Research on Data Risks and Protection in Engineering Enterprises Under the Context of Generative Large Models
Ruiqi Wang · International Journal of Artificial Intelligence Interdisciplinary Research · 2026
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.