All I Want for the Fiscal Year End Holiday is an Optimized Security Group

In one of our recent posts, Rob Sobers talked a bit about the Varonis recommendations engine and how it compares with’s similar technology. I’m currently in the throes of last minute holiday shopping, and one of the things I find myself grateful for is Amazon’s recommendations engine. By analyzing the behavior of its busy shoppers, Amazon can point me towards items that might be of interest based on what I’ve been looking at. If I’m checking out some kitchen gadgets, Amazon will start to populate its pages with other things I might be interested in, like relevant cookbooks. The site is analyzing the activity of millions of users and then making actionable recommendations for me based on all of that behavior.  It’s not just browsing behavior, either. Amazon is able to use information from purchases over time, user wishlists and other relevant metadata to come up with these recommendations.

In an age of virtually unlimited choice, we can easily find ourselves trying to weigh the pros and cons from long lists of items that begin to blur together, making what would normally be a pleasant experience seem overwhelming.  Amazon makes my shopping easier, helping narrow down the choices I have and helping me figure out where I should best spend my energy.

Whether the behavioral analysis is being done on shopping sites, search engines or within your data center, leveraging metadata through automation is a crucial technique for getting better, more actionable information. Varonis helps IT administrators and data owners by providing recommendations on where users have access they likely no longer need. Varonis looks at permissions, user and group relations and access activity on multiple platforms over time in order to produce this analysis.

Big data analytics like this is changing how we make these kinds of decisions by giving us information that was impossible before automation. If you’re considering trying to clean up Active Directory membership next year, think how easy it would be if you had an accurate recommendations engine to get you started.