Data classification based data leakage prevention method and system

Abstract

The invention relates to a data classification based data leakage prevention method and system, which can further improve the data leakage prevention effect. The data classification based data leakage prevention method comprises the steps as follows: A, designing a basic data classifier, B, defining various elements of DSL (domain specific languages) to form a data leakage strategy library, C, detecting input data via the DSL strategy library, executing defined response in the strategy, and writing time which violates the strategy into a data leakage event database, D, obtaining data, E, executing defined corresponding operation on the violated data, and F, performing statistical classification on the violated data leakage events, and displaying at a client. The data classification based data leakage prevention system comprises a basic classifier management module, a DSL strategy defining and editing module, a DSL strategy analysis execution module, a data access module, a data protection response module and an event management module.

Description

technical field [0001] The invention relates to a data leakage prevention method and a data leakage prevention system adopting the method, in particular to a data classification-based data leakage prevention method and a system thereof. Background technique [0002] Data Leakage Prevention (DLP) is to prevent specified data or information assets from being stored, used and transmitted in a form that violates security policies through certain technical and management means. Data leakage prevention has a strategic position in protecting national data security and an important role in ensuring information security, and is an important part of national security. For enterprises, data leakage is a problem that requires more attention. Regardless of the size of the enterprise, there is a risk of data leakage, and these risks will expose the enterprise to threats to security, intellectual property, property, privacy, and regulatory compliance, resulting in a double waste of ti...

AI Technical Summary

Problems solved by technology

Currently commonly used data identification technologies mainly include: keywords, data dictionaries, regular expressions, specific verification rules (such as ID cards), data description attributes, sample data fingerprints, and classification algorithms based on training samples. The product includes many or all of the recognition technologies, but the existing products are based on a simple logical combination of the above technologies, which cannot accurately describe the data content of the real world. At the same time, the user's professional knowledge is relatively high. For example, the user needs to understand complex Logical expressions, the priority of logical operators, etc. Therefore, users are not satisfied with the use of the product and the final data leakage prevention effect

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