Leveraging Machine Learning Techniques for Zero Trust Privacy Protection

The Zero Trust framework is centered on the assumption of "never trust, always verify" has emerged as a dominant approach to securing access to sensitive information. It offers a transformative solution which challenges conventional security best-practices through implementing continuous verification and with least privileged access control. This strategy is based on its mitigation mechanism on account of the rising complexity posed by cybersecurity threats. This demands new creative strategies to safeguard sensitive information supporting a shift away from implicit trust to a model of mandatory default verification for every network transaction. This paper explores the theoretical foundations, practical implementations, applications, challenges, and emerging trends of Zero Trust. More specifically, focusing on integrating machine learning (ML) techniques within the Zero Trust framework to enhance privacy protection by concentrating on Zero Trust principles its challenges, mitigation mechanism, as well as combining ML and artificial intelligence (AI) with emerging technologies.