A cybersecurity process that takes note of the normal conduct of users and entities within a network and identifies any anomalous behavior that could indicate a security threat.
UEBA uses machine learning and statistical analysis to establish baselines of normal behavior for users and entities, then flags deviations that could indicate compromise, insider threats, or other security incidents. It's particularly effective at detecting subtle changes in behavior that rule-based systems might miss.
A quantitative method for assessing and ranking the likelihood and potential impact of security threats, typically assigning numerical values to help prioritize response efforts.
The identification of items, events, or observations that do not conform to an expected pattern or normal behavior in a dataset.
The systematic observation and analysis of user behavior patterns to identify potential security risks or policy violations.