The Age of the Big Data

With the age of Big Data Security upon us, it’s about time we decided to understand the different types of data sets presented by Big Data in order to improve data security. In fact, 44% of organizations characterize their security data processing, storage and collection activities as “Big Data”, while another 44% believe that their security data processing, storage and collection activities will become “Big Data” within 2 years.

Although the age of Big Data security analytics is here, yet most organizations face a growing issue. On one hand they need Big Data to make more informed decisions, and on the other, they don’t have the skills, staff or process to handle big data analytics, let alone reap the benefits from it.

This is actually a really big deal with enterprise security being a story of haves and have not’s. According to the ESG research, about 17% to 22% of large enterprises fall into the “advanced” category, suggesting that these organizations are capable of embarking on big data security journey. That leaves about 80% of the organizations that requires some form of assistance if they decided to ‘tag’ along on the voyage.

In order to bridge this gap, the big data security analytics have responded with a few solutions such as:

Canned Algorithms

Although, large organizations will have the teams of data scientists, programmers, and security analysts working together, they will still be dependent on their security analytics vendors who can deliver a constant flow of canned algorithms that will detect infected hosts, Command and Control (C&C) communications, credential harvesting and network reconnaissance.


Generally, large organizations need to know everything that there is about their network like, what assets are connected, what other assets communicate with, how are they configured and they need to fully grasp the concept of network trafficking patterns in order to detect suspicious and anomalous behavior. The Big Data Security acts as an intelligence hub by correlating situational awareness and continuous monitoring.


In today’s world there are too many network packets, events, vulnerabilities and threats to people to keep track of. Although the security community is shy about the idea of installing security devices in blocking mode, this can actually be a part of the big data security analytics solution of moving forward where an analytics engines spots a threat and takes action.


It was in 1955 that the idea of grouping came about, especially when a group of IBM 701 users in Los Angeles had gotten together to exchange experiences, practices and ideas. This resulted in the formation of ‘SHARE”, which is a mainframe user group. This form of collective collaboration is actually important for those inexperienced users who are looking to benefit from Big Data security analytics. Vendors who focus on promoting and organizing such efforts among the industry line can actually gain an advantage.

Big Data Security Analytics might be an enterprise inevitability, however, it may turn out to be too geeky for most organizations.

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