Revolutionizing Role-Based Access Control: The Impact of AI and Machine Learning in Identity and Access Management

Abstract

Identity and access management is the bedrock of cybersecurity. Identity access to digital resources is governed by its techniques, procedures, and rules, which also define the breadth of identity permission over those resources. Some new cyberattack or data breach pops up in the news every week. Many data breaches occur due to inadequate security measures, software flaws, human mistake, malevolent insiders, or the abuse of access and privileges. An improved access control system is possible with the use of AI methods. In order for organisations to better handle authentication and access control in order to reduce cyber risks and other IAM difficulties, studies into artificial intelligence in IAM are necessary. With an eye towards AI’s potential uses in identity and access management - more especially in the areas of privilege monitoring, administration, and control - this research investigates the nature of the connection between AMIS and AI. To better understand how AI works in minimising recognised IAM issues, this study aimed to present evidence from the relevant literature. This study’s results show how AI reinforces identity and access management, which helps with automating procedures, keeping up with technology advances, and reducing the prevalence of cyber threats. One way to accomplish this is by using a binary classification system for security access
control, which takes the PDP problem and turns it into a yes/no question. In order to create a distributed, effective, and accurate policy decision point
(PDP), a vector decision classifier is also built using the supervised machine learning technique. Kaggle-Amazon access control policy dataset evaluated
performance by comparing the proposed mechanism to previous research standards in terms of performance, duration, and flexibility. Given that the PDP is not in direct contact with the PAP, the proposed approach accomplishes a high level of secrecy in relation to access control requirements. In conclusion, PDP-based ML can manage massive access requests, execute many major policies simultaneously, and have a 95% accuracy rate, all without policy conflicts, with a response time of about 0.15 s. The security of access control can be enhanced by making it more responsive, flexible, dynamic, and dispersed.