Every day users log into corporate computers when in the office during business hours and even remotely from home, but when someone logs on at 2am and starts running database queries is it legit or a hacker? Maybe it’s your pimple faced database administrator performing after hours maintenance, but maybe it isn’t. Old school perimeter defenses are no longer adequate in the new age of hacking.
But by using User Behavior Analytics we can eliminate this guesswork using these machine learning algorithms to assess the risk in real-time. Is it normal or is it a risk?
Risk = Likelihood x Impact
Likelihood = refers to the probability that the user behavior in question is anomalous. It is determined by behavior modeling algorithms.
Impact = is based on the classification and criticality of the information accessed, and what controls have been imposed on that data.
User Behavior Analytics is transforming security and fraud management because it enables enterprises to detect when legitimate user accounts/identities have been compromised by external attackers or are being abused by insiders for malicious purposes.