Top 7 QRadar Features for Machine Learning and Artificial Intelligence

QRadar Advisor  with Watson 

Leverages AI and natural language processing to identify and prioritize security incidents for analysts.


User Behavior  Analytics (UBA) 

Detects anomalous behavior by analyzing user activity and creates risk scores to prioritize incidents.


Risk Manager 

Uses machine learning to assess vulnerabilities and prioritize remediation efforts based on potential impact.


Advanced Analytics 

Applies statistical models and algorithms to network traffic to detect hidden threats and patterns.


Cognitive Insights 

Analyzes historical data and identifies patterns and anomalies to predict and prevent future security incidents.


Threat Intelligence 

Uses machine learning to correlate and analyze threat intelligence data from multiple sources to detect and respond to emerging threats.


Incident Forensics 

Provides detailed information about security incidents, including their source, scope, and impact, to aid in investigation and response efforts.


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