Zero Trust Architecture for AI-Driven Cloud Platforms: A Comprehensive Security Framework

Abstract

Zero Trust Architecture offers a transformative security paradigm for AI-driven cloud platforms, addressing critical vulnerabilities essential in traditional boundary-based models. As cloud surroundings increasingly complex artificial intelligence workloads, conventional security approaches fail to accommodate unique characteristics such as distributed processing conditions and dynamic scaling patterns. The proposed frame incorporates three core factors: an AI-driven threat Machine exercising machine literacy for dynamic access opinions, fine-granulated micro-segmentation establishing granular security boundaries through service mesh technologies, and nonstop Authentication mechanisms that persistently validate sessions using behavioral biometrics. Perpetration across different sectors demonstrates substantial security advancements while maintaining functional effectiveness, enabling associations to emplace sensitive AI operations securely while meeting nonsupervisory conditions. The armature specifically addresses AI-unique pitfalls, including model birth, data poisoning, and conclusion attacks through specialized discovery and forestallment mechanisms operating at both structure and operation layers.