AI Trust, Risk, and Security Management
Framework, Principles, and Practices
1st Edition
Published on 16. January 2026
Book
Hardback
416 pages
978-1-394-39299-5 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Dimensions
Height: 236 mm
Width: 158 mm
Thickness: 28 mm
Weight
694 gr
ISBN-13
978-1-394-39299-5 (9781394392995)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
R. Karthick Manoj | S. Senthilnathan | S. Arunmozhi Selvi
AI Trust, Risk, and Security Management
Framework, Principles, and Practices
E-Book
01/2026
1st Edition
Wiley
€188.99
Available for download
R. Karthick Manoj | S. Senthilnathan | S. Arunmozhi Selvi
AI Trust, Risk, and Security Management
Framework, Principles, and Practices
E-Book
01/2026
1st Edition
Wiley
€179.99
Available for download
Persons
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.
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.
Content
Series Preface xix
Preface xxi
Part I: Fundamentals of Trustworthy and Transparent AI 1
1 Creating Trustworthy AI: A Lifecycle Risk Management Framework 3
Satish Kumar S., Bharathi K., Vinod S., Rudhra S., Balaraman R. and Suresh A.
2 Comprehensibility and Transparency of AI Systems with Applications 19
N. Hemalatha, R. Elavarasi, P. Gajalakshmi, N. Magadevi and D. Kadhiravan
3 Leveraging Correlation Analysis for Effective Feature Selection in AI Model Development 43
Raju Arumugam
4 Fusion-Based CNN Ensemble with Grad-CAM for Trustworthy and Transparent Plant Disease Detection 73
G. Abirami and S. Aasha Nandhini
5 Case Studies and Applications of Explainability and Interpretability in AI Models 99
P. Gajalakshmi, N. Hemalatha, R. Elavarasi, N. Magadevi and D. Kadhiravan
Part II: Privacy-Preserving and Secure AI Systems 125
6 Privacy-Preserving AI Techniques: Protecting Data in the Age of AI 127
N. Ram Shankar, S. Suhasini, M. Aravind Adityaa, B. Charan Sai, R. Deekshit, D. Derrick Nathaniel and K. Manikandan
7 Federated Learning for Early Detection of Chronic Diseases: Privacy-Preserving Models in Population Health Management 153
A.V. Sriharsha and Sai Nomitha Yarabolu
8 Secure and Trustworthy AI for Efficient Diabetic Retinopathy Screening with Deep Learning Model 183
S. Sreedevi, K. Sarmila Har Beagam, G. Ezhilarasi and D. Lakshmi
9 Addressing Security Challenges in AI-Driven Cyber Security: Enhancing Resilience While Fostering Sustainable Practices with Green Computing 205
P. Geetha, G. Abirami, T. Padmavathy, S. Sivagami and D. Vinodha
Part III: AI in Smart Healthcare, Agriculture and Energy and Power Systems 227
10 Enhancing Breast Cancer Health Care Using Vision Transformer Processing with Dingo Optimization 229
S. Baulkani and Koushalya S.
11 Enhancing Biometric Identification: A Trustworthy Framework for Toddler Iris Recognition through AI Innovations 249
Ramesh S. and V. Krishnaveni
12 AI-Enhanced Reactive Power Compensation in Weak Grids Integrating Wind Energy Systems: A Trustworthy and Risk-Managed Approach 279
R. Rajasree, D. Lakshmi, K. Stalin and R.K. Padmashini
13 AI-Based Frequency Regulation for a Deregulated Two-Area Power System 305
D. Lakshmi, V. Pramila, S. Aasha Nandhini and R. Rajasree
Part IV: Real-World AI Applications and Future Opportunities 329
14 Smart Defense Vehicle (Bot) with AI-Assisted Security System 331
V. Sridevi and S. Priya
15 Smart Motor Fault Detection Leveraging LabVIEW and IoT Integration 251
Vinoth Kumar P., Priya S., Prakash S., Gunapriya D. and Sridevi V.
References 368
Index 371
Preface xxi
Part I: Fundamentals of Trustworthy and Transparent AI 1
1 Creating Trustworthy AI: A Lifecycle Risk Management Framework 3
Satish Kumar S., Bharathi K., Vinod S., Rudhra S., Balaraman R. and Suresh A.
2 Comprehensibility and Transparency of AI Systems with Applications 19
N. Hemalatha, R. Elavarasi, P. Gajalakshmi, N. Magadevi and D. Kadhiravan
3 Leveraging Correlation Analysis for Effective Feature Selection in AI Model Development 43
Raju Arumugam
4 Fusion-Based CNN Ensemble with Grad-CAM for Trustworthy and Transparent Plant Disease Detection 73
G. Abirami and S. Aasha Nandhini
5 Case Studies and Applications of Explainability and Interpretability in AI Models 99
P. Gajalakshmi, N. Hemalatha, R. Elavarasi, N. Magadevi and D. Kadhiravan
Part II: Privacy-Preserving and Secure AI Systems 125
6 Privacy-Preserving AI Techniques: Protecting Data in the Age of AI 127
N. Ram Shankar, S. Suhasini, M. Aravind Adityaa, B. Charan Sai, R. Deekshit, D. Derrick Nathaniel and K. Manikandan
7 Federated Learning for Early Detection of Chronic Diseases: Privacy-Preserving Models in Population Health Management 153
A.V. Sriharsha and Sai Nomitha Yarabolu
8 Secure and Trustworthy AI for Efficient Diabetic Retinopathy Screening with Deep Learning Model 183
S. Sreedevi, K. Sarmila Har Beagam, G. Ezhilarasi and D. Lakshmi
9 Addressing Security Challenges in AI-Driven Cyber Security: Enhancing Resilience While Fostering Sustainable Practices with Green Computing 205
P. Geetha, G. Abirami, T. Padmavathy, S. Sivagami and D. Vinodha
Part III: AI in Smart Healthcare, Agriculture and Energy and Power Systems 227
10 Enhancing Breast Cancer Health Care Using Vision Transformer Processing with Dingo Optimization 229
S. Baulkani and Koushalya S.
11 Enhancing Biometric Identification: A Trustworthy Framework for Toddler Iris Recognition through AI Innovations 249
Ramesh S. and V. Krishnaveni
12 AI-Enhanced Reactive Power Compensation in Weak Grids Integrating Wind Energy Systems: A Trustworthy and Risk-Managed Approach 279
R. Rajasree, D. Lakshmi, K. Stalin and R.K. Padmashini
13 AI-Based Frequency Regulation for a Deregulated Two-Area Power System 305
D. Lakshmi, V. Pramila, S. Aasha Nandhini and R. Rajasree
Part IV: Real-World AI Applications and Future Opportunities 329
14 Smart Defense Vehicle (Bot) with AI-Assisted Security System 331
V. Sridevi and S. Priya
15 Smart Motor Fault Detection Leveraging LabVIEW and IoT Integration 251
Vinoth Kumar P., Priya S., Prakash S., Gunapriya D. and Sridevi V.
References 368
Index 371