AI-Enhanced Identity and Access Management: A Machine Learning Approach to Zero Trust Security

Abstract

The swift embrace of cloud computing has revolutionised contemporary enterprises, facilitating scalable, efficient, and adaptable operations. Nonetheless, it has also presented new security issues, especially in identity and access management (IAM). Conventional IAM methods are progressively being supplanted or augmented by AI-driven methodologies that provide better authentication, authorisation, and access control. This study examines the role of Artificial Intelligence (AI) in enhancing Identity and Access Management (IAM) inside cloud systems, investigating AI's capacity to bolster security, improve user experience, and facilitate regulatory compliance. This report offers a detailed examination of AI methodology, case studies, problems, and future prospects, serving as a guide for organisations aiming to leverage AI for safe and efficient Identity and Access Management in the cloud. Recent technical developments have accelerated the introduction of Machine Learning (ML) to safety- and security-critical applications, such as autonomous machines, financial systems, and military systems. You may utilise ML components for processing input data or for making decisions. Due of the high expectations for reaction time and success rate, many training algorithms generate models that are difficult for humans to understand and validate, such as multilayer neural networks. In most circumstances, it is not practicable to provide comprehensive testing coverage due to the complexity of these models. Security concerns arise when ML components exhibit unusual behaviour as a result of malicious manipulation, sometimes known as backdoor attacks.