Overview

As artificial intelligence (AI) becomes increasingly integrated into cybersecurity operations, organizations are leveraging AI-driven security tools to detect threats, automate responses, and strengthen resilience against evolving cyber risks. However, the growing complexity and autonomy of these systems raise important concerns related to transparency, fairness, accountability, and regulatory compliance. Explainable AI addresses these challenges by providing clear insights into how AI models generate decisions, enabling outcomes to be understood, validated, and audited. By incorporating explainability, ethical governance, risk management, and compliance principles into AI-powered security solutions, organizations can increase trust, improve decision-making, and align with legal and regulatory requirements. Developing transparent and responsible security frameworks is essential for balancing technological innovation with accountability while maintaining confidence in AI-enabled cybersecurity systems. Explainable and Responsible AI Security Tools for Trust, Accountability, and Compliance explores the integration of explainable and responsible AI principles within modern cybersecurity environments. This book examines the methods, frameworks, and real-world applications that enhance the interpretability, fairness, robustness, and governance of AI-based security solutions. Covering topics such as adversarial machine learning, federated learning, and visualization dashboards, this book is a valuable resource for engineers, cybersecurity professionals, academics, researchers, scientists, policymakers, and technology leaders seeking to advance trustworthy and responsible AI-driven security practices.

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Dr. Abu Sarwar Zamani is currently working as an Asst. Professor at Prince Sattam bin Abdulaziz University, Alkharj, Kingdom of Saudi Arabia and as an Adjunct Senior Fellow in Kulliyyah of Engineering at International Islamic University Malaysia, Gombak, Malaysia. Previously he worked as a Senior Lecturer in Shaqra University and King Saud University, Kingdom of Saudi Arabia. He received his Ph.D. in Computer Science from Pacific Academy of Higher Education and Research University, India and Master in Computer Science from Hamdard University, New Delhi, India in 2007. His research interests are, AI, ML, IoT, Health Informatics, Big Data, Cloud Computing and Data Mining. He has more than 15 years of experience in teaching, research, and industries. He has published 100+ research papers in many reputed journals in high indexed outlets. He has around three international patents from Australia and India. He has completed numerous research projects that were financed by Saudi Arabia's Ministry of Education's Deanship of Scientific Research. He is currently working for numerous reputable journals, such as Springer, Elsevier, and MDPI, as an Academic Editor, Associate Editor, and Guest Editor.

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