Preemptive Security Through Information Analytics

被引:0
|
作者
Early, Gregory [1 ]
Stott, William, III [1 ]
机构
[1] Raytheon Missile Syst, 3360 E Hemisphere Loop,Bldg M10, Tucson, AZ 85706 USA
来源
INFORMATION SECURITY JOURNAL | 2015年 / 24卷 / 1-3期
关键词
data attribute correlation; data scientist; innocuous security data fusion; predictive big data analytics; security information analytics;
D O I
10.1080/19393555.2015.1042600
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Business security and threat actors continue to play a dangerous cat-and-mouse game with businesses intellectual property, customer data, and business reputations at stake. Businesses need to delve into a new way of doing business security to break out of this game. Businesses are sitting on repositories full of security-relevant data that is not being capitalized upon with the current information security and physical security organizations within businesses. This article poses the introduction of a data scientist role and a new supporting central data correlation technology platform based on big data predictive analytics into business security functions. The goal is to intelligently and autonomously identify, correlate and pinpoint normally innocuous or unnoticed security event attributes to allow security personnel to preemptively remediate physical and information risks before exploitation or loss of intellectual property occurs.
引用
收藏
页码:48 / 56
页数:9
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