Zero-Trust Learning

In this internet age of security challenges and threats from cybercrime, enhanced security measures are necessary. The zero trust modelâ"the IT security model that requires strict identity verification for every person and device trying to access resources on a private networkâ"helps to meet these ever-increasing and evolving security challenges.

This new volume, Zero-Trust Learning: Applications in Modern Network Security, offers a comprehensive overview of the zero trust security model and its application in the field of cybersecurity, covering the principles, technologies, and best practices for implementing a zero trust approach, equipping readers with the knowledge and tools to secure their digital environments effectively.

This book stands out by providing a holistic view of the zero trust security model, combining practical guidance for professionals with educational insights for both professionals and students. It goes beyond theory, offering real-world examples and case studies that bridge the gap between learning and implementation. Additionally, it incorporates the latest advancements in cybersecurity education, making it an indispensable resource for both educators and practitioners.

Chapters cover the fundamentals, importance, and advantages of zero-trust security models; address the roles AI, blockchain, and machine learning play in zero trust learning and security; and look at the different kinds of ever more sophisticated security threats and how zero trust can mitigate risk.

This volume will be a valuable guide for those charged with and involved with network security, such as cybersecurity professionals and practitioners, information technology professionals, and faculty and students in these areas.