AI-Powered Zero-Trust Security Models for Next Generation Cloud Infrastructure

Abstract

With the expansion of cloud computing, hybrid networks, and edge computing, traditional security models relying on perimeter defenses are no longer effective against advanced persistent threats (APTs), insider attacks, and zero-day vulnerabilities. The Zero-Trust Security Model (ZTSM) has emerged as a paradigm shift, where access to cloud resources is granted only after continuous authentication, risk-based verification, and contextual analysis. This paper explores how Artificial Intelligence (AI) enhances Zero-Trust Security Models through advanced machine learning (ML) algorithms, behavioral analytics, anomaly detection, and automated security policy enforcement. AI-powered adaptive authentication and predictive threat intelligence minimize unauthorized access risks while reducing operational complexity. Experimental results demonstrate that AI-enhanced ZTSM improves threat detection rates by 22%, reduces false positives by 67%, and lowers incident response time by 68%. This study highlights how AI-driven Zero-Trust Security will be a fundamental approach for securing next-generation cloud infrastructure.