Reduce False Positives
Netra uses full contextual analysis and agentic AI triage to eliminate noise, surface real incidents, and help security teams respond with confidence.
The Challenge
Too Many False Positives
Traditional DLP generates massive volumes of false positives, creating analyst fatigue and making real risks harder to find and investigate.
The Solution
How Netra Reduces Noise
Netra collects and correlates the full context around each event — including user behavior, data lineage, and the full actions of AI agents — then uses agentic AI to triage alerts and remove benign noise. Over time, Netra's AI agents also learn from analyst feedback and fine-tune policies to improve precision and reduce false positives even further.
The Capabilities
Key Features
Contextual AI Triage
AI analyzes alerts with full context — including user behavior, data flows, and application activity — to filter benign events and prioritize real incidents.
Correlated Risk Analysis
AI correlates user behaviors, data lineage, and content recognition to understand what happened, reason the user intent and identify genuine security risks.
Adaptive Policy Tuning
AI agents learn from analyst feedback and investigation outcomes to continuously refine policies and reduce recurring false positives.
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Your Organization?
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