AI-driven Cyber Risk Management
by Hooman Razavi;Muriel Figueredo Franco;Mariya Ouaissa;Mariyam Ouaissa;Gautam Srivastava;
English | 2026 | ISBN: 8743808069 | 540 pages | True PDF EPUB | 84.9 MB
This book provides a comprehensive exploration of how artificial intelligence (AI) is revolutionizing cyber risk management, offering advanced methodologies to identify, assess, quantify, and mitigate threats effectively. Through the integration of AI, big data analytics, and emerging technologies, this book presents cutting-edge approaches to addressing cyber risks. Readers will gain insights into how AI enhances threat detection, fraud prevention, risk quantification, and incident response, equipping businesses to anticipate, measure, and mitigate cyber disruptions with greater precision. In todays digital landscape, businesses increasingly depend on complex cyber systems including cloud computing and data infrastructure, enterprise systems, financial and payment, and other operational systems, which make them susceptible to a wide range of risks beyond conventional security threats. Downtimes, system failures, data breaches, and cyber infrastructure outages can cause significant operational, reputational, and financial disruptions. To navigate these challenges, organizations must move beyond traditional risk management approaches and adopt AI-driven solutions for more proactive and intelligent cyber risk management. Structured around key aspects of AI-driven cyber risk management, the book begins with foundational principles before delving into the rise of AI in cyber risk management, the role of big data in risk analysis, and the application of AI in threat intelligence and fraud detection. Readers will explore AI-powered risk quantification models, automated mitigation strategies, and the integration of AI into cybersecurity infrastructure, business continuity planning, and disaster recovery. Industry-specific case studies offer real-world insights into AIs impact across different sectors, while discussions on emerging technologies, such as IoT, blockchain, large language models, advanced machine learning, and explainable AI, highlight the future trajectory of AI in cyber risk management. Aimed at professionals, researchers, and students, this book balances technical depth with practical clarity, making it an essential guide for those seeking to bridge the gap between AI innovation and real-world cyber risk applications. By addressing both the potential and challenges of AI-driven solutions, it presents a forward-thinking strategy for building resilient, AI-powered cyber risk management frameworks. AI-driven Cyber Risk Management explores cutting-edge AI applications in cybersecurity, covering threat detection, fraud prevention, and risk quantification. The book examines machine learning, big data analytics, XAI, and blockchain integration for resilient defense.
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