Generative AI, Cybersecurity, and Ethics (eBook)

Mohammad Rubyet Islam (Autor)

eBook Download: EPUB

2024
574 Seiten
Wiley (Verlag)
978-1-394-27930-2 (ISBN)

Infos

'Generative AI, Cybersecurity, and Ethics' is an essential guide for students, providing clear explanations and practical insights into the integration of generative AI in cybersecurity. This book is a valuable resource for anyone looking to build a strong foundation in these interconnected fields.'
-Dr. Peter Sandborn, Professor, Department of Mechanical Engineering, University of Maryland, College Park

'Unchecked cyber-warfare made exponentially more disruptive by Generative AI is nightmare fuel for this and future generations. Dr. Islam plumbs the depth of Generative AI and ethics through the lens of a technology practitioner and recognized AI academician, energized by the moral conscience of an ethical man and a caring humanitarian. This book is a timely primer and required reading for all those concerned about accountability and establishing guardrails for the rapidly developing field of AI.'
-David Pere, (Retired Colonel, United States Marine Corps) CEO & President, Blue Force Cyber Inc.

Equips readers with the skills and insights necessary to succeed in the rapidly evolving landscape of Generative AI and cyber threats

Generative AI (GenAI) is driving unprecedented advances in threat detection, risk analysis, and response strategies. However, GenAI technologies such as ChatGPT and advanced deepfake creation also pose unique challenges. As GenAI continues to evolve, governments and private organizations around the world need to implement ethical and regulatory policies tailored to AI and cybersecurity.

Generative AI, Cybersecurity, and Ethics provides concise yet thorough insights into the dual role artificial intelligence plays in both enabling and safeguarding against cyber threats. Presented in an engaging and approachable style, this timely book explores critical aspects of the intersection of AI and cybersecurity while emphasizing responsible development and application. Reader-friendly chapters explain the principles, advancements, and challenges of specific domains within AI, such as machine learning (ML), deep learning (DL), generative AI, data privacy and protection, the need for ethical and responsible human oversight in AI systems, and more.

Incorporating numerous real-world examples and case studies that connect theoretical concepts with practical applications, Generative AI, Cybersecurity, and Ethics:

• Explains the various types of cybersecurity and describes how GenAI concepts are implemented to safeguard data and systems
• Highlights the ethical challenges encountered in cybersecurity and the importance of human intervention and judgment in GenAI
• Describes key aspects of human-centric AI design, including purpose limitation, impact assessment, societal and cultural sensitivity, and interdisciplinary research
• Covers the financial, legal, and regulatory implications of maintaining robust security measures
• Discusses the future trajectory of GenAI and emerging challenges such as data privacy, consent, and accountability

Blending theoretical explanations, practical illustrations, and industry perspectives, Generative AI, Cybersecurity, and Ethics is a must-read guide for professionals and policymakers, advanced undergraduate and graduate students, and AI enthusiasts interested in the subject.

Dr. Ray Islam (Mohammad Rubyet Islam) has served as a Lecturer in Cyber Security at ACES Honors College, University of Maryland, College Park, and as an Adjunct Professor in Natural Language Processing at George Mason University. He has held strategic and leadership roles in Generative AI and Cyber Security at esteemed firms like Deloitte, Lockheed Martin, Booz Allen, Raytheon, and the American Institutes for Research. He has published numerous journal articles and papers on Artificial Intelligence and Generative AI and has presented at prestigious conferences. He actively contributes to the academic community as a reviewer for the journal Reliability Engineering & System Safety and serves as Associate Editor for the Journal of Prognostics and Health Management.

Textauszug

1
Introduction

In this introductory chapter, we shall probe the pivotal themes in generative artificial intelligence (GenAI), cybersecurity, and ethics, laying the groundwork for an in-depth investigation of this captivating topic.

1.1 Artificial Intelligence (AI)

AI has emerged from the realm of science fiction to become a transformative force within the modern digital arena. Essentially, AI replicates human intelligence, equipping machines with the ability to learn, reason, self-correct, and even comprehend and generate human language. The field is predicated on the belief that human intelligence can be precisely delineated and duplicated by machines. This concept was propelled by Alan Turing’s seminal paper, which introduced the pressing question, “Can machines think?” and established the Turing test [1]. This test measures a machine’s capacity to display intelligent behavior that is indistinguishable from that of a human. During the test, a human evaluator interacts with both a machine and a human, unaware of which is which. If the evaluator cannot consistently differentiate the machine from the human based on their responses, the machine is considered to have passed the Turing test. This standard has become a critical benchmark in AI, highlighting the challenge of designing machines that can convincingly mimic human thought and conversation. AI encompasses multiple disciplines, including computer science, cognitive science, linguistics, psychology, and neuroscience, underscoring the complexity and vast scope of AI research. Various approaches to AI, such as the symbolic approach that focuses on logic and languages, and the connectionist approach that emphasizes learning from examples through artificial neural networks (ANNs), derive from these fields [2].

In 2016, AlphaGo, an AI by Google DeepMind, achieved the unimaginable by defeating Lee Sedol, a top Go player. This victory was monumental, as Go’s complexity far exceeds that of chess, testing AI’s strategic prowess and intuition. AlphaGo’s success highlighted significant advancements in deep learning and neural networks, demonstrating AI’s ability to learn and devise strategies, mirroring human intuition and propelling AI development into new territories.

AI systems are often categorized based on their capabilities and the breadth of their applications. These classifications encompass the following.

