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Transforming Noise
to Knowledge

With security teams stretched thin and facing an ever-growing stream of threat data, today’s analysts are overwhelmed.

The average organization faces 200,000 security events
a day
users
cloud
network
assets

A stream of 
threats and vulnerabilities

Security analysts are faced with thousands of vulnerabilities, threats and attacks each day. QRadar cuts through the network noise to focus on the security events that matter so that security teams can swiftly take actions to defend against them.

Interpret Data
Normalize log and
network flow data in a consistent format for more robust analysis.
Profile Activity
Baseline asset, user,
service and network activity to learn normal patterns and enable accurate
anomaly detection
Alert
Fuse related signals uncovered during analysis to establish the end-to-end chain of a security event, determine the severity of the event and generate a single alert.
Advanced Analytics
in Real-Time
Select an advanced analytics feature to learn more.
Threat Intelligence
Compares event attributes against up-to-date threat information, such as malicious domains or hashes, to more accurately identify
the latest known threats.
Threat Intelligence
+
Entity Behavior Analytics
Continuously monitors machine
entities for anomalous behaviors, services and connections to more effectively detect compromised systems.
Entity Behavior Analytics
+
Peer Group Clustering
Clusters users into peer groups based on similar activities, and continuously looks for anomalous behaviors to more quickly and accurately uncover high-risk or malicious users.
Peer
Group Clustering
+
User Behavior Analysis
Continuously analyzes individual user behavior to detect deviations
that can help identify compromised user credentials and malicious insider activity.
User
Behavior Analysis
+
Forecasting Analytics
Uses a behavioral forecasting model to predict future behaviors and detect when actions or behaviors deviate from what’s expected.
Forecasting Analysis
+
Statistical
Analysis
Statistically analyzes entity behavior to help identify
outlying, potentially compromised systems, such as endpoints sending abnormally large amounts of data to unauthorized cloud services.
Statistical Analysis
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Historical Analysis
Many attackers skip normal steps in an attempt to breach systems. When certain actions are not preceded by expected behavior,
historical analysis can flag them for attention.
Historical Analysis
+
Threshold Analysis
Analyzes activity volumes to identify deviations from the norm, such as increases or decreases across things like bandwidth or service usage.
Threshold Analysis
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Anomaly Detection
Detects “normal” behavioral patterns over time and identifies deviations from the known normal that may indicate a threat.
Anomoly Detection
+
Pattern Analysis
Analyzes event attributes in real-time against patterns of known
malicious activities to quickly identify and classify active threats.
Pattern Analysis
+
Investigate
Root
Cause
Cognitive Reasoning
Uses natural language processing to automatically create knowledge graphs, which are then used to determine the root cause, provide an attack overview and identify related IOCs.
Threat Details