Petar Radanliev – författare

UNLOCK THE FUTURE OF TECHNOLOGY WITH AI, BLOCKCHAIN, AND QUANTUM COMPUTING

 

Embark on a comprehensive journey that seamlessly intertwines the realms of Artificial Intelligence (AI), Blockchain, and Quantum Computing. This book goes beyond academic theory, offering readers a deep dive into the vast possibilities, inherent challenges, and profound implications of these groundbreaking technologies.

 

Blending rigorous research with practical insights from years of industry and academic experience in AI, Cybersecurity, and Quantum Computing, The Rise of AI Agents caters to professionals and technology enthusiasts alike. It demystifies complex concepts, providing a holistic perspective on these interconnected technologies and their potential to revolutionize industries.

 

Covering everything from the foundational aspects of AI, Blockchain, and Quantum Computing to their integration and future applications, this book delivers a forward-looking view rarely seen in existing literature. Real-world case studies; critical analyses of ethical, legal, and societal implications; and hands-on exercises enrich the reader’s understanding, making complex theories accessible and actionable.

 

Whether you’re a professional seeking deeper insights, a student exploring technological advancements, or an academic investigating the latest developments, this book covers the knowledge and tools you need to stay ahead in the rapidly evolving landscape of AI and beyond.

Use AI to Predict, Prevent, and Protect Against the Next Cyber Attack

From cybersecurity leader Omar Santos and AI expert Dr. Petar Radanliev comes a groundbreaking guide to the future of cyber defense. This book is a practical guide to building intelligent, AI-powered cyber defenses in today’s fast-evolving threat landscape. With cyber threats growing in speed, scale, and sophistication, traditional defenses can no longer keep up. This essential book shows how to use AI technologies to detect threats earlier, respond faster and build stronger digital resilience.

Designed for IT professionals, security analysts, engineers, executives, academics, and students, this guide bridges the gap between advanced AI technologies and real-world cybersecurity strategies. Whether you are managing enterprise networks, leading a security team, or preparing for a career in digital defense, this book will help you use AI to protect your most valuable assets.

As ransomware attacks, data breaches, and zero-day exploits rise, organizations must move from reactive defense to proactive resilience. This book explains how technologies such as generative AI, large language models (LLMs), and small language models (SLMs) enable real-time threat detection, automated incident response, and predictive threat analysis.

This book delivers:

Clear explanations of AI technologies such as large language models (LLMs), generative AI, and behavior-based analytics. Hands-on strategies for leveraging AI to boost detection, response, recovery, and resilience. Case studies and practical tools to help you apply cutting-edge defense methods in real-world environments.

By reading AI-Powered Digital Cyber Resilience, you will:

Understand the fundamentals of digital cyber resilience in an AI-driven world and why traditional security methods are no longer enough. Gain deep insight into generative AI, large language models (LLMs), small language models (SLMs), and how they are transforming cybersecurity. Apply AI-based techniques for real-time threat detection, anomaly detection, and predictive threat forecasting. Implement AI-driven incident response strategies, including automated orchestration and real-time decision-making. Secure IoT devices and cloud infrastructures using machine learning, behavioral analytics, and AI-powered access control. Use advanced encryption, data privacy tools, and compliance frameworks powered by intelligent automation. Build and enhance cybersecurity programs and policies with AI integration for better governance and risk management. Ensure secure and ethical AI deployments, including continuous model updates and protection against adversarial attacks.

Don’t wait for the next cyberattack. Take control with the knowledge and tools you need to build lasting digital resilience. AI-Powered Digital Cyber Resilience is your essential guide to navigating and mastering the future of cybersecurity.

Prepare for the future of cybersecurity--where quantum computing and AGI collide to reshape the rules of digital defense.

 

Quantum computing and Artificial General Intelligence (AGI) are poised to revolutionize the digital world--but they also introduce unprecedented security risks. These advancements have the potential to break modern encryption, manipulate digital identities, and bypass traditional cybersecurity defenses, leaving financial systems, government records, and private data dangerously exposed.

