AI-Driven Identity and Access Management: Opportunities, Challenges, and Future Directions

Abstract

The rapid advancement of Artificial Intelligence (AI) has revolutionized Identity and Access Management (IAM), enabling organizations to enhance security, streamline authentication processes, and mitigate cyber risks. AI-driven IAM systems leverage machine learning, behavioral analytics, and predictive modeling to detect anomalies, automate access decisions, and improve compliance with data protection regulations. This paper explores the emerging opportunities that AI brings to IAM, including adaptive authentication, risk-based access control, and real-time threat detection. It also examines the major challenges such as data privacy concerns, model bias, explainability issues, and integration complexities within existing infrastructures. Furthermore, the study provides a critical overview of future research directions and practical implications, highlighting the need for transparent, ethical, and resilient AI frameworks. The findings suggest that while AI significantly enhances IAM efficiency and security, a balanced approach integrating human oversight and robust governance is essential for sustainable deployment.

Ähnliche Arbeiten