Generative AI Security: Theories and Practices (Future of Business and Finance) [2024 ed.] 3031542517, 9783031542510

Table of contents :
Foreword
Foreword
Preface
Acknowledgments
Contents
About the Editors
Part I: Foundation of GenAI and Its Security Landscape
Chapter 1: Foundations of Generative AI
Chapter 2: Navigating the GenAI Security Landscape
Chapter 1: Foundations of Generative AI
1.1 Introduction to GenAI
1.1.1 What Is GenAI?
Origin and Significance
Underlying Mechanisms
Applications and Real-World Impacts
Challenges Ahead
1.1.2 Evolution of GenAI over Time
1.2 Underlying Principles: Neural Networks and Deep Learning
1.2.1 Basics of Neural Networks
1.2.2 Deep Learning Explored
1.2.3 Training and Optimization in Deep Learning
Forward and Backward Propagation
Optimization and Regularization Techniques
1.3 Advanced Architectures: Transformers and Diffusion Models
1.3.1 Transformers Unveiled
Self-Attention Mechanism
Multi-Head Attention and Positional Encoding
Transformer Blocks and Stacking
Implications and Success Stories
1.3.2 Diffusion Models Demystified
Understanding the Diffusion Process
From Noise to Structure
Training Diffusion Models
Advantages and Applications
1.3.3 Comparing Transformers and Diffusion Models
1.4 Cutting-Edge Research and Innovations in AI
1.4.1 Forward-Forward (FF) Algorithm
1.4.2 Image-Based Joint-Embedding Predictive Architecture (I-JEPA)
1.4.3 Federated Learning and Privacy-Preserving AI
Privacy Considerations
Implications and Use Cases
1.4.4 Agent Use in GenAI
Understanding Agents in GenAI
Planning and Reasoning
Action and Execution
1.5 Summary of Chapter
1.6 Questions
References
Chapter 2: Navigating the GenAI Security Landscape
2.1 The Rise of GenAI in Business
2.1.1 GenAI Applications in Business
2.1.2 Competitive Advantage of GenAI
2.1.3 Ethical Considerations in GenAI Deployment
2.2 Emerging Security Challenges in GenAI
2.2.1 Evolving Threat Landscape
Observability Issues
Adversarial Attacks
Data Manipulation and Poisoning
Automated and Scalable Threats and Zero-Day Vulnerabilities
Entitlement Policy Issues
Security Tools Integration Issues
Emergence of Malicious GenAI Tools
Data Leak Due to Aggregation
Emerging Network Security Threats
2.2.2 Why these Threats Matter to Business Leaders
2.2.3 Business Risks Associated with GenAI Security
Reputational Damage
Legal Liabilities
Loss of Competitive Advantage
Strategic and Operational Risks
2.3 Roadmap for CISOs and Business Leaders
2.3.1 Security Leadership in the Age of GenAI
Steering Security Initiatives in the Age of GenAI
Setting Priorities in GenAI Security
Aligning Security with Business Objectives in the Context of GenAI
2.3.2 Building a Resilient GenAI Security Program
2.3.3 Collaboration, Communication, and Culture of Security
Collaboration in GenAI Security
Communication in GenAI Security
Culture of Security Awareness in GenAI
2.4 GenAI Impacts to Cybersecurity Professional
2.4.1 Impact of Rebuilding Applications with GenAI
2.4.2 Skill Evolution: Learning GenAI
2.4.3 Using GenAI as Cybersecurity Tools
2.4.4 Collaboration with Development Teams
2.4.5 Secure GenAI Operations
2.5 Summary
2.6 Questions
References
Part II: Securing Your GenAI Systems: Strategies and Best Practices
Chapter 3: AI Regulations
Chapter 4: Build Your Security Program for GenAI
Chapter 5: GenAI Data Security
Chapter 6: GenAI Model Security
Chapter 7: GenAI Application Level Security
Chapter 3: AI Regulations
3.1 The Need for Global Coordination like IAEA
3.1.1 Understanding IAEA
Functions and Impact of the IAEA
Application of the IAEA Model to AI
Potential Roles of an International AI Coordinating Body
Establishing Global Safety Standards for AI
Regulatory Functions and Compliance
Developing Consensus on Contentious AI Issues
Challenges in Establishing an AI International Body
The Need for International Coordination in AI
Exploring the Structure and Operations of a Global AI Body
3.1.2 The Necessity of Global AI Coordination
Addressing Global Disparities in AI
Mitigating the Misuse of AI
Establishing Global AI Standards
Keeping Pace with AI Technological Advancements
Addressing the Social Implications of AI
Challenges in Establishing an International AI Body
3.1.3 Challenges and Potential Strategies for Global AI Coordination
Tension Between National Sovereignty and GenAI Objectives
Role of Commercial Entities
Diverse Cultural and Ethical Norms
Enforcement and Compliance with AI Standards
Decoding National Intentions in AI Policies
Adapting to AI Progress
Regulatory Dilemma in the Global Arena: Foundation Models, Applications, and International Coordination
