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Machine Learning in Cybersecurity

How ML Adds Value for Cloud Security

Just as machine learning (ML) has become a growing trend in healthcare and manufacturing, the same is true for cybersecurity. In combination with human expertise, well-trained machine learning tools help make sense out of a sea of data by detecting anomalies as well as patterns and behaviors that indicate potential attacks in progress, enabling Security Operations teams to act quickly and mitigate damage from a cybersecurity event.

One easy way to illustrate the value of machine learning in cybersecurity is to share concrete examples of how this technology helps SecOps work faster and smarter in the cloud.

Automated Prioritization of Security Threats

Threat events in the cloud, such as a compromised account, have increased by more than 27% year-over-year. While it's clear there are more threats to address, not all carry the same risk, and with development teams moving quickly, it's crucial that SecOps is able to prioritize investigation and response resources.

While manually sifting through potential security threats can be time-consuming and prove ineffective due to false alarms, advanced machine learning intelligently prioritizes these risks based on defined algorithms and effectively reduces the number of false-positives. This also brings behaviors that suggest larger cloud security vulnerabilities, such as insecure APIs or misconfigurations, to the top of the list so SecOps can attack pressing security issues first and attend to other matters afterward.

Streamlined Access to Comprehensive Data Sets

It's one thing to be alerted of a threat; it's another thing to understand the nature of it. Where did the issue first start? What are all the applications and devices involved in this conversation?

Connecting these dots can prove difficult from a manual standpoint—especially when you've got a hybrid environment and different interfaces to jump between. Alternatively, leading machine learning tools are trained to correlate and collect relevant event data in a single environment. A comprehensive view of a timeline of detections, device details, and transaction records (among other elements) provides SecOps with the clear picture they need to address the cybersecurity issue without any useful information falling through the cracks.

Prescribed Next Steps to Quickly Resolve Issues

On average, it takes organizations 191 days to identify a data breach. While this is due in part to a lack of visibility into the hybrid or cloud environment that hides cybersecurity attacks, it's also a matter of a long investigation process. A lot of this ties back to a lack of contextual information.

Today's leading machine learning cybersecurity tools are designed to provide recommended next steps to guide the investigation. In just a few clicks, you can have access to an associated digital instruction packet that erases several hours SecOps could spend finding these answers manually. And because most cyberattacks tend to follow similar patterns, the answer to one issue could be the catalyst to lateral changes that help prevent future security breaches.

ExtraHop Reveal(x) Cloud combines all of the value of machine learning cybersecurity in a single product for cloud-based network detection and response (NDR)—an integral part of the Shared Responsibility Model. To learn more about how this cloud-native, SaaS-based solution can improve your security operations, visit the Reveal(x) Cloud product page.

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