Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Faculty Publications- Book Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Manoj, R. Karthick; Senthilnathan, S.; Selvi, S. Arunmozhi; Kumar, T. Ananth; Balamurugan, S. Title A name given to the resource AI Trust, Risk, and Security Management: Framework, Principles, and Practices Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived AI Trust, Risk, and Security Management: Framework, Principles, and Practices;pp.1-377 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394393022" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394393022</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032868588?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032868588?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Manoj R.K., Department of Electrical and Electronics Engineering, Academy of Maritime Education and Training, Deemed to be University, Tamil Nadu, India; Senthilnathan S., Department of Electronics and Communication Engineering, School of Engineering and Technology, Christ University, Bangalore, India; Selvi S.A., Computer Science and Engineering, Holy Cross Engineering College, Tamil Nadu, India; Kumar T.A., Computer Science and Engineering, IFET College of Engineering (Autonomous), Tamil Nadu, India; Balamurugan S., iRCS, an Indian Technological Research &amp; Consulting Firm, Coimbatore, Tamil Nadu, Coimbatorex, India Description An account of the resource For industry practitioners, academic researchers, and governance professionals alike, this book offers both clarity and depth in one of the most important domains of modern technology. As AI matures, trust and risk management will define its success-and this book lays the groundwork for achieving that vision. As AI continues to permeate sectors ranging from healthcare to finance, ensuring that these systems are not only powerful but also accountable, transparent, and secure, is more critical than ever. This book offers a vital exploration into the intersection of trustworthiness, risk mitigation, and security governance in artificial intelligence systems, serving as a definitive guide for professionals, researchers, and policymakers striving to build, deploy, and manage AI responsibly in high-stakes environments. Using a comprehensive approach, it explores how to integrate technical safeguards, organizational practices, and regulatory alignment to manage the unique risks posed by AI, including algorithmic bias, data misuse, adversarial attacks, and opaque decision-making. The result is a strategic approach that not only identifies vulnerabilities, but also promotes resilient, auditable, and trustworthy AI ecosystems. At its core, AI TRiSM is a forward-looking concept that embraces the realities of AI in production environments. The framework moves beyond traditional static models of governance to propose dynamic, adaptive controls that evolve alongside AI systems. Through real-world case studies, the book outlines how tools like model cards, bias audits, and zero-trust architectures can be embedded into the AI development lifecycle. Readers will find the volume: Introduces concepts to stay ahead of regulations and build trustworthy AI systems that customers and stakeholders can rely on; Addresses security threats, bias, and compliance gaps to avoid costly AI failures; Explores proven frameworks and best practices to deploy AI responsibly and strategies to outperform; Provides comprehensive guidance through real-world case studies and contributions from industry and academia. Audience AI and machine learning engineers, data scientists, cybersecurity and risk management specialists, academics, researchers, and policymakers specializing in AI ethics, security, and risk management. 2026 Scrivener Publishing LLC. Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139439302-2; 978-139439299-5; Language A language of the resource English Type The nature or genre of the resource Book Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online