 

In Post-Quantum Security for AI, industry expert Petar Radanliev provides a comprehensive guide to safeguarding digital infrastructure against quantum and AI-driven cyber threats. This essential resource explores the vulnerabilities posed by quantum computing, the evolving risks of AGI, and the next-generation cryptographic solutions designed to counteract them. Readers will gain insight into quantum-resistant encryption, decentralized digital identity management, blockchain security strategies, and real-world implementation frameworks for enterprises and governments.

 

With a unique dual focus on AGI and quantum threats--areas often treated separately--this book delivers practical, forward-looking security strategies backed by case studies and expert analysis. Whether you're a cybersecurity professional, IT leader, policymaker, cryptographer, or developer, this book equips you with the critical knowledge and tools to navigate the future of digital security. The security landscape is changing fast—are you ready for it?

 

The security landscape is evolving--equip yourself now, before the future arrives unencrypted.

As artificial intelligence (AI) becomes more and more woven into our everyday lives—and underpins so much of the infrastructure we rely on—the ethical, security, and privacy implications require a critical approach that draws not simply on the programming and algorithmic foundations of the technology.

 

Bringing together legal studies, philosophy, cybersecurity, and academic literature, Beyond the Algorithm examines these complex issues with a comprehensive, easy-to-understand analysis and overview. The book explores the ethical challenges that professionals—and, increasingly, users—are encountering as AI becomes not just a promise of the future, but a powerful tool of the present.

 

· An overview of the history and development of AI, from the earliest pioneers in machine learning to current applications and how it might shape the future

· Introduction to AI models and implementations, as well as examples of emerging AI trends

· Examination of vulnerabilities, including insight into potential real-world threats, and best practices for ensuring a safe AI deployment

· Discussion of how to balance accountability, privacy, and ethics with regulatory and legislative concerns with advancing AI technology

· A critical perspective on regulatory obligations, and repercussions, of AI with copyright protection, patent rights, and other intellectual property dilemmas

· An academic resource and guide for the evolving technical and intellectual challenges of AI

· Leading figures in the field bring to life the ethical issues associated with AI through in-depth analysis and case studies in this comprehensive examination.

The author provides a rigorous, technically grounded framework for analysing, modelling, and mitigating adversarial threats against artificial intelligence systems. The book focuses on adversarial machine learning and AI-native cyber-attacks, examining how threat actors exploit vulnerabilities in data pipelines, model architectures, training procedures, and inference mechanisms to compromise the integrity, confidentiality, and availability of AI-driven systems.The significance of this book lies in addressing a structural gap in contemporary cybersecurity practice. Traditional security models were designed for deterministic software and networked systems, not for probabilistic, adaptive, and data-driven AI models. As AI increasingly underpins high-stakes decision-making across finance, healthcare, critical infrastructure, autonomous systems, and defence, adversarial manipulation of AI models has become an operational and strategic risk rather than a theoretical concern. This book responds directly to that risk by reframing cybersecurity through a model-centric, adversarial lens.The book is organised around the primary classes of AI cyber-attacks, each chapter analysing a major attack class that subsumes multiple concrete adversarial techniques. Collectively, these chapters cover the most dangerous and operationally relevant attack vectors observed in real-world AI deployments, including adversarial perturbations, data poisoning and backdoors, model extraction and inversion, membership inference, prompt injection and jailbreak attacks on large language models, AI-powered social engineering and deepfakes, federated learning and reinforcement learning attacks, and adversarial malware targeting AI-based security systems. Key features include lifecycle-based threat modelling, red-teaming methodologies, quantitative risk assessment frameworks, and technical countermeasures such as adversarial training, differential privacy, secure aggregation, cryptographic watermarking, and AI-specific governance controls.Readers will gain an operational understanding of how AI systems fail under adversarial pressure, how to simulate and test adversarial behaviours, and how to design resilient AI architectures suitable for deployment in high-risk environments. The book assumes prior familiarity with machine learning fundamentals and cybersecurity concepts and is aimed at advanced practitioners, researchers, and postgraduate audiences.