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Veriato AI & Machine Learning

User & Entity Behavior Analytics (UEBA)

Powered by advanced machine learning, statistical analysis, and natural language processing, Veriato autonomously creates an integrated user view, by analyzing both structured and unstructured data aggregated from various sources. This data is then used to rank risky users in a watch list which prioritizes, predicts and prevents potential threats.

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Creating a Digital Finger Print

Self-learning of behavioral patterns for both individuals and groups, driven by advanced machine learning, enables no-touch understanding of what normal looks like in your environment.

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Grouping

Import groups from Active Directory, or let the software autonomously identify groups within your organization through pattern analysis of resource and application usage.

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Low False Positives

Veriato tests against the CERT dataset and consistently has <2% false positives.

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Predicting Future Threats

Disgruntled employees are not born overnight. Cerebral’s Sentiment Analysis uses computational linguistic analysis, to identify and categorize opinions expressed in text. Cerebral determines whether the writer's sentiment towards the company is changing in either a positive or negative direction and can alert security of a possible risk. Sentiment analysis gives you the ability to intervene at an HR level; long before their actions reach an unethical or criminal level.

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Anomaly Detection

Cerebral takes into consideration statistical anomalies and applies machine learning to them to find unique deviations from the baseline anomalies.  Detecting deviations from established patterns enables early warning of insider threats. An outside attacker, no matter how sophisticated, will cause a deviation from normal behavior.

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Risk Scoring

Actions and activities all play a part in building a comprehensive Risk Score for each employee on your network. The score’s are based on all online behaviors, from files downloaded to geolocation. Once an employee reaches a certain threat threshold set by your security team, an alert is sent and action can be taken.

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Investigational Efficiency

Risk scores and alerts not only provide immediate notice to threats that may never have been noticed, but they also bring massive efficiencies to a security department. Security teams no longer need to continually sift through mountains of data trying to deduce if a breach occurred in the past. Efforts can be concentrated on investigating actual breaches real time or even preempting breaches through early intervention

"Advanced forensic data analytics is becoming an indispensable tool to detect insider threats"

-  Ernst & Young Managing Insider Threats 2016

 

 

End Point Data is Big Data

Cerebral continually monitors all the actions on every endpoint, including files, applications, network use, email, web, geolocation, psycholinguistics, signs of compromised credentials and more. The volume of data that needs to correlated, analyzed, cross analyzed against dynamic baselines and group behaviors is enormous.

To continually assimilate this dynamic data stream coming from every user and pick out fluctuating patterns of behavior and signs of threat isn’t possible by even the best security teams. A mature insider threat strategy requires machine learning and AI.