使用企业数据源预测文档中的策略违规
By using random sampling and metadata analysis, policy violation hotspots in unstructured data sources of large enterprises are identified, solving the problem of low efficiency in identifying and predicting policy violations in existing technologies, and achieving efficient and low-cost compliance analysis.
CN116029544BActive Publication Date: 2026-07-17INTERNATIONAL BUSINESS MACHINE CORPORATION
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INTERNATIONAL BUSINESS MACHINE CORPORATION
- Filing Date
- 2022-09-15
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing technologies struggle to efficiently identify and predict policy violations in unstructured data sources for large enterprises, especially when performing full-text analysis on a large volume of documents, which is both costly and inefficient.
Method used
By using random sampling and metadata analysis, and leveraging metadata sample size and predictive models, potential policy violation hotspots can be identified. Documents can be predicted to contain violations based solely on metadata attributes, reducing computational resource consumption.
Benefits of technology
It enables efficient identification and prediction of policy violations without analyzing all document content, reducing computational costs and time, and improving the efficiency of compliance analysis.
✦ Generated by Eureka AI based on patent content.
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Figure CN116029544B_ABST
Abstract
预测给定数据源(诸如文档集合)中的潜在策略违规，使得可以对文档集合执行更深入分析以获得对可能包含在其中的潜在策略违规的附加见解。在一些实例中，该预测是通过对文档集合执行随机采样操作并从这些文档收集元数据以便确定被随机采样的文档集合是否包括可以被补救的策略违规来完成的。
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