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.

1

User Behavior  Analytics (UBA) 

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

2

Risk Manager 

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

3

Advanced Analytics 

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

4

Cognitive Insights 

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

5

Threat Intelligence 

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

6

Incident Forensics 

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

7

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