NEXUS-A Secure Voice Assistant Virtual Robot Using ZTA And Reinforcement Learning

Abstract

With the rapid advancement of voice assistant technology, ensuring security and privacy remains a critical challenge. This research introduces a Secure AI Voice Assistant Robot that incorporates Zero Trust Architecture (ZTA) and Multi-Factor Authentication (MFA) to establish a highly secure framework. By integrating Voice Biometric Authentication (VBA) and fingerprint recognition, unauthorized access is effectively mitigated. Additionally, Support Vector Machine (SVM) and Reinforcement Learning techniques enhance authentication accuracy and adaptive threat detection.

The proposed security model was rigorously tested under various threat scenarios, demonstrating its effectiveness in protecting sensitive user data. The integration of ZTA, MFA, and AI-driven security strategies provides a comprehensive defence against cyber threats and unauthorized intrusions. Moving forward, future improvements will focus on refining AI-driven anomaly detection, enhancing real-time security adaptability, and expanding encryption techniques to further secure sensitive interactions. These enhancements aim to bolster security while maintaining seamless user interaction.