Exposure-Aware Protection of Edge-to-Cloud Data Synchronization Workflows

Abstract

Cloud edge architectures rely on continuous synchronization between edge gateways, regional cloud services, storage systems, and central analytics platforms. Vulnerabilities in synchronization agents, transfer protocols, gateway services, or access policies may expose sensitive edge data or allow unauthorized modification during transmission. This study proposes a policy-aware vulnerability risk quantification framework for cloud edge data synchronization pipelines. The framework evaluates edge-node sensitivity, synchronization frequency, transfer encryption, gateway vulnerability status, data classification, access-token scope, retry behavior, and policy violations. A synchronization-lineage model is built to connect vulnerable edge components with upstream data sources and downstream cloud storage assets. Experiments are conducted on a cloud edge environment containing 1,340 edge gateways, 9,780 synchronization jobs, 4,620 regional storage endpoints, 2,150 analytics ingestion services, 11,900 access tokens, and 21,300 vulnerability records from gateway images, transfer agents, and synchronization APIs. The proposed framework identifies 1,920 data-synchronization risk chains, including vulnerable transfer agents handling sensitive sensor data, weakly encrypted synchronization channels, over-scoped access tokens, and exposed regional ingestion endpoints. It groups 21,300 raw findings into 2,760 synchronization-level remediation units based on data lineage and policy impact. The median scoring time is 46 ms per synchronization job, while the full risk assessment completes in 14.9 minutes. During remediation testing, the first 420 prioritized fixes remove 980 vulnerable data-transfer chains and reduce exposed regional ingestion endpoints from 386 to 121. The framework processes 14,600 gateway-policy relationships per minute during incremental updates. The results demonstrate that policy-aware vulnerability quantification can support risk-based protection of cloud edge data movement, especially where data security depends on synchronization paths, gateway exposure, and access-token control.