When data science is thought of in a security context, it is natural to think of intrusion/anomaly detection, malware identification, or other operations-supporting solutions. But there are also benefits in using data science to support high-level security decisions and risk analysis.
Jay Jacobs dives into data from tens of thousands of organizations and shares techniques that pick out the relationships and identify patterns of risky behavior—once we start to find these indicators, we can actually test and prove what separates good from the mediocre when it comes to security.
Jay Jacobs is the senior data scientist at BitSight Technologies. Previously, Jay spent four years as the lead data analyst for the Verizon Data Breach Investigations Report. Jay is the coauthor of Data-Driven Security, which covers data analysis and visualizations for information security, and hosts the Data-Driven Security and R World News podcast. Jay is also a cofounder of the Society of Information Risk Analysts and currently serves on its board of directors. Jay is active in the R community; he coordinates his local R user group for the greater Minneapolis area and contributes to local events and functions supporting data analysis.
©2016, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • firstname.lastname@example.org