<![CDATA[The heterogeneity and resource constraints of Internet of Things (IoT) devices render traditional perimeter security inadequate. This study proposes a Zero Trust Security (ZTS) framework for IoT infrastructures that integrates a novel dynamic policy engine with continuous authentication and AI-assisted anomaly detection. The framework was evaluated in a simulated IoT environment using the TON_IoT dataset. Experimental results demonstrate that the proposed model achieved a 92.5% detection accuracy, reduced average response latency to 1.76 seconds, and decreased unauthorized access attempts by 87.1%. The key novelty lies in the architecture's context-aware feedback loop, where anomaly findings directly and adaptively inform access policies in real-time, a mechanism not extensively explored in prior ZTS models for IoT. These findings confirm that integrating ZTS with intelligent analytics significantly enhances IoT security resilience. This framework offers a practical blueprint for implementing robust, context-aware security in large-scale IoT applications, such as smart cities and industrial automation.]]>
