If there's one thing we know for sure about technology, it's that innovation moves fast, but frequent innovation can be problematic for security -- especially for big data platforms.
As large organizations pull more data from disparate sources, they are realizing too late that their security solutions don't provide adequate protection. The need for advanced big data technology, however, is urgent due to its potential: organizations can save millions of dollars and ease huge operational burdens thanks to its capabilities, but these benefits shouldn't come at the expense of security.

How to Smartly Secure Your Big Data Projects 

Big data platforms have unique security requirements because of the huge volumes of disparate information involved. They operate in a distributed architecture format -- not a traditional mainframe architecture system in which only one server needs to be secured.

The stakes for securing data are high and new innovations are coming at the speed of light. What's a forward-thinking, big-data-minded organization to do?

Step 1: Get security in the room from the start

It's the security team's responsibility to provide their expertise at each step in the development process. The security team must be approachable, open with their knowledge, and committed to finding a custom solution for securing big data technologies. However, it's incumbent on the data analytics team to invite the security team into the project early and keep them informed at every step.

Organizations run into trouble when the data analytics team views the security team as "killjoys" or when they intentionally cut security out of the early stages of the process based on a belief that security implications will limit innovation. Security teams brought into the process late are often frustrated by the fact that development is too far along to incorporate any meaningful security strategies. In many cases, security teams let developers off the hook because there's no realistic way to adequately secure the tool at such a late stage.

The data analytics team must recognize its obligation to incorporate robust security measures into its big data innovations, even if it means reeling in their technology plans.

Step 2: Start with objectives, not solutions 

Too often, security teams prescribe simplistic solutions as requirements for any system an organization uses, despite the fact those solutions won't successfully protect complex big data systems. Adequate big data security differs from normal operations and cannot be subjected to the same standard. In fact, a big data environment can't be secured with just one solution. Organizations must understand that only a customized blend of tactics has a chance at completely managing the risk.

Security teams should adjust their thinking when it comes to big data security efforts. They can begin by asking what specific security objectives your team is trying to achieve. From there, they can work backwards to find the custom and alternative solutions to secure your environment.

Read the full article published in Upside.