Unsupervised Anaomaly Detection Service (UADS)
Find the Threats You Don't Know to Look For
Unsupervised Anomaly Detection Service helps to identify unknown, emerging, and hidden security threats across massive cybersecurity datasets.
Based on the Cognitive Data Grid, UADS analyzes the shape, relationships, frequency shifts, and temporal behavior of your security data to surface high-value anomalies without relying on predefined rules, labels, signatures, or known threat patterns.
Threat detection today is focused on the known, leaving a critical visibility gap in the face of machine-speed evolution.
The modern security landscape is saturated with threats that have not yet been recognized by traditional repositories. Most detection stacks rely on known indicators, yet AI has drastically accelerated the rate at which zero-day vulnerabilities are identified and weaponized.
New threats, such as Anthropic Mithos, are quickly exploited before rules can be written. This creates a dangerous lag between the emergence of a novel attack path and the ability of indicator-based models to respond, leaving enterprises exposed to the reality of the unknown.
How UAD Works
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Analyzes security data without rules, labels, or signatures to find threats teams did not know to search for.
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Detects meaningful changes in behavior, frequency, relationships, and patterns across large volumes of structured and unstructured telemetry.
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Uses Knowledge Grid’s Cognitive Data Model to evaluate high-dimensional feature combinations more efficiently than traditional search.
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Prioritizes anomalies with severity and credibility scoring so analysts can focus on the findings that matter most.
UAD Processing Flow
The result is a smarter anomaly analysis layer that surfaces unknown, hidden, and emerging threats from the data you already collect.
Knowledge Grid’s Unsupervised Anomaly Detection Service is focused on the unknown unknowns.
UADS operates as an AI-native detection layer that moves beyond the limitations of static rules. By deploying unsupervised machine learning models across rich temporal security data, the service establishes a baseline of normal behavior unique to your environment without requiring prior knowledge of attack patterns or historical labels.
The core of the solution lies in its ability to surface behavioral anomalies at scale. Rather than searching for a needle in a haystack of known signatures, UADS analyzes complex relationships and shifts in data flow to identify novel attack paths and zero-day execution before they are classified by traditional security stacks.
UADS bridges the gap between known-indicator security and emerging behavioral threats.
Emerging Threat Detection
Identify new attack techniques as they emerge, before they are documented or assigned signatures.
Zero-Day Coverage
Detect novel exploit paths and Anthropic Mithos-style vulnerabilities that bypass rule-based models.
Eliminate Blind Spots
Connect behavioral anomalies across tool silos to eliminate the gaps between SIEM, EDR, and NDR.
High-Quality Signals
Reduce investigation time with high-fidelity, contextual anomaly signals optimized for technical teams.
Unsupervised Anomaly Detection Service
Our Unsupervised Anomaly Detection Service is designed to uncover the threats, behaviors, and operational changes that traditional rule-based tools typically miss. We focus on the Unknown Unknowns.
By analyzing large volumes of structured and unstructured data without requiring predefined signatures or labeled training sets, our service identifies meaningful deviations, rare patterns, and emerging activity hidden within complex environments. The result is a more adaptive, scalable approach to detection that helps organizations surface high-value anomalies earlier, reduce noise, and strengthen security operations.
Core Features
Service Plan Comparison
Essential
Entry Level Affordability for getting started with essential capabilities
Premium
Advanced capabilities with improved features to optimize detection capabilities with expanded models
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Signatureless detection for unknown and emerging threats
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Behavior-based analysis across structured and unstructured data
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Temporal anomaly discovery that reveals change over time
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High-signal prioritization to reduce noise and speed investigation