4 trends in security data science for 2018

A glimpse into what lies ahead for response automation, model compliance, and repeatable experiments. This is the third consecutive year I’ve tried to read the tea leaves for security analytics. Last year’s trends post manifested well: from a rise in adversarial machine learning (ML) to the deep learning craze (such that entire conferences are now dedicated to this subject). This year, Hyrum Anderson, technical director of data science from Endgame, joins me in calling out the trends in security data science for 2018. We present a 360-degree view of the security data science landscape—from unicorn startups to established enterprises. The format remains mostly remains the same: four trends to map to each quarter of the year. For each trend, we provide a rationale about why the time is right to capitalize on the trend, offer practical tips on what you can do now to join the conversation, and include
Tiered modeling models each geographic region separately
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