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Big data security context

Brian Contos | April 16, 2014
Embracing big data security can be extremely valuable but without context it can also be a massive time and resource black hole.

I just finished up a lengthy tour through Latin America and Asia, as described in many of my latest blogs. Most recently I was in Australia and New Zealand (ANZ). I had the opportunity to work with various government agencies, organizations within critical infrastructure and general enterprise businesses across ANZ. Their primary topic of interest: big data. More specifically, they were interested in determining what needs to be part of a successful big data security strategy.

Years ago some organizations throughout ANZ viewed cyber security in the same way they viewed physical security in response to nation-state threats. Because ANZ has a land and sea gap physically separating them from other countries, there was a feeling of separation and protection from the nefarious activities that might be happening around the world. Of course others realized, as almost all do today, that cyber attacks have grater range than a jet fighter or ICBM regardless of whether they're perpetrated by nation-states, cyber criminals or activists. To address this issue, organizations are trying to optimize their use of big data security by letting the machines do the heavy lifting and allowing the humans to manage by exception.

Big Data

Big data has already proven its value outside of security across many areas such as space exploration, sports, retail and insurance.

When we think of big data, it doesn't get much bigger than space. Big data analytics have lead to corrections — rest in peace Pluto — and countless discoveries such as:

Consider the value it afforded baseball as portrayed in the 2011 movie Moneyball. We've moved from just relying upon visceral reactions by scouts and gut feel to also incorporating math and science.

Think of your latest online shopping experience.   Chances are that the webpage the retailer displays to you has been customized for your interests based on a variety of factors ranging from age and gender to purchase history and geography. And consider how this experience will mature with mobile devices, the Internet of Things, and apps when you visit a brick-and-mortar establishment or drive past a location that has a sale on a brand you like and as such you are alerted with a map, item photo, sale price, inventory, etc.

Finally, remember when getting car insurance was a few simple questions like make, model and year of vehicle, driving record and age? Now questions include marital status, number of children, your highest level of education and home ownership, because they can be measured against a statistical model to help develop a risk score and ultimately determine what you should be charged.


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