Research on Data Risks and Protection in Engineering Enterprises Under the Context of Generative Large Models

Generative large models have been deeply integrated into the core business operations and enterprise management of engineering enterprises, significantly improving production and management efficiency. However, engineering enterprise data contains a large amount of trade secrets, and the risk of exposure in the large model environment is becoming increasingly prominent. This paper takes the Data Lifecycle Management (DLM) method as a framework [1] to systematically identify data security risks faced by engineering enterprises at various stages of large model application, and constructs a protection strategy system covering the entire lifecycle of "creation—storage—use—archiving—deletion," providing practical references for enterprises to apply generative large models safely and compliantly.