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2024 | Book

Foundations of Generative AI Read chapter Read first chapter

Generative AI Security

Theories and Practices

Editors: Ken Huang, Yang Wang, Ben Goertzel, Yale Li, Sean Wright, Jyoti Ponnapalli

Publisher: Springer Nature Switzerland

Book Series : Future of Business and Finance

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book explores the revolutionary intersection of Generative AI (GenAI) and cybersecurity. It presents a comprehensive guide that intertwines theories and practices, aiming to equip cybersecurity professionals, CISOs, AI researchers, developers, architects and college students with an understanding of GenAI’s profound impacts on cybersecurity.

The scope of the book ranges from the foundations of GenAI, including underlying principles, advanced architectures, and cutting-edge research, to specific aspects of GenAI security such as data security, model security, application-level security, and the emerging fields of LLMOps and DevSecOps. It explores AI regulations around the globe, ethical considerations, the threat landscape, and privacy preservation. Further, it assesses the transformative potential of GenAI in reshaping the cybersecurity landscape, the ethical implications of using advanced models, and the innovative strategies required to secure GenAI applications. Lastly, the book presents an in-depth analysis of the security challenges and potential solutions specific to GenAI, and a forward-looking view of how it can redefine cybersecurity practices. By addressing these topics, it provides answers to questions on how to secure GenAI applications, as well as vital support with understanding and navigating the complex and ever-evolving regulatory environments, and how to build a resilient GenAI security program.

The book offers actionable insights and hands-on resources for anyone engaged in the rapidly evolving world of GenAI and cybersecurity.

Table of Contents

Frontmatter

Part I

Frontmatter
Chapter 1. Foundations of Generative AI
Abstract
This chapter offers an introduction to the field of Generative AI (GenAI), providing critical foundational knowledge on neural networks, deep learning, advanced architectures, and recent innovations propelling this domain. It delineates GenAI as a branch of AI focused on creating novel, coherent content, distinguishing it from discriminative models. Tracing the origins of GenAI, the chapter elucidates the concepts of neural networks, unraveling their components like input layers, hidden layers, and output layers. Backpropagation, which facilitates training through gradient computation, is explained in detail. The chapter progresses to explore deep learning, attributed to increases in compute power and data availability. Techniques like convolutional and recurrent neural networks, which enable feature learning, are highlighted. Advanced architectures like transformers and diffusion models, based on attention mechanisms and reversed diffusion processes, respectively, are analyzed as cutting-edge innovations. The chapter concludes with promising new developments like Hinton’s Forward-Forward algorithm, Meta’s I-JEPA model, privacy-preserving federated learning, and integration of reasoning agents, painting an exciting outlook for the future. Overall, the chapter provides a layered knowledge base, spanning history, techniques, architectures, and innovations in GenAI. With its comprehensive yet accessible approach, it aims to equip readers with a holistic understanding of the foundations propelling GenAI.
Ken Huang, Yang Wang, Xiaochen Zhang
Chapter 2. Navigating the GenAI Security Landscape
Abstract
This chapter provides a high-level exploration of the security implications surrounding GenAI in the modern technological landscape. It begins with an examination of the rise of GenAI, emphasizing its innovative capacities while underscoring the novel security challenges and responsibilities that have emerged. The chapter discusses the new threat landscape of GenAI, including the need for diligent navigation, robust measures to protect against risks, ethical dimensions, and regulatory compliance. The role of governance, transparency, and the pressing need for a collaborative approach between technology and business teams is highlighted. Special attention is given to the roadmap for Chief Information Security Officers (CISOs) and business leaders, as well as an in-depth analysis of the impact on cybersecurity professionals. Serving as a foundational component, this chapter lays the groundwork for a comprehensive understanding of GenAI security topics, setting the stage for the more detailed discussions that follow in the subsequent chapters of this book.
Ken Huang, Jyoti Ponnapalli, Jeff Tantsura, Kevin T. Shin

