User Behavior Analytics (UBA)

Builds behavioral profiles and detects anomalous activities that traditional SIEM/DLP systems may miss.

Why It Matters

Hidden & Slow Deviations;

Noise Reduction;

Atypical Relationships;

Shift to Proactive Security.

How It Works

Collects events

Establishes baseline

Compares

Detects anomaly

Key Benefits

We use advanced machine learning methods to identify patterns that static rules cannot detect

ML Models
A combination of outlier-detection algorithms increases accuracy and uncovers subtle user deviations
Explainable AI (XAI)
Each alert is given a clear explanation of why it was triggered — showing exactly which deviation caused the incident
Low False Positives (FP)
Models learn continuously, reducing false alerts so analysts can focus on real threats

Measured Outcomes

Context over Rules:
Actions are evaluated against historical behavior, not static thresholds.

Agentless Operation:
Easy deployment with minimal impact on workstation performance.

Adaptability:
Self-learning models that adjust to your company’s unique environment.

Transparency:
Explainable alerts (XAI) enable fast, confident decision-making.

Scalability:
Flexible integration with any infrastructure and response systems.

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User Behavior Analytics (UBA)

Why It Matters

How It Works

Collects events Establishes baseline Compares Detects anomaly

Establishes baseline

Compares

Detects anomaly

Key Benefits

Measured Outcomes

Context over Rules:

Agentless Operation:

Adaptability:

Transparency:

Scalability:

Collects events

Establishes baseline

Compares

Detects anomaly