Cyberattacks come from a variety of sources. Attackers look to breach security firewalls setup by organizations through the slightest of available openings. These can range from:
proliferation of endpoints that is dominated by the increasing spread of IoT enabled devices and networks to..
employees creating Shadow IT scenarios, by signing up for a variety of cloud services to gainfully get their work done while (unwittingly) exposing their organizations to malicious attacks.
As organizations battle multiplying complex threats to their data and sensitive information, they are forced to face an unsettling fact: in many cases, the threat originates from the inside, with a trusted user. To make matters worse, harmful insiders are almost impossible to detect, because they have legitimate access to valuable data in the normal course of their jobs.
Most security products in organizations today work in siloes, producing mountains of disconnected data. As a result, these tools are not able to interconnect the data from various sources to detect abnormal behavioral changes of legitimate users, allowing insiders continue to operate undetected.
Don’t miss Sudeep Das’ Learning Lab session at 10:50 AM on Nov 12th, at the Gartner Symposium in Goa, India to learn how you can automate the detection of such rogue insiders and potentially compromised users to quickly contain insider threats and limit their impact using machine learning and behavioral analytics.
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