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As generative AI rapidly advances with the field of artificial intelligence, its presence poses significant ethical, security, and data management challenges. While this technology encourages innovation across various industries, ethical concerns regarding the potential misuse of AI-generated content for misinformation or manipulation may arise. The risks of AI-generated deepfakes and cyberattacks demand more research into effective security tactics. The supervision of datasets required to train generative AI models raises questions about privacy, consent, and responsible data management. As generative AI evolves, further research into the complex issues regarding its potential is required to safeguard ethical values and security of people’s data. Generative AI and Implications for Ethics, Security, and Data Management explores the implications of generative AI across various industries who may use the tool for improved organizational development. The security and data management benefits of generative AI are outlined, while examining the topic within the lens of ethical and social impacts. This book covers topics such as cybersecurity, digital technology, and cloud storage, and is a useful resource for computer engineers, IT professionals, technicians, sociologists, healthcare workers, researchers, scientists, and academicians.
The history of robotics and artificial intelligence in many ways is also the history of humanity’s attempts to control such technologies. From the Golem of Prague to the military robots of modernity, the debate continues as to what degree of independence such entities should have and how to make sure that they do not turn on us, its inventors. Numerous recent advancements in all aspects of research, development and deployment of intelligent systems are well publicized but safety and security issues related to AI are rarely addressed. This book is proposed to mitigate this fundamental problem. It is comprised of chapters from leading AI Safety researchers addressing different aspects of the AI control problem as it relates to the development of safe and secure artificial intelligence. The book is the first edited volume dedicated to addressing challenges of constructing safe and secure advanced machine intelligence. The chapters vary in length and technical content from broad interest opinion essays to highly formalized algorithmic approaches to specific problems. All chapters are self-contained and could be read in any order or skipped without a loss of comprehension.
In recent years, interest and progress in the area of artificial intelligence (AI) and machine learning (ML) have boomed, with new applications vigorously pursued across many sectors. At the same time, the computing and communications technologies on which we have come to rely present serious security concerns: cyberattacks have escalated in number, frequency, and impact, drawing increased attention to the vulnerabilities of cyber systems and the need to increase their security. In the face of this changing landscape, there is significant concern and interest among policymakers, security practitioners, technologists, researchers, and the public about the potential implications of AI and ML for cybersecurity. The National Academies of Sciences, Engineering, and Medicine convened a workshop on March 12-13, 2019 to discuss and explore these concerns. This publication summarizes the presentations and discussions from the workshop.
The recent rise of emerging networking technologies such as social networks, content centric networks, Internet of Things networks, etc, have attracted significant attention from academia as well as industry professionals looking to utilize these technologies for efficiency purposes. However, the allure of such networks and resultant storage of high volumes of data leads to increased security risks, including threats to information privacy. Artificial Intelligence and Security Challenges in Emerging Networks is an essential reference source that discusses applications of artificial intelligence, machine learning, and data mining, as well as other tools and strategies to protect networks against security threats and solve security and privacy problems. Featuring research on topics such as encryption, neural networks, and system verification, this book is ideally designed for ITC procurement managers, IT consultants, systems and network integrators, infrastructure service providers, computer and software engineers, startup companies, academicians, researchers, managers, and students.
This monograph reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI.
This book provides stepwise discussion, exhaustive literature review, detailed analysis and discussion, rigorous experimentation results (using several analytics tools), and an application-oriented approach that can be demonstrated with respect to data analytics using artificial intelligence to make systems stronger (i.e., impossible to breach). We can see many serious cyber breaches on Government databases or public profiles at online social networking in the recent decade. Today artificial intelligence or machine learning is redefining every aspect of cyber security. From improving organizations’ ability to anticipate and thwart breaches, protecting the proliferating number of threat surfaces with Zero Trust Security frameworks to making passwords obsolete, AI and machine learning are essential to securing the perimeters of any business. The book is useful for researchers, academics, industry players, data engineers, data scientists, governmental organizations, and non-governmental organizations.
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In an era where cyber threats are becoming increasingly sophisticated, "Implementing Generative AI in Cybersecurity: Techniques, Tools, and Case Studies" serves as a comprehensive guide for professionals and enthusiasts looking to leverage the power of generative AI to bolster their cybersecurity defenses. This book delves into the intersection of two rapidly evolving fields-artificial intelligence and cybersecurity-providing readers with the knowledge and tools necessary to stay ahead of cyber adversaries. The book begins with an introduction to generative AI and its pivotal role in transforming cybersecurity. It covers the basics of generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), explaining their mechanics and applications in creating synthetic data, enhancing training datasets, and anonymizing sensitive information. Moving into practical applications, the book explores how generative AI can be used for data augmentation and synthesis to improve the accuracy and robustness of machine learning models used in threat detection and incident response. Readers will learn about the latest techniques for detecting and defending against adversarial attacks, ensuring their AI systems remain resilient against sophisticated manipulations. A significant portion of the book is dedicated to real-world case studies, demonstrating how leading organizations in various sectors-finance, healthcare, and government-have successfully implemented generative AI solutions to enhance their cybersecurity posture. These case studies provide valuable insights into the practical challenges and strategies for integrating AI technologies into existing security frameworks. Deepfake detection and prevention, a crucial aspect of modern cybersecurity, is also covered in depth. The book outlines state-of-the-art detection techniques and countermeasures to combat the rising threat of synthetic media used for malicious purposes. The use of natural language processing (NLP) in security is another focal point, highlighting its applications in phishing detection, secure communication analysis, and threat intelligence. Ethical considerations, privacy concerns, and the regulatory landscape are discussed to provide a holistic view of the challenges and responsibilities involved in deploying AI-driven cybersecurity solutions. "Implementing Generative AI in Cybersecurity: Techniques, Tools, and Case Studies" is an essential resource for cybersecurity professionals, AI practitioners, and anyone interested in the future of digital security, offering practical guidance and actionable insights to navigate the complexities of integrating generative AI into cybersecurity strategies.