Stay Ahead of the Latest Threats with Intel Driven Security Operations

Understanding the threat landscape and what it means for your organization is the cornerstone of establishing a modern approach to threat detection, investigation, and response (TDIR). However, achieving this dynamic, modern approach with traditional SIEMs has proven difficult. Why? Data overload—far too much information to make use of strategically within the organization—and lack of effectively applied threat intelligence.

Traditional SIEMs are also not typically thought of as outcome oriented products. They’re very good at collecting data and providing a way to search that data, but ultimately require the SecOps team to connect the dots and apply what is needed in order to understand what is happening in the organization's environment. 

As a result, many security teams struggle to understand which constitutes a real threat and find it difficult to determine which steps to take to respond more effectively to the ever-changing threat landscape. It also places SecOps teams in an awkward position when it’s time to answer the tough questions from the C-suite and translate security risks into business risks.

This paper examines why SecOps teams struggle to effectively operationalize threat intelligence to deliver the security outcomes their organizations require. Readers will also learn how a modern, intel-driven SOC that leverages applied threat intelligence enables organizations to keep up with the changing threat landscape and reduce risk. Why SecOps Teams Struggle with Threat Intelligence There are a few challenges when it comes to traditional threat intelligence. What most people do in practice today is try to stitch together the information that they're collecting or buying from third-party data sources with their existing SIEM. And the natural questions are “why isn't that good enough, why doesn't that work right”?  There are a few reasons why the current status quo of trying to connect the data between these two tools doesn't result in optimal outcomes.