Last week the world learned that the hacks at Target hit more customers than originally thought – somewhere in the 100 million vicinity – and that Neiman Marcus also saw customer credit card information spirited away by data thieves. They’re not the first big-name outfits to suffer a security setback, could they be the last?
No one can ever say never, of course. But it’s possible that new tools that leverage machine learning predictive analytics could put a serious dent in the black hats’ handiwork, while also improving IT’s hand at application performance management.
A big problem in both the APM and security space today is that there’s just a ton of data coming at IT pros dealing with those issues, much of it just describing the normal state of affairs, and no one’s got time to spend reviewing that. What IT staffers want to know about are problems, which leads to a lot of rules-writing to identify thresholds that could point to issues, and to a lot of rewriting of those rules to account for the fact that things change fast in today’s world of system complexity – and to a lot of misses because of the impossibility of keeping up. Sixty percent of problems are still reported by users, not the tools IT is using, says Kevin Conklin, marketing vp at Prelert, whose machine learning predictive analytics technology is used in CA’s Application Behavior Analytics and available as Anomaly Detective for the Splunk IT apps ecosystem.