A Classification and Grading Governance Framework for Cross-Border Data Flow in Intelligent and Connected Vehicles

The intelligent and connected vehicle industry is a key component of the digital economy. The data collected during vehicle operation contains a significant amount of critical information, and its leakage or tampering in cross-border data flow scenarios can result in severe security incidents. China implements a data classification and grading management system; however, current challenges such as subjective classification criteria and dynamic adjustment persist. This paper analyzes the developmental trends of global data flow policies and further explores the difficulties in data classification and grading, such as contextual dependency and data quality. Focusing on cross-border data transfer in vehicles, this study proposes a data nature and quality analysis framework. By conducting a layered analysis of data nature (foundation layer, functional layer, informational layer, and risk layer) and evaluating data quality (identifiability and temporality), the framework provides a quantitative assessment method for governing cross-border vehicle data flows.