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Big Data

Cyber Security Has Become a Big Data Problem

Why no one else is searching all your data for security threats

 

The Challenge

Cyber security has become a big data problem. Most organizations generate anywhere from 5 million to 5 billion or more logs every day.  As your organization continues to change and to grow, your data volume is also increasing, putting a huge strain on the internal IT or Security team, who must quickly process and analyze massive amounts of data.

Finding enough storage for all your log data and budgeting enough time to manage it all is the least of your worries. Today’s threat detection tools (known as Security Incident and Event Management or “SIEM” tools) are stuck in a 15-year-old analysis paradigm.  SIEM technology was not originally designed to handle today’s massive data volumes, so SIEM tools can only process a smaller, manageable portion of your data.

To compensate for its lack of computing power, a SIEM tool reduces the data down to smaller datasets by eliminating data, which the SIEM vendor or installer decides is superfluous and not needed. Unfortunately, this introduces bias into the analysis and eliminates potential indicators of compromise (IOCs) that would normally be available for correlation and searching.  This data reduction approach has been deemed “good enough” for the past two decades, however, as threat actors continue to advance and innovate their attacks, more and more companies will discover that their SIEM or other cyber tools are inadequate at finding cyber threats.

Another complicating factor is that most third party SIEM vendors license their tools based on how much log volume is created, stored, or analyzed.  This further disincentivizes organizations from analyzing all of their data for threats, and so only subsets or reduced data from fewer devices are included in the analysis scope.

 

How to Improve your Cyber Security

If you currently utilize a SIEM tool, either directly or through a managed provider, you can minimize some risk in the short-run by ensuring that your most precious data on your devices is sending many logs as possible to be analyzed.

In the long-run, you can minimize a lot of risk by moving from a SIEM tool to an advanced Behavioral Analytics platform that can analyze compounding data quickly, so no data is left behind.

Another benefit to a behavioral analytics platform is the ability to search all the data for unknown threats, anomalies, and dynamic threats in addition to the static threats that SIEMs find.

 

How do Behavioral Analytics Platforms Work?

Specifically, the ThreatWatch Behavioral Analytics platform built by Security On-Demand uses algorithms and advanced analytics to correlate events and find the hidden threats within the data.

ThreatWatch can analyze the entirety of the data in hours instead of days and weeks, taking an unbiased look at the data and then comparing it to yesterday and the day before. We call this Unsupervised Anomaly Detection, which allows us to find unknown threats, giving us a unique and important advantage over a SIEM tool.

Behavioral Analytics platforms are smarter a SIEM and filter out many of the false positives that bog down most IT teams, giving you more flexibility and more bandwidth to focus on the alerts that matter.

Though this is not the case for all behavioral analytics platforms, the pricing model tends to be more reasonable, like paying per device instead of per log. This enables businesses to analyze more of their devices at a lower pricepoint without penalizing the increasing volume of logs over time.

Ultimately, SOD’s ThreatWatch platform allows us to search through all the data, and find threats faster and with greater accuracy, so we find more threats than others.

 

Conclusion

Protecting your most important data matters. By upgrading your SIEM tool to a Behavioral Analytics platform, you can search all your data for threats quickly and with greater accuracy. For more information or questions, please contact us here.