AI-Driven Access Review Architecture for Scalable Identity Governance in Modern Enterprises
DOI:
https://doi.org/10.22399/ijcesen.3176Keywords:
Identity and Access Management, Identity Governance and Administration, Access Certification, Artificial Intelligence (AI), Machine Learning, Segregation of Duties (SoD), Risk-Based Access ControlAbstract
Identity and Access Management (IAM) is defined as a critical discipline in cybersecurity, designed to ensure that appropriate access is granted to individuals based on organizational policies and user roles [15]. As digital ecosystems expand and threats grow more sophisticated, the relevance of IAM is increasingly acknowledged across enterprises and governments alike. Access reviews, positioned as a fundamental component of IAM and Identity Governance and Administration (IGA), are required to validate whether users retain the correct access over time. Traditionally, these reviews have been conducted manually, often resulting in inefficiencies, oversight, and compliance risks. To address these limitations, the integration of artificial intelligence into access review processes is being explored. In this paper, the concept of AI-driven access reviews is introduced and examined as a transformative approach to automating decision-making, detecting access anomalies, and enhancing policy enforcement. Emphasis is placed on how machine learning, behavioural analysis, and contextual risk scoring can be applied to optimize review cycles and reduce human error. Multiple methodologies for implementing AI in access reviews are evaluated, and the potential impact of these advancements on future IAM strategies is discussed in detail.
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