Adaptive Rule-Based Trust Model with Real-Time Policy Matching for Multi-Cloud Environments

In these days modern enterprizes are rapidly opting for multi-could platforms because of cost, complaince and most importantly to optimize their overall perfromance. Given this flexibility, a central challenge that arises is how to maintain trust among heterogeneous Cloud Service Providers (CSPs) as since service reliability may fluctuate and policies may evolve, with new risks appearing without any notice. We tried to cope with this varying trust issue across multiple CSPs by proposing an adaptive, rule-based trust framework incorporating measurable Performance Specifications (P-Specs) together with a real-time Policy Matching Engine (PME). In contrast to existing static models, the proposed approach further refines rule weights and penalties continuously based on the observed behavior of cloud service providers. The proposed P-Specs capture quantitative attributes of latency, availability, and data integrity, while the PME checks policy conformance dynamically. Under a simulated multi-cloud testbed, the proposed framework reached 84% trust classification accuracy with a mean decision latency of 12 ms and a 90% policy matching rate, comparatively better than the existing trust models. Hence, this proposed framework offers a practical and scalable way to handle the trust across dynamic and diverse cloud service providers.