Application of big data technology in enterprise information security management.

This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models. For different types of security threats, the system uses feature engineering and model training processes to extract key risk indicators and optimize model prediction performance. The experimental results show that the constructed risk prediction model has excellent performance on the test set, and its Area Under the Curve reaches 0.95, indicating that the model has good differentiation ability and high prediction accuracy. In addition, in the multi-class risk identification task, the model achieves an average precision of 0.87. Compared with the traditional method, it has remarkably improved the early warning accuracy and response speed of enterprises to various information security incidents. Therefore, this study confirms the effectiveness and feasibility of applying BDT to EIS risk management, and the successfully constructed prediction model provides strong technical support for EIS protection.