AI-Driven Adaptive Permission Management System for the Android Operating System

Abstract

The rapid proliferation of mobile applications on the Android Operating System has significantly increased concerns regarding user privacy and data security. Traditional permission management models in Android rely on static, user-granted permissions at install time or runtime, which often fail to adapt to changing user behavior, contextual factors, and evolving security threats. This limitation can lead to over-permissioned applications and unintended data exposure. This research proposes an AI-driven adaptive permission management system that dynamically monitors application behavior and user interaction patterns to intelligently grant, revoke, or suggest permissions in real time. By leveraging machine learning techniques such as behavioral analysis, anomaly detection, and context-aware decision-making, the system aims to minimize unnecessary permission access while maintaining seamless user experience. The proposed model continuously learns from user preferences, app usage frequency, location context, and historical permission decisions to create a personalized security profile for each user. Furthermore, the system integrates a risk assessment engine that identifies potentially malicious or suspicious application activities, providing proactive alerts and automated mitigation strategies. Experimental evaluation demonstrates that the proposed approach significantly reduces privacy risks and improves permission accuracy compared to conventional permission frameworks, without imposing substantial computational overhead. The findings of this study highlight the potential of artificial intelligence in enhancing mobile security and present a scalable solution for next-generation permission management systems in Android environments.