The AI Data Layer for Cyber Security
Knowledge Grid transforms raw data telemetry into structured, high-signal knowledge that improves analytics, supports autonomous workflows, and helps uncover threats traditional systems miss.
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Make security telemetry AI-ready without replacing your stack
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Provides the missing intelligence layer for cyber AI
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Reduce Time to Detect with superior data context
Security Data was Not Built for AI
Most security stacks were designed for storage, search, dashboards, and human investigation. They were not built to convert massive, fragmented telemetry into machine-usable knowledge for AI-driven analysis and autonomous operations.
The Architecture Problem
Traditional Data Stacks
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Traditional data pipelines were built for human analysts, dashboards, and rule-based detections.
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AI requires something different: structured relationships, temporal context, and optimized data representations that help models understand what changed, what matters, and what to prioritize.
The Ops Problem
Too Much Data, Too Little Context
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Security teams have no shortage of data, but most of it lacks the structure, correlation, and temporal context AI needs to generate reliable insights.
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Without that optimization layer, AI models are forced to process noisy inputs that reduce accuracy, slow down outputs and increase cost.
The analytics Problem
Data is Fragmented Across Too Many Systems
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Critical security insights are spread across multiple tools, formats, and storage layers.
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This fragmentation makes it difficult for AI to reason across the full picture, connect patterns over time, and produce reliable outputs.
Built for Machine Reasoning at Scale
Data Pipelines, SEIMs, databases, data lakes & warehouses) were built built to supply data to dashboards and analysis to