大数据保密:使用基于规则的系统实现企业合规的方法
Georgios Vranopoulos1, Nathan Clarke1, Shirley Atkinson1
1School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, United Kingdom.
Big data
|October 31, 2023
概括
组织面临的数据治理挑战,特别是大数据的变化. 一个新的框架使用算法分类来减轻数据暴露风险并有效执行政策.
科学领域:
- 信息安全 信息安全
- 数据管理数据管理
- 计算机科学 计算机科学
背景情况:
- 组织投资数据分析以获得竞争优势,在高度监管的行业中面临法律和监管挑战.
- 大数据的维度 (体积,速度,多样性) 加大了数据丢失的风险,多样性是一个重要的,经常被忽视的挑战.
- 现有的数据治理框架难以应对数据多样性的复杂性,增加暴露和损失风险.
研究的目的:
- 为各个组织提供一个新的数据评估框架,以减轻数据暴露和丢失风险.
- 通过算法分类和工作流能力来解决大数据的多样性维度所带来的挑战.
- 为执行数据分类政策和管理异常提供实用解决方案.
主要方法:
- 开发一个基于规则的系统,实施企业数据分类政策.
- 集成算法分类和工作流的能力,以进行一致的数据评估.
- 实施具有批准机制的异常处理流程.
主要成果:
- 一个概念验证原型由具有广泛安全和数据管理经验的学者和高管开发和评估.
- 90%的评论员认为数据多样性是最令人不安的大数据维度.
- 大约60%的人证实了适当的政策和程序的存在,但强调落后的实施工具.
结论:
- 拟议的框架有效地解决了数据暴露和损失风险,特别是与数据多样性相关的风险.
- 人们认识到需要改进实施工具,以支持数据分类政策.
- 这项研究强调了大数据时代管理数据多样性的关键重要性.
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