Call for Chapters: Explainable and Responsible AI Security Tools for Trust, Accountability, and Compliance

Editors

Abu Sarwar Zamani, Prince Sattam bin Abdulaziz University, Saudi Arabia
Aisha Hashim, International Islamic University Malaysia, Malaysia
Anwer Hilal, Prince Sattam bin Abdulaziz University, Saudi Arabia
Vishal Barot, Gujarat Technological University, India
Durgesh Nandan, SR University, India

Call for Chapters

Proposals Submission Deadline: June 14, 2026
 Full Chapters Due: August 16, 2026
 Submission Date: August 16, 2026

Introduction

This book, “Explainable and Responsible AI Security Tools for Trust, Accountability, and Compliance.,” will explore the integration of explainable artificial intelligence (XAI) and responsible AI principles within modern cybersecurity systems. As AI-driven security tools become increasingly complex, their decision-making processes often lack transparency, creating challenges in trust, accountability, and compliance. The book will provide a comprehensive examination of methods, frameworks, and real-world applications that enhance interpretability, fairness, robustness, and ethical governance in AI-based cybersecurity solutions. Topics will include explainable threat detection models, adversarial robustness, bias mitigation, human-in-the-loop systems, regulatory considerations, and privacy-preserving techniques. By combining theoretical foundations with applied research and case studies, the publication aims to bridge the gap between advanced AI techniques and practical cybersecurity implementation, ensuring that intelligent systems are not only effective but also transparent and trustworthy.

Objective

The book title: “Explainable and Responsible AI Security Tools for Trust, Accountability, and Compliance” aims to provide a unified and practical framework that integrates AI security, explainable AI (XAI), and responsible governance into a single, coherent discipline. Its primary objective is to design and promote AI-driven security systems that are not only robust against threats such as adversarial attacks and data poisoning but are also transparent, interpretable, and accountable to stakeholders. By aligning technical development with regulatory requirements such as GDPR and the EU AI Act, the book emphasizes a “compliance-by-design” approach that ensures legal and ethical considerations are embedded from the outset. It further seeks to enhance trust in AI systems by introducing measurable standards for transparency and reliability, alongside frameworks for auditing, traceability, and post-decision analysis. In advancing current research, the book addresses a critical gap by bridging traditionally separate domains cybersecurity, explainability, and ethical AI, while offering real-world case studies, implementation strategies, and evaluation metrics. Ultimately, it contributes to the field by enabling the development of AI security tools that are not only technically effective but also socially responsible, legally compliant, and widely trustworthy in high-stakes environments.

Target Audience

The target audience for this publication includes: • Academic researchers and scholars in cybersecurity, artificial intelligence, machine learning, and data science • Industry professionals and practitioners working in cybersecurity operations, AI development, and risk management • Policy makers and regulators interested in AI governance, compliance, and ethical standards • Graduate and postgraduate students studying AI, cybersecurity, and related disciplines • Technology consultants and solution architects designing secure and trustworthy AI-driven systems The book is particularly suited for readers seeking to understand not only how AI can enhance cybersecurity, but also how to ensure these systems are interpretable, ethical, and aligned with societal expectations.

Recommended Topics

• Foundations of Explainable and Responsible AI in Cybersecurity • Threat Landscape in AI-Driven Cybersecurity Systems • Explainable AI Techniques for Intrusion Detection Systems • Explainability in Malware and Ransomware Detection • Adversarial Machine Learning and Robust Explainability • Fairness, Bias, and Ethical Risks in AI-Based Cyber Defense • Privacy-Preserving and Secure AI Models for Cybersecurity • Human-Centered AI and Decision Support in Security Operations Centers (SOCs) • Governance, Compliance, and Regulatory Frameworks for Responsible AI • Explainable AI in Emerging Cybersecurity Domains • Future Directions: Toward Trustworthy, Autonomous, and Self-Explaining Cyber Defense Systems • Evaluation Metrics and Benchmarking for Explainable AI in Cybersecurity • Visualization and Interpretability Dashboards for Security Analysts • Integration of Explainable AI into Security Information and Event Management (SIEM) Systems • Federated and Distributed Learning for Secure and Explainable Cyber Defense • Case Studies and Real-World Deployments of Explainable AI in Cybersecurity

Submission Procedure

Researchers and practitioners are invited to submit on or before June 14, 2026, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 28, 2026 about the status of their proposals and sent chapter guidelines.Full chapters of a minimum of 10,000 words (word count includes references and related readings) are expected to be submitted by August 16, 2026, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Explainable and Responsible AI Security Tools for Trust, Accountability, and Compliance. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.

Publisher

This book is scheduled to be published by IGI Global Scientific Publishing, an international academic publisher of the "Information Science Reference", "Medical Information Science Reference", "Business Science Reference", and "Engineering Science Reference" imprints. IGI Global Scientific Publishing specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2027.

Indexing Information for Prospective Authors

IGI Global Scientific Publishing meets the criteria for inclusion in major indexing services such as Scopus; however, it is important to note that all indexing decisions are made independently by these services. IGI Global Scientific Publishing books are selectively indexed by the indexing organization after publication. Indexing cannot be guaranteed for any book prior to publication, and the indexing organization has complete control over the final selection and timeline.

Important Dates

June 14, 2026: Proposal Submission Deadline
June 28, 2026: Notification of Acceptance
August 16, 2026: Full Chapter Submission
September 20, 2026: Review Results Returned
October 18, 2026: Final Acceptance Notification
October 25, 2026: Final Chapter Submission

Inquiries

Abu Sarwar Zamani, Prince Sattam bin Abdulaziz University, a.zamani@psau.edu.sa
Aisha Hashim, International Islamic University Malaysia, aisha@iium.edu.my
Anwer Hilal, Prince Sattam bin Abdulaziz University, a.hilal@psau.edu.sa
Vishal Barot, Gujarat Technological University, vishal.barot@gtu.edu.in
Durgesh Nandan, SR University, dnandan@ieee.org
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