Abstract

The rapid adoption of cloud computing, remote work, Software-as-a-Service (SaaS), and hybrid enterprise environments has significantly expanded the identity attack surface. Traditional perimeter-based security models are increasingly inadequate because users and applications frequently access organizational resources from different devices, locations, and networks. Zero Trust security addresses this challenge by continuously evaluating identity, device, resource, and contextual signals rather than implicitly trusting access based on network location.

This paper proposes an AI-driven adaptive identity security framework for Microsoft Azure based on Microsoft Entra ID, Conditional Access, Identity Protection, Privileged Identity Management, and security telemetry. The framework uses risk signals and machine-learning-supported identity protection to dynamically evaluate authentication attempts and apply access decisions according to contextual risk. Rather than implementing uniform authentication requirements for every request, the proposed model applies adaptive controls such as multifactor authentication, access restriction, password remediation, or session blocking according to detected risk. The study examines architecture, automation strategies, and practical use cases including compromised-account detection, anomalous sign-in response, privileged-access protection, and automated identity-risk remediation. The proposed approach demonstrates how identity can function as a dynamic security perimeter for modern Azure environments while supporting Zero Trust principles.