International Journal of Science, Engineering and TechnologyAuthors: Pooja Sharma, Ankit Mehra, Shalini Nair, Rohit Chauhan
Abstract: As enterprises grow increasingly reliant on identity-centric security models, managing and auditing privileged access has become paramount especially in regulated environments such as healthcare, government, and finance. Centrify, a leading privileged access management (PAM) platform, offers comprehensive vaulting, session control, and policy enforcement. However, static access control methods alone often fail to detect nuanced insider threats, credential misuse, or abnormal behavioral patterns. This review explores the integration of artificial intelligence into Centrify-managed UNIX and hybrid environments to enhance user access analytics and proactively detect risks. We examine how machine learning techniques ranging from supervised classification to anomaly detection and time-series modeling can be used to analyze session metadata, command histories, vault activity, and authentication behavior. The paper outlines the architecture of AI-enhanced pipelines, data collection strategies, real-time alerting systems, and integration points with Centrify’s policy engine. We also evaluate the implications of AI-based adaptive access controls, context-aware role adaptation, and forensic replay for audit and compliance. Through detailed sections on threat modeling, deployment strategies, and federated learning approaches, this review positions AI as a transformative layer over traditional access control. Ultimately, AI-augmented user access analytics enable more intelligent, responsive, and resilient identity governance—essential for maintaining Zero Trust postures and meeting modern regulatory requirements.
DOI: http://doi.org/10.5281/zenodo.15847029
