Protecting Big Data for Your Company
With the growing demand for big data comes the growing demand to secure it. Network solutions such as firewalls or employee security awareness are still valid, but traditional database solutions such as permission levels or encryption are much less valid when you're talking about enormous data sets serving a specialized purpose.
Securing your big data archives is more of a challenge than securing your traditional databases. For one thing, obviously, it's bigger, and with today's multiple channels of data capture both on and offline it's only going to get a lot bigger, much faster. Setting aside for a moment the performance issues resulting from security measures such as encryption, there's significant impact on disaster recovery when something goes wrong.
Big data protection policies must take into account the size, cost, and efficiency of backup-recovery solutions, as well as the need for frequently loading masses of historical data into your business intelligence models. Full data backups require too much time, and disk volumes may not be adequate to storing petabytes of information, especially considering 48% of data is semi-structured or unstructured. That's a lot of hardware when you think about maintaining copies of that huge data archive.
Neither are software solutions effective on these huge data sets, as running scans of all that diverse information for injection attacks or malicious code is also time consuming when it's done on a regular basis. Replication and encryption of data also eats up already-strained resources and affects performance. Blue Coat System's Facebook page can suggest some excellent solutions, but most companies aren't even thinking security issues when they think big data.
Video and audio files may lie forgotten for months or years before new issues bring a need for them. Photos and documents on discontinued products may suddenly become useful again if upper management decides the time is right for engineering an upgraded version. While aging data becomes less relevant to analytics, there may come a situation where it's needed. Archiving aged data to other data stores doesn't eliminate the hardware and performance issues, but, when that data is needed, compounds it.