Big data and machine learning for securing identity and access management systems

In an era of expanding digital interconnectivity, the security of Identity and Access Management (IAM) systems has become a pivotal concern. This study explores the transformative potential of integrating big data analytics and machine learning technologies into IAM frameworks to address contemporary cybersecurity challenges. By examining the historical evolution and core functions of IAM systems, the research underscores their importance in managing digital identities and regulating access across complex infrastructures. The paper delves into various facets of big data processing—collection, storage, anomaly detection, real-time monitoring—and evaluates how machine learning techniques such as predictive analytics, adaptive access control, and user behavior analysis can fortify IAM against sophisticated cyber threats. Further, it investigates practical implementations, real-world applications, and challenges including data privacy, compliance, model interpretability, and scalability. Through a critical synthesis of recent literature and applied case studies, this research offers strategic insights and recommendations for deploying AI-driven IAM systems that are secure, adaptive, and scalable, positioning them as critical enablers of trust in modern digital ecosystems.