Addressing the critical challenges in constructing a data security protection system for intelligent rail transit, this paper systematically reviews recent research advances in this field. First, it examines the characteristics of rail transit data, including its multi-source heterogeneity, high real-time demands, and strong sensitivity, along with the primary security risks during data transmission, storage, and processing. Second, it explores the current application status, suitability conditions, and performance metrics of cutting-edge protection technologies—such as federated learning, lightweight encryption, and dynamic access control—against threats like cyberattacks and data breaches in resource-constrained rail transit environments. Finally, it identifies key future research directions, including AI-empowered active defense and cross-system collaborative governance. The findings demonstrate that federated learning effectively resolves data silo issues while enhancing model training efficiency under privacy guarantees. Lightweight encryption algorithms demonstrate millisecond-level response advantages in wayside equipment, exhibiting significant application potential and representing a major future development trend. The research outcomes provide both theoretical underpinnings and practical guidance for building an efficient and reliable intelligent rail transit data security protection system.