1.1.1 Narrow AI (Weak AI)

Specialized systems, devoid of consciousness or genuine comprehension, define much of today’s AI landscape. These systems are programmed for specific tasks, falling short of the expansive capabilities theorized for AI. Consider digital assistants such as Siri and Alexa, which adeptly set reminders, or the recommendation systems utilized by Netflix and Amazon, epitomizing Narrow AI [3]. Further manifestations include Spotify’s recommendation engines, which adeptly predict user preferences, self-driving cars dedicated solely to navigation, medical AI that proficiently identifies diseases from images, and industrial robots with narrowly defined functions. The realm of Narrow AI garners extensive exploration in AI literature and research.

1.1.2 General AI (Strong AI)

Artificial general intelligence (AGI), or General AI, represents an uncharted territory of captivating research. Unlike Narrow AI, which excels in particular tasks, AGI would usher in a revolution across diverse domains through its ability to learn and adapt in a manner akin to humans. In the medical field, for instance, AGI could sift through extensive datasets to deliver precise, personalized medical treatments. In the realm of creativity, it could autonomously generate original compositions in literature, music, and art. Characters such as Data from Star Trek embody the AGI ideal—adaptive, context-aware, and autonomous. The potential of AGI to reshape industries and daily life is immense; it could provide customized tutoring in education or optimize traffic management and safety in transportation. Researchers explore the promising advancements and the profound safety implications associated with AGI [3]. As we edge closer to realizing AGI, the prospects for a world where machines and humans collaborate seamlessly expand dramatically.

1.2 Machine Learning (ML)

ML thrives on the fascinating idea that machines can acquire knowledge and adapt through experience. Utilizing statistical methods, ML algorithms enable computers to learn from data, identify patterns, and make decisions with minimal human oversight [4]. This aspect of AI harbors tremendous potential. Essentially, ML is defined as the capacity of a computer program to continually improve its performance on a specific task through accumulated experience [5]. Mitchell’s definition provides a foundational understanding of ML: it emphasizes continuous, iterative enhancement rather than mere initial programming. For example, a spam filter progressively refines its ability to distinguish between “spam” and “nonspam” by analyzing various email contents and user responses, thereby increasing its indispensability in our digital ecosystem.

In 2019, researchers used machine learning to discover a previously unnoticed collision of two black holes recorded by LIGO in 2015. Traditional methods missed the subtle signal, but the algorithm detected it. This finding highlights machine learning’s power in astrophysics, proving it can uncover what humans can’t see and revolutionize scientific discoveries.

Bishop introduces the field of ML, an innovative discipline centered on designing algorithms capable of detecting concealed patterns in data and making precise predictions [6]. For instance, handwriting recognition technology evolves to match individual writing styles, demonstrating the practical utility of these algorithms. Similarly, Hastie et al. underscore the objective of ML: to construct models that accurately generalize from familiar to unfamiliar data [7]. In the financial industry, ML transforms credit scoring by analyzing historical data to forecast loan defaults, thereby revolutionizing the assessment of creditworthiness.

1.3 Deep Learning

Deep learning, inspired by the structure and function of the human brain, particularly ANNs, stands as a captivating subclass of ML. These algorithms autonomously learn complex data representations from images, videos, and text, eschewing rigid programming frameworks [8]. A landmark achievement in image recognition materialized during the 2012 ImageNet competition when Krizhevsky et al. unveiled AlexNet, a deep neural network that demonstrated unprecedented accuracy [9]. This milestone highlighted the profound potential of deep learning, spurring rapid progress in AI. The depth of deep learning, with its multiple interconnected layers mimicking neurons, allows it to grasp intricate data representations. The seminal insights of LeCun et al. in “Deep Learning” have significantly propelled the advancement of neural networks [8]. In computer vision, convolutional neural networks have achieved notable success, while natural language processing (NLP) has undergone a revolution with models like the Transformer, introduced by Vaswani et al. in “Attention is All You Need,” leading to innovations such as OpenAI’s GPT series [10]. Deep learning also revolutionizes autonomous vehicles by processing vast sensory data for real-time decision-making, with companies like Tesla and Waymo leveraging deep neural networks to boost vehicle agility and safety. Furthermore, DeepMind’s WaveNet has significantly enhanced the naturalness of synthesized speech [11].

In 2015, researchers introduced “Neural Style Transfer,” a deep learning algorithm that applies artistic styles from one image, like a famous painting, to another. For example, it can transform a photo to mimic Van Gogh’s “Starry Night.”

The true potency of deep learning emerges from its capacity to discern complex structures within vast datasets through the backpropagation algorithm, thereby equipping machines with the ability to adapt and refine their capabilities incessantly. This adaptability and scalability render deep learning models essential for tackling challenges that were once deemed insurmountable, firmly positioning them at the vanguard of AI research and applications.

1.4 Generative AI

Generative AI, or GenAI, represents a significant leap forward in AI, enabling machines to create new content—from text and images to music and code—by leveraging learned patterns and data. This technology utilizes sophisticated algorithms and neural networks to grasp and mimic the structure and nuances of various data types. For instance, in the realm of NLP, Generative Pretrained Transformer (GPT) models are capable of composing essays, crafting creative fiction, or even generating code, emulating human-like writing styles. Similarly, in the field of visual arts, models such as DALL-E can generate images from textual descriptions, artfully combining specified elements to forge novel artworks or design concepts.

In a striking demonstration of GenAI’s capabilities, an AI trained on Johann Sebastian Bach’s extensive works composed a new piece mirroring his unique style. This project involved feeding the AI with Bach’s compositions, allowing it to learn...

Produkt-Details

• Erscheint lt. Verlag: 25.11.2024
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Schlagworte: AI Cybersecurity • AI human-centric design • AI information warfare • AI Transparency • data protection • ethical AI • ethical algorithms • ethical cybernetics • Ethical Hacking • generative AI • responsible coding • Secure coding • trustworthy AI • zero trust AI
• ISBN-10: 1-394-27930-2 / 1394279302
• ISBN-13: 978-1-394-27930-2 / 9781394279302