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The key to predicting threats, especially unknown threats, is to monitor user and entity behavior – to recognize when that behavior starts being anomalous.
For UEBA, analytics transforms user and entity behavior data into risk-prioritized intelligence, for the purpose of driving business action.
The Gurucul Outlier Categorical Model gives the relative probability of an occurrence being an outlier based on prior observations.
The Gurucul Identity Classification Machine Learning Model is a supervised learning approach that sorts identities with similar attributes into buckets.
As we look back at 2018, which Gurucul blog posts had the most views? This tells us what resonated with you. Here are the top 10 blog posts of 2018.
The dramatically increased demand for Incident Response personnel is real, and will be with us for the foreseeable future.
Fraud is a massive problem: card not present, ID theft, synthetic ID, social engineering, phishing, real-time payment fraud, call center fraud, ATM fraud – the list goes on and on.
The Gurucul Risk Analytics Link Analysis machine learning model links behavior associated with disparate accounts to one particular user or machine.
The Gurucul Feature Analysis machine learning model examines data sets and separates out good data from bad data for high-end machine learning scenarios.