Proposal for a Global AI Safety Index
3.2 Regulatory Efforts by Different Countries
3.2.1 EU AI Act
3.2.2 China CAC’s AI Regulation
3.2.3 United States’ AI Regulatory Efforts
At the White House
In Congress
At Federal Agencies
Recommendations and Pitfalls
Analyzing the Gaps
3.2.4 United Kingdom’s AI Regulatory Efforts
3.2.5 Japan’s AI Regulatory Efforts
3.2.6 India’s AI Regulatory Efforts
3.2.7 Singapore’s AI Governance
3.2.8 Australia’s AI Regulation
3.3 Role of International Organizations
3.3.1 OECD AI Principles
3.3.2 World Economic Forum’s AI Governance
3.3.3 United Nations AI Initiatives
3.4 Summary
3.5 Questions
References
Chapter 4: Build Your Security Program for GenAI
4.1 Introduction
4.2 Developing GenAI Security Policies
4.2.1 Key Elements of GenAI Security Policy
4.2.2 Top 6 Items for GenAI Security Policy
4.3 GenAI Security Processes
4.3.1 Risk Management Processes for GenAI
Threat Modeling
Continuous Improvement
Incident Response
Patch Management
4.3.2 Development Processes for GenAI
Secure Development
Secure Configuration
Security Testing
Monitoring
4.3.3 Access Governance Processes for GenAI
Authentication
Access Control
Secure Communication
4.4 GenAI Security Procedures
4.4.1 Access Governance Procedures
Authentication Procedure for GenAI
Access Management Procedure for GenAI
Third-Party Security Procedure for GenAI
4.4.2 Operational Security Procedures
4.4.3 Data Management Procedures for GenAI
Data Acquisition Procedure for GenAI
Data Labeling Procedure for GenAI
Data Governance Procedure for GenAI
Data Operations Procedure for GenAI
4.5 Governance Structures for GenAI Security Program
4.5.1 Centralized GenAI Security Governance
4.5.2 Semi-Centralized GenAI Security Governance
4.5.3 Decentralized AI Security Governance
4.6 Helpful Resources for Your GenAI Security Program
4.6.1 MITRE ATT&CK’s ATLAS Matrix
Understanding the ATLAS Matrix
Applying the ATLAS Matrix
4.6.2 AI Vulnerability Database
Vulnerability Database (AVID)
NIST’s National Vulnerability Database (NVD)
OSV (https://osv.dev/)
4.6.3 Frontier Model by Google, Microsoft, OpenAI, and Anthropic
4.6.4 Cloud Security Alliance
4.6.5 OWASP
4.6.6 NIST
4.7 Summary of the Chapter
4.8 Questions
References
Chapter 5: GenAI Data Security
5.1 Securing Data Collection for GenAI
5.1.1 Importance of Secure Data Collection
5.1.2 Best Practices for Secure Data Collection
5.1.3 Privacy by Design
5.2 Data Preprocessing
5.2.1 Data Preprocessing
5.2.2 Data Cleaning
5.3 Data Storage
5.3.1 Encryption of Vector Database
5.3.2 Secure Processing Environments
5.3.3 Access Control
5.4 Data Transmission
5.4.1 Securing Network Communications
5.4.2 API Security for Data Transmission
5.5 Data Provenance
5.5.1 Recording Data Sources
5.5.2 Data Lineage Tracking
5.5.3 Data Provenance Auditability
5.6 Training Data Management
5.6.1 How Training Data Can Impact Model
5.6.2 Training Data Diversity
5.6.3 Responsible Data Disposal
5.6.4 Navigating GenAI Data Security Trilemma
5.6.5 Data-Centric AI
Key Principles of Data-Centric AI
Data-Centric AI and Training Data Management
5.7 Summary of Chapter
5.8 Questions
References
Chapter 6: GenAI Model Security
6.1 Fundamentals of Generative Model Threats
6.1.1 Model Inversion Attacks
Conceptual Understanding
The Mechanics of Model Inversion Attacks
Mitigation Techniques
6.1.2 Adversarial Attacks
Adversarial Samples and Their Impact on Generative Models
Mitigation Techniques
6.1.3 Prompt Suffix-Based Attacks
6.1.4 Distillation Attacks
Description of Distillation Attacks
Mitigation Techniques
6.1.5 Backdoor Attacks
Exploring Backdoor Attacks
Mitigation Techniques
6.1.6 Membership Inference Attacks
Understanding Membership Inference Attacks
Mitigation Techniques
6.1.7 Model Repudiation
Understanding Model Repudiation
Mitigation Techniques
6.1.8 Model Resource Exhaustion Attack
Understanding Model Resource Exhaustion Attacks
Mitigation Techniques
6.1.9 Hyperparameter Tampering
Understanding Hyperparameter Tampering
Mitigation Techniques
6.2 Ethical and Alignment Challenges
6.2.1 Model Alignment and Ethical Implications
6.2.2 Model Interpretability and Mechanistic Insights
6.2.3 Model Debiasing and Fairness
Identifying Biases and Their Consequences
Techniques and Methodologies for Model Debiasing
6.3 Advanced Security and Safety Solutions
6.3.1 Blockchain for Model Security
6.3.2 Quantum Threats and Defense
Understanding Quantum Threats to GenAI
Strategies for Safeguarding GenAI in Quantum Era
6.3.3 Reinforcement Learning with Human Feedback (RLHF)
Understanding RLHF
The Role of Proximal Policy Optimization (PPO)
