Most of us are familiar with the challenge of locating a particular red-and-white-striped character within a sea of colorful figures. Whether you’ve searched for this peculiar gentleman—let’s call him Waldy—with your children or by yourself in the dentist’s waiting room, you’re well-versed in the mission and its challenges.
Finding a bad guy within an organization is a similar mission. Within a sea of data lies clues to a serious threat. So how do you cut through the noise to find the one actor who is up to no good?
For today’s security operations centers (SOCs), finding real threats quickly is more difficult than ever before. There are more threats, more data to sift through, and less time to catch the bad guys.
Imagine this. You’re operating in a SOC team for a billion-dollar enterprise. Every device and business system is yielding massive amounts of data on a daily basis. Somewhere within thousands, millions, or even billions of these data points lies a small number of clues about a malicious actor trying to steal high-value information. It’s your job to sift through all of the data and find the threat—before it’s too late.
But doing this manually will take days or even months, and the security software you’ve deployed to help you sift through all of this data keeps flooding you with alerts that send you and your time-strapped teammates on a wild goose chase. However, you know what’s good at sifting through massive amounts of data looking for patterns? Computers!
Many of you reading this blog may not have to imagine this scenario. For you, this is day-to-day life. And you’re well aware that the job is too much for a human to do alone.
Rules-based security tools that leverage correlation technology play can play a critical role in your SOC. For known threats, correlation continues to be the most effective and efficient method of detection. It’s important to recognize, however, that these technologies can miss threats designed to fly under the radar due to limitations in analyzing and adapting to dynamic user behavior.
Anomaly detection powered by machine learning has the potential to transform threat detection in the SOC and boost the efficiency and speed of detecting, triaging, investigating, and responding to both known and unknown threats. Known as user and entity behavioral analytics (UEBA), this technology leverages hundreds of machine learning models to analyze vast quantities of events to understand what is “normal” behavior for every entity (every user, every machine, every printer, every IP address, etc.) in your organization. This baseline is then used to evaluate potential risk as new events and behaviors from each entity comes in.
For each entity, UEBA learns hundreds of different baseline types, compares it to hundreds of thousands of other entities, evaluates it with millions and billions of events and individual clues, and generates a single, simple risk score between 0 to 100 that can be used to compare against all the other potential threats in your organization. Suddenly, billions of data points turn into just a handful of prioritized threat leads.
UEBA finds the threats that matter for enterprises with valuable assets to protect, limited security or financial resources, and significant surface area to monitor. With this approach, SOC teams can analyze more data, detect threats faster and more efficiently, begin remediation sooner, and free up valuable time lost to manual tasks and chasing alerts and false positives.
Learn how Micro Focus can protect your business by arming your SOC with powerful machine learning by visiting microfocus.com/interset.
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