Zero-trust identity principles in next-gen networks: AI-driven continuous verification for secure digital ecosystems

With the rise in the decentralization of digital ecosystems, identity has come out as the new pillar of cybersecurity in the next generation networks. However, with the increasing complexity of the threats that include the hybrid, cloud-native, and edge computing, traditional models relying on perimeters cannot solve the problem as well as before. Zero Trust Architecture (ZTA) alters the security paradigm by applying the concept of never trust, always verify, so that everything must constantly be authenticated and dynamically authorized by everyone and everything. In this paper we will be examining how Zero Trust is changing the way identity management is done by eliminating static credentials and role-based access with real-time verification using behavior. At the heart of such transformation lies the inclusion of Artificial Intelligence (AI), which facilitates the constant evaluation of trust on the basis of any contextual data such as device posture, user behavior, geolocation and access patterns. We hypothesize a dynamic trust model that leverages machine-learning models to generate dynamically adaptive trust scores and make policy decisions in execution. The model supports the main issues in identity lifecycle, detection of threats, and risk aware access control. The paper also discusses security, scalability, and privacy of using AI to insert identity verification workflow. In this way, we will show how smart automation can reinforce access control in next-gen networks by applying Zero Trust principles that provide a robust, scalable, and context-aware defense to attackers based on identity in next-gen networks.