Machine learning and data science can make a complex device easier to use

“Up popped an alert when an employee installed Tor, software that enables online anonymity, which is sometimes used by cybercriminals to mask their whereabouts. Had the same user’s device made another suspicious move — like scanning the network for an administrator’s account and then trying to guess at her password by trying to log in multiple times — (this new security) tool would highlight the employee’s computer and inform a security officer that the employee was a threat with a high degree of certainty. Had the odd behavior stopped at Tor, it would have also been flagged, but not been perceived as an immediate threat”
“a security solution (must) provide enough credible intelligence to investigate thoroughly, so we’re not bogged down with false positives and unnecessary work”
“(this new security) tool uses machine learning and data science to listen, think and anticipate an attacker’s next move. It learns the typical traffic patterns and behaviors on a network, then remembers and correlates any abnormal behavior it has seen over days, weeks or months.”
“An employee who inadvertently clicked on an ad and installed tracking software would be flagged as a low priority and remediation issue, whereas an infected device that was being used to pull data out of the network would pop up as a high-threat priority.”
“If a complex device like the iPhone can be made easier to use, why can’t you extend that to a security product?”


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