Research on Data Security Risk Assessment Methods for Automobile Product

With the increasing degree of intelligence, connecting and digitalization, automobiles have evolved into intelligent platforms that integrate many sensors, controllers and communication modules, constantly generating and processing vast amounts of data. While the performance of automotive products and user experience is improving, how to ensure the security of automotive data has become a major challenge. Current data security risk assessment methods are generally based on static, predefined models, whereas modern automotive products constitute a highly complex, real-time connected ecosystem where data flow and interaction patterns are difficult to accurately capture and evaluate with existing methods. This paper proposes a comprehensive framework for conducting data security risk assessments in automotive products that accommodates uncertain and imprecise data—specifically designed to tackle the intricate and evolving risks associated with automotive data security. It systematically integrates fundamental principles of data protection with particular emphasis on automotive systems while adopting an all-encompassing approach to safeguarding information integrity. The findings from this study aim to provide a more adaptable and accurate risk assessment method for automotive data security that can effectively identify, evaluate, and mitigate potential threats, optimize strategies for protecting sensitive information, ensure regulatory compliance, enhance decision-making processes concerning safety-critical systems, and ultimately enhance the cyber and data security resilience of automotive products.