The Netskope Cloud Security PlatformMachine Learning Anomaly Detection

Netskope Machine Learning Anomaly Detection

Use adaptive machine learning and advanced rule engines to continuously analyze user behaviors and detect deviations that could indicate malicious activities.

Spot threats with user behavior analysis

Netskope’s machine learning, advanced rule engine, and an extensive set of predefined conditions analyze cloud and web traffic to spot anomalies that could indicate a threat. These anomalies can be prioritized by risk level, filtered, or drilled into to support investigations and appropriate action.

Top 5 machine learning anomaly use cases

01

Machine learning anomalies

  • Continuously analyze multiple dimensions of user behavior to create baselines of normal behavior. 
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02

Data anomalies

Understand how your data is moving in and out of cloud services and apps including account instances. 

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03

Credential anomalies

Detects potential credential misuse and remediates the risk of unauthorized access to your cloud services.

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04

Location anomalies

Reduce your attack surface by analyzing the geographic locations associated with your cloud and web usage. 

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05

Anomaly dashboard insights

Quickly detect anomaly-based risks across all your cloud services, apps and web traffic.

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It’s very reassuring to know that Netskope’s platform is constantly looking at patterns and strange behavior to detect threats from insiders and bad actors. We’re simply ill-equipped to handle this without this type of detection technology.

—Security Architect, Global Retailer

Trusted by leading companies

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