With the rapid development of the digital economy, data, as an important productive factor, plays a central role in fostering new forms of productive forces and ensuring the efficient functioning of the economy and society. The efficient utilization of data as a production factor, along with its orderly, secure, and compliant circulation among various entities, is essential to realizing its full value. However, data circulation is confronted with significant security risks and trust-related challenges, including leakage, tampering, and unavailability. At the same time, the requirements for data authenticity and circulation transparency are becoming increasingly important. To address these issues, technologies such as cryptography, blockchain, and trusted execution environments are widely applied. However, these technologies still face limitations such as high costs and insufficient flexibility, and current research often focuses on specific stages, lacking a holistic perspective. Therefore, this study is structured around the full life cycle of data factors, develops an analytical framework comprising six stages: data collection, data transmission, data storage, data processing, data publication, and data traceability, and systematically analyzes the security and trust-related challenges at each stage. For the first time, this study explicitly proposes two evolutionary models: the “three-line differentiation and collaborative integration” model for security technologies and the “verification-depth progression” model for trustworthy technologies. This study aims to offer systematic solutions to key issues in this field and outline directions for future research.