Risk behavior detection method and system based on DLP technology

Through the risk behavior detection method based on DLP technology, combined with machine learning and NLP technology, a user risk behavior monitoring and evaluation model is built, which solves the problem of difficulty in comprehensively and accurately detecting user risk behavior in the existing technology, and achieves high-accurate risk behavior identification and timely risk alerts, and improves the level of network security protection.

CN119652619APending Publication Date: 2025-03-18FUJIAN YANGTENG INNOVATION INFORMATION TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411815770.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-11
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

The prior art has shortcomings in the detection of user risk behavior and content, and it is difficult to fully and accurately identify and evaluate various risk behaviors, and it is impossible to prevent potential security threats in a timely and effective manner.

Method used

Using a risk behavior detection method based on DLP technology, by constructing a user risk behavior monitoring model, collecting and monitoring user behavior data and IM communication software communication data, combining machine learning technology and NLP technology, a user risk behavior identification model and a comprehensive risk assessment model are established to monitor and evaluate users' risk behavior in real time.

Benefits of technology

It has achieved comprehensive and accurate detection and evaluation of user risk behaviors, improved the accuracy of risk behavior identification, promptly issued risk alerts, helped enterprises or organizations to effectively prevent data leakage and compliance risks, and protected the security of information assets.

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Abstract

The invention relates to a risk behavior detection method and system based on the DLP technology, and the method comprises the following steps: building a user risk behavior monitoring model based on the DLP technology, and the user risk behavior monitoring model is used for collecting and monitoring user behavior data and IM communication software communication data; establishing a user risk behavior identification model through a machine learning technology according to user historical behavior data collected by the user risk behavior monitoring model, and monitoring daily operation behaviors of the user in real time; according to IM communication software communication data collected by the user risk behavior monitoring model, whether risk content exists in the IM communication software communication data or not is recognized through the NLP technology; and according to an output result of the user risk behavior identification model and risk content identified by the NLP technology, constructing a comprehensive risk assessment model, when a risk value output by the risk assessment model is higher than a preset value, giving a risk alarm, and automatically recording related data to provide evidence for subsequent investigation and auditing.
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