for Enterprise Usage 24

Evaluating Security Controls with MCSB 24

Control Domains 26

Network Security 27

Identity Management 28

Privileged Access 28

Data Protection 29

Asset Management 29

Logging and Threat Detection 29

Incident Response 30

Posture and Vulnerability Management 30

Endpoint Security 31

Backup and Recovery 31

DevOps Security 32

Governance and Strategy 32

Security Baselines 33

Applying Microsoft Cloud Security Baseline to Azure OpenAI 33

Security Profile 34

How to Approach the Security Baseline 34

Data Protection 35

Identity Management 36

Logging and Threat Detection 37

Network Security 38

Asset Management 38

Backup and Recovery 39

Endpoint Security 40

Posture and Vulnerability Management 40

Privileged Access 41

Selected Controls 42

Mapping the Selected Controls to CIS and NIST 44

Using Azure Policy to Secure Azure OpenAI at Scale 46

Azure Policy 46

Continuous Compliance Monitoring 47

Azure Policies for Azure OpenAI 48

Key Takeaways 49

References 49

Chapter 3 Implementing Azure OpenAI Security Controls 51

OWASP Top 10 for LLM Applications 51

Prompt Injection 52

Insecure Output Handling 52

Training Data Poisoning 53

Model Denial of Service 53

Supply Chain Vulnerabilities 53

Sensitive Information Disclosure 54

Insecure Plugin Design 54

Excessive Agency 54…