AI-Powered Identity and Access Management Solutions for Multi-Cloud Environments

Abstract

The rapid adoption of multi-cloud infrastructures has introduced complex challenges in managing Identity and Access Management (IAM) systems, requiring robust, scalable, and dynamic solutions to ensure secure and efficient access control across diverse platforms. Traditional IAM approaches struggle to address the dynamic and distributed nature of multi-cloud environments, often resulting in security vulnerabilities, operational inefficiencies, and increased risks of unauthorized access. This paper explores the integration of Artificial Intelligence (AI) into IAM solutions for multi-cloud ecosystems, highlighting how AI-driven methods can enhance realtime decision-making, automate policy enforcement, and adapt to evolving access needs. By leveraging machine learning, anomaly detection, and predictive analytics, AI-powered IAM systems can provide advanced capabilities such as continuous authentication, intelligent rolebased access control, and adaptive risk-based access policies. Furthermore, the paper examines the benefits of AI in improving IAM scalability, reducing manual intervention, and optimizing compliance with industry standards. Through case studies and real-world examples, this paper demonstrates how AI enhances IAM security and efficiency in multi-cloud infrastructures, offering new opportunities for organizations to streamline access management while mitigating the risks associated with cloud adoption.