RLHF’s Implications for Model Security
6.3.4 Reinforcement Learning from AI Feedback (RLAIF)
6.3.5 Machine Unlearning: The Right to Be Forgotten
6.3.6 Enhance Safety via Understandable Components
6.3.7 Kubernetes Security for GenAI Models
6.3.8 Case Study: Black Cloud Approach to GenAI Privacy and Security
6.4 Frontier Model Security
6.5 Summary
6.6 Questions
References
Chapter 7: GenAI Application Level Security
7.1 OWASP Top 10 for LLM Applications
7.2 Retrieval Augmented Generation (RAG) GenAI Application and Security
7.2.1 Understanding the RAG Pattern
7.2.2 Developing GenAI Applications with RAG
7.2.3 Security Considerations in RAG
1. Avoid Embedding Personal Identifiable Information (PII) or Other Sensitive Data into Vector Database
2. Protect Vector Database with Access Control Due to Similarity Search
3. Protect Access to Large Language Model APIs
4. Always Validate Generated Data Before Sending Response to Client
7.3 Reasoning and Acting (ReAct) GenAI Application and Security
7.3.1 Mechanism of ReAct
7.3.2 Applications of ReAct
7.3.3 Security Considerations
7.4 Agent-Based GenAI Applications and Security
7.4.1 How LAM Works
7.4.2 LAMs and GenAI: Impact on Security
7.5 LLM Gateway or LLM Shield for GenAI Applications
7.5.1 What Is LLM Shield and What Is Private AI?
7.5.2 Security Functionality and Comparison
7.5.3 Deployment and Future Exploration of LLM or GenAI Application Gateways
7.6 Top Cloud AI Service and Security
7.6.1 Azure OpenAI Service
Types of Data Processed by Azure OpenAI Service
Processing of Data within Azure OpenAI Service
Measures to Prevent Abuse and Harmful Content Generation
Exemption from Abuse Monitoring and Human Review
Verification of Data Storage for Abuse Monitoring
7.6.2 Google Vertix AI Service
Trusted Tester Program Opt Out
Reporting Abuse
Safety Filters and Attributes in GenAI
Vertex AI PaLM API Safety Features
Ethical Considerations and Limitations
Recommended Practices for Security and Safety
7.6.3 Amazon BedRock AI Service
Simplified Experience with Serverless Technology
Comprehensive Use Cases
Diverse Selection of Foundation Models
Fully Managed Agents
Comprehensive Data Protection and Privacy
Security for Amazon Bedrock
Support for Governance and Auditability
7.7 Cloud Security Alliance Cloud Control Matrix and GenAI Application Security
7.7.1 What Is CCM and AIS
7.7.2 AIS Controls: What They Are and Their Application to GenAI
Review of AIS Controls
AIS Control and Applicability for GenAI
7.7.3 AIS Controls and Their Concrete Application to GenAI in Banking
7.7.4 AIS Domain Implementation Guidelines for GenAI
7.7.5 Potential New Controls Needed for GenAI
7.8 Summary
7.9 Questions
References
Part III: Operationalizing GenAI Security: LLMOps, Prompts, and Tools
Chapter 8: From LLMOps to DevSecOps for GenAI
Chapter 9: Utilizing Prompt Engineering to Operationalize Cybersecurity
Chapter 10: Use GenAI Tools to Boost Your Security Posture
Chapter 8: From LLMOps to DevSecOps for GenAI
8.1 What Is LLMOps
8.1.1 Key LLMOps Tasks
8.1.2 MLOps Vs. LLMOps
8.2 Why LLMOps?
8.2.1 Complexity of LLM Development
8.2.2 Benefits of LLMOps
8.3 How to Do LLMOps?
8.3.1 Select a Base Model
Code Example for Loading a Base Model.
8.3.2 Prompt Engineering
8.3.3 Model Fine-tuning
8.3.4 Model Inference and Serving
8.3.5 Model Monitoring with Human Feedback
8.3.6 LLMOps Platforms
MLflow from Databrick
Dify.AI
Weights and Biases (W&B) Prompts
8.4 DevSecOps for GenAI
8.4.1 Security as a Shared Responsibility
8.4.2 Continuous Security
8.4.3 Shift to Left
8.4.4 Automated Security Testing
8.4.5 Adaptation and Learning
8.4.6 Security in CI/CD Pipeline
8.5 Summary
8.6 Questions
References
Chapter 9: Utilizing Prompt Engineering to Operationalize Cybersecurity
9.1 Introduction
9.1.1 What Is Prompt Engineering?
9.1.2 General Tips for Designing Prompts
Start Simple
The Instruction
Avoid Impreciseness
To Do or Not to Do
Prompt Elements in Cybersecurity
9.1.3 The Cybersecurity Context
9.2 Prompt Engineering Techniques
9.2.1 Zero Shot Prompting
9.2.2 Few Shot Prompting
Few Shot Example
Limitations of Few Shot Prompting
9.2.3 Chain of Thought Prompting
9.2.4 Self Consistency
Definition
How It Works
Application in Cybersecurity
9.2.5 Tree of Thought (ToT)
9.2.6 Retrieval-Augmented Generation (RAG) in Cybersecurity
9.2.7 Automatic Reasoning and Tool Use (ART)
9.2.8 Automatic Prompt Engineer
9.2.9 ReAct Prompting
9.3 Prompt Engineering: Risks and Misuses
9.3.1 Adversarial Prompting
9.3.2 Factuality
9.3.3 Biases
9.4 Summary of Chapter
9.5 Questions
References
Chapter 10: Use GenAI Tools to Boost Your Security Posture
10.1 Application Security and Vulnerability Analysis
10.1.1 BurpGPT
10.1.2 CheckMarx
10.1.3 Github Advanced Security
10.2 Data Privacy and LLM Security
10.2.1 Lakera Guard
10.2.2 AIShield.GuArdIan
10.2.3 MLFlow’s AI Gateway
10.2.4 NeMo Guardrails
10.2.5 Skyflow LLM Privacy Vault
10.2.6 PrivateGPT
10.3 Threat Detection and Response
10.3.1 Microsoft Security Copilot
10.3.2 Duet AI by Google Cloud
10.3.3 Cisco Security Cloud
10.3.4 ThreatGPT by Airgap Networks
10.3.5 SentinelOne’s AI Platform
10.4 GenAI Governance and Compliance
10.4.1 Titaniam Gen AI Governance Platform
10.4.2 CopyLeaks.Com GenAI Governance
10.5 Observability and DevOps GenAI Tools
10.5.1 Whylabs.ai
10.5.2 Arize.com
10.5.3 Kubiya.ai
10.6 AI Bias Detection and Fairness
10.6.1 Pymetrics: Audit AI
10.6.2 Google: What If Tool
10.6.3 IBM: AI Fairness 360 Open-Source Toolkit
10.6.4 Accenture: Teach and Test AI Framework
10.7 Summary
10.8 Questions
References