AI Trust, Risk, and Security Management

Framework, Principles, and Practices

Inbunden, Engelska, 2026

2 481 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2026-01-16
Mått:162 x 236 x 30 mm
Vikt:794 g
Format:Inbunden
Språk:Engelska
Antal sidor:416
Förlag:John Wiley & Sons Inc
ISBN:9781394392995

Utforska kategorier

Artificiell intelligens inom Data och IT

Mer om författaren

R. Karthick Manoj, PhD is an Assistant Professor at the Academy of Maritime Education and Training Tamil Nadu, India, with more than 14 years of experience. His scholarly contributions include six national and twelve international journal articles, four patents, three books, ten book chapters, and more than fifteen conference presentations. S. Senthilnathan, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering in the School of Engineering and Technology at Christ University, Bangalore, India. His research interests include quantum dot cellular automata and quantum computing. S. Arunmozhi Selvi, PhD is a Professor in the Holy Cross Engineering College, Anna University, Tamil Nadu, India with more than 15 years of research and teaching experience. She has published 30 articles in international journals and conference proceedings and written many book chapters. T. Ananth Kumar, PhD is an Associate Professor in the Department of and Computer Science and Engineering, IFET College of Engineering, Tamil Nadu, India. He has authored one book, edited six books and several book chapters, and presented papers in various national and international journals and conferences. S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting, Coimbatore India. He has published 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of experience researching various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies and startups.

Innehållsförteckning

Series Preface xixPreface xxiPart I: Fundamentals of Trustworthy and Transparent AI 11 Creating Trustworthy AI: A Lifecycle Risk Management Framework 3Satish Kumar S., Bharathi K., Vinod S., Rudhra S., Balaraman R. and Suresh A.2 Comprehensibility and Transparency of AI Systems with Applications 19N. Hemalatha, R. Elavarasi, P. Gajalakshmi, N. Magadevi and D. Kadhiravan3 Leveraging Correlation Analysis for Effective Feature Selection in AI Model Development 43Raju Arumugam4 Fusion-Based CNN Ensemble with Grad-CAM for Trustworthy and Transparent Plant Disease Detection 73G. Abirami and S. Aasha Nandhini5 Case Studies and Applications of Explainability and Interpretability in AI Models 99P. Gajalakshmi, N. Hemalatha, R. Elavarasi, N. Magadevi and D. KadhiravanPart II: Privacy-Preserving and Secure AI Systems 1256 Privacy-Preserving AI Techniques: Protecting Data in the Age of AI 127N. Ram Shankar, S. Suhasini, M. Aravind Adityaa, B. Charan Sai, R. Deekshit, D. Derrick Nathaniel and K. Manikandan7 Federated Learning for Early Detection of Chronic Diseases: Privacy-Preserving Models in Population Health Management 153A.V. Sriharsha and Sai Nomitha Yarabolu8 Secure and Trustworthy AI for Efficient Diabetic Retinopathy Screening with Deep Learning Model 183S. Sreedevi, K. Sarmila Har Beagam, G. Ezhilarasi and D. Lakshmi9 Addressing Security Challenges in AI-Driven Cyber Security: Enhancing Resilience While Fostering Sustainable Practices with Green Computing 205P. Geetha, G. Abirami, T. Padmavathy, S. Sivagami and D. VinodhaPart III: AI in Smart Healthcare, Agriculture and Energy and Power Systems 22710 Enhancing Breast Cancer Health Care Using Vision Transformer Processing with Dingo Optimization 229S. Baulkani and Koushalya S.11 Enhancing Biometric Identification: A Trustworthy Framework for Toddler Iris Recognition through AI Innovations 249Ramesh S. and V. Krishnaveni12 AI-Enhanced Reactive Power Compensation in Weak Grids Integrating Wind Energy Systems: A Trustworthy and Risk-Managed Approach 279R. Rajasree, D. Lakshmi, K. Stalin and R.K. Padmashini13 AI-Based Frequency Regulation for a Deregulated Two-Area Power System 305D. Lakshmi, V. Pramila, S. Aasha Nandhini and R. RajasreePart IV: Real-World AI Applications and Future Opportunities 32914 Smart Defense Vehicle (Bot) with AI-Assisted Security System 331V. Sridevi and S. Priya15 Smart Motor Fault Detection Leveraging LabVIEW and IoT Integration 251Vinoth Kumar P., Priya S., Prakash S., Gunapriya D. and Sridevi V.References 368Index 371