Part II

Frontmatter
Chapter 3. AI Regulations
Abstract
This chapter provides an analysis of the regulatory landscape governing artificial intelligence on national and international levels. It emphasizes the growing need for global coordination in AI governance, drawing parallels with frameworks like the IAEA that enable constructive oversight of complex technologies. Through a comparative analysis, the chapter examines major regulatory initiatives, themes, tensions, and best practices taking shape across vital regions, including the European Union, China, the United States, the United Kingdom, Japan, India, Singapore, and Australia. Additionally, the pivotal role of international organizations like the OECD, World Economic Forum, and United Nations in developing harmonized principles and governance models for responsible AI is discussed. The chapter highlights how adaptable, balanced regulatory frameworks are crucial to promoting AI safety, ethics, and societal well-being while also fostering innovation. It sets the stage for further discourse on implementing AI governance to align with ethical norms and human values.
Ken Huang, Aditi Joshi, Sandy Dun, Nick Hamilton
Chapter 4. Build Your Security Program for GenAI
Abstract
This chapter explores policies, processes, and procedures to build a robust security program tailored for GenAI models and applications. It discusses key policy elements like goals, risk management, compliance, consequences, and priority areas focused on model integrity, data privacy, resilience to attacks, and regulatory adherence. The chapter also covers specialized processes for GenAI across risk management, development cycles, and access governance. Additionally, it provides details on security procedures for access control, operations, and data management in GenAI systems. Centralized, semi-centralized, and decentralized governance structures for GenAI security are also analyzed. Helpful framework resources including MITRE ATT&CK’s ATLAS Matrix, AI vulnerability databases, the Frontier Model Forum, Cloud Security Alliance initiatives, and OWASP’s Top 10 LLM Application risks are highlighted.
Ken Huang, John Yeoh, Sean Wright, Henry Wang
Chapter 5. GenAI Data Security
Abstract
This chapter provides an in-depth exploration of data security within the realm of GenAI. Highlighting the pivotal role of data, often likened to the “oil” of the digital age, the chapter navigates data’s lifecycle from collection to disposal. The narrative underscores the importance of secure collection, preprocessing, storage, and transmission. The chapter delves into data provenance, stressing the need to understand, verify, and validate data’s journey. Training data management is highlighted, with a focus on how training data can impact model performance, data diversity, and responsible disposal. Throughout, the chapter accentuates the significance of trust, transparency, and responsibility, offering insights into best practices in GenAI data security.
Ken Huang, Jerry Huang, Daniele Catteddu
Chapter 6. GenAI Model Security
Abstract
Safeguarding GenAI models against threats and aligning them with security requirements is imperative yet challenging. This chapter provides an overview of the security landscape for generative models. It begins by elucidating common vulnerabilities and attack vectors, including adversarial attacks, model inversion, backdoors, data extraction, and algorithmic bias. The practical implications of these threats are discussed, spanning domains like finance, healthcare, and content creation. The narrative then shifts to exploring mitigation strategies and innovative security paradigms. Differential privacy, blockchain-based provenance, quantum-resistant algorithms, and human-guided reinforcement learning are analyzed as potential techniques to harden generative models. Broader ethical concerns surrounding transparency, accountability, deepfakes, and model interpretability are also addressed. The chapter aims to establish a conceptual foundation encompassing both the technical and ethical dimensions of security for generative AI. It highlights open challenges and lays the groundwork for developing robust, trustworthy, and human-centric solutions. The multifaceted perspective spanning vulnerabilities, implications, and solutions is intended to further discourse on securing society’s growing reliance on generative models. Frontier model security is discussed using Anthropic proposed approach.
Ken Huang, Ben Goertzel, Daniel Wu, Anita Xie
Chapter 7. GenAI Application Level Security
Abstract
This chapter provides a comprehensive overview of security considerations, vulnerabilities, and controls at the application layer for GenAI systems. Analysis of the OWASP Top 10 for LLM applications gives the initial context of security concerns of GenAI Applications. Leading application design paradigms including RAG, ReAct, and agent-based systems are explored, along with their security implications. Major cloud-based AI services and associated security features are discussed. The Cloud Security Alliance’s Cloud Control Matrix is leveraged to evaluate application security controls relevant to GenAI. Examples grounded in banking connect security controls to real-world scenarios. Through multifaceted coverage of risks, design patterns, services, and control frameworks, the chapter equips readers with actionable insights on securing diverse GenAI applications by integrating security across the full application life cycle.
Ken Huang, Grace Huang, Adam Dawson, Daniel Wu

Part III

Frontmatter
Chapter 8. From LLMOps to DevSecOps for GenAI
Abstract
This chapter explores the emerging discipline of LLMOps, contrasting it with traditional MLOps approaches and highlighting unique considerations when operationalizing GenAI models and applications. A detailed examination of implementing LLMOps across the model lifecycle is provided, encompassing activities like base model selection, prompt engineering, model tuning, deployment, and monitoring. Recognizing security as a critical priority, strategies for integrating DevSecOps into LLMOps are outlined, establishing security as a shared responsibility across the development and operational lifecycle. The chapter offers conceptual foundations and practical guidance for successfully navigating the intricacies of LLMOps.
Ken Huang, Vishwas Manral, Wickey Wang
Chapter 9. Utilizing Prompt Engineering to Operationalize Cybersecurity
Abstract
This chapter provides a comprehensive guide to prompt engineering techniques for cybersecurity operations. Core concepts establish a foundation for constructing specialized prompts that tap the power of GenAI for threat analysis, incident response, and security enhancement. Specific methods including few shot learning, Retrieval Augmented Generation, Chain of Thought, Tree of Thought, ReAct, and automated reasoning are elucidated to improve model capabilities on complex cybersecurity tasks. However, prudent practices are emphasized to address risks around adversarial attacks, biases, and ethical breaches. The chapter aims to equip security professionals with prompt engineering proficiencies to leverage GenAI responsibly based on principles of accountability and transparency.
Ken Huang, Grace Huang, Yuyan Duan, Ju Hyun
Chapter 10. Use GenAI Tools to Boost Your Security Posture
Abstract
This chapter provides an analysis of emerging GenAI tools and techniques that are transforming cybersecurity and ethical AI capabilities. It explores tools with innovative solutions across application security, data privacy, threat detection, governance, observability, and bias detection. The chapter analyzes how natural language processing, neural networks, reinforcement learning, and other GenAI technologies are being applied in purpose-built platforms to boost security, optimize workflows, and uphold transparency. Focus areas include leveraging GenAI tools to strengthen resilience, improve security posture, and promote responsible AI development.
Ken Huang, Yale Li, Patricia Thaine
Metadata
Title
Generative AI Security
Editors
Ken Huang
Yang Wang
Ben Goertzel
Yale Li
Sean Wright
Jyoti Ponnapalli
Copyright Year
2024
Electronic ISBN
978-3-031-54252-7
Print ISBN
978-3-031-54251-0
DOI
https://doi.org/10.1007/978-3-031-54252-7

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