基于贝叶斯网络的数据跨境流动风险识别与评估OACHSSCD

To reduce the risks associated with cross-border data flows in the electric vehicle industry,this study identifies key risk factors and develops a Bayesian network model for risk assessment.Based on collected cases of cross-border data violations and a review of relevant literature,grounded theory was applied to identify risk factors related to cross-border data flows in the elec-tric vehicle sector,resulting in the extraction of fifteen major risk factors.An Interpretive Structural Model(ISM)was then em-ployed to organize these factors into five hierarchical levels,and the resulting hierarchical structure was further transformed into a Bayesian network structure.Using the constructed Bayesian network,the collected data were input into the GeNIe software for pa-rameter learning,and the probability distribution of different risk levels for cross-border data flows in the electric vehicle industry was obtained.Subsequently,diagnostic analysis,sensitivity analysis,and maximum causal chain analysis were conducted based on the Bayesian network model to identify the key and sensitive factors leading to cross-border data flow risks.Based on the analysis results,corresponding risk prevention and control strategies were proposed,providing a scientific basis and analytical tool for cross-border data risk management in the electric vehicle industry.