The Role of AI & Machine Learning in Identity Governance
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© 2025 by IJCTT Journal
Volume-73 Issue-6
Year of Publication : 2025
Authors : Yashasvi Sharma
DOI :  10.14445/22312803/IJCTT-V73I6P101

How to Cite?

Yashasvi Sharma, "The Role of AI & Machine Learning in Identity Governance," International Journal of Computer Trends and Technology, vol. 73, no. 6, pp. 1-6, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I6P101

Abstract
Organizations must prioritize identity governance as an essential element for cybersecurity and risk management because their digital ecosystems continue to grow. The growing use of cloud services, remote work, and third-party integrations has made managing user identities and access controls harder while maintaining compliance standards. Traditional Identity and Access Management (IAM) approaches face difficulties in efficient scaling, threat detection, and policy enforcement at runtime. Security gaps emerge from these limitations, which result in unauthorized access, insider threats, and non-compliance with regulations.
Artificial Intelligence (AI) and Machine Learning (ML) are powerful modern identity governance tools that deliver real time identity monitoring, predictive analytics, and automated decision-making capabilities. AI solutions process extensive identity data to identify unusual behaviors, which enables them to prevent security risks from becoming major breaches. Through behavioral analytics, AI systems create normal user activity baselines to detect suspicious actions, including unauthorized data access, privilege escalations, and suspicious login attempts. Real-time threat detection becomes possible through this proactive security approach. (Azhar, 2015) One of the key benefits of AI in identity governance is automated access control. Traditional Role-Based Access Control (RBAC) models often require manual updates and periodic reviews, leading to inefficiencies and errors. AI-powered identity governance solutions enable dynamic access provisioning, automatically granting or revoking permissions based on user behavior, job role changes, and contextual risk assessments. This approach guarantees that employees have correct access when needed, lowering the chance of privilege misuse.
This research explores how AI and ML technologies work together with identity governance systems through practical use cases, implementation methods, and real-world deployment examples. The research illustrates how AI-based identity governance systems improve security while enhancing operational efficiency and regulatory compliance across various sectors. Organizations must adopt AI-powered identity governance as cyber threats evolve to protect sensitive data, reduce insider threats, and optimize access control in contemporary IT systems. [7]

Keywords
Identity Governance, Artificial Intelligence (AI), Machine Learning (ML), Access Control, Cybersecurity compliance.

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