While much of the focus on emerging security analytics programs tends to fixate on the data science, algorithms, and technology that makes it all possible, people and process plays as much of a role in analytics as it does in any other facet of security. Many organizations today are learning that lesson the hard way as they find process-oriented impediments standing in the way of security analytics success.
Following are some of the common organizational mistakes that trip up enterprises.
Organizational silos block data flows
To get the full benefit out of a mature security analytics program, data scientists need to get their hands on a lot of contextual business data and IT operational data that doesn't come from security devices.
"Don't underestimate the importance of functional collaboration," says Jessica Gulick, chief strategist for Global Cyber solutions at CSG International. "Accurately correlating security data with business and IT analytics will promote a well-rounded approach."
Unfortunately, organizational structure can greatly impede the free flow of data.
"There [are] often these silos in larger organizations where the people who run the firewall or run the vulnerability scan might not even be in the security group; they're operations," says John Pescatore, director of emerging trends at SANS Institute. "Then the threat analytics guys are in the security group and they're using totally different tools, and the data found in the other silos never get banged into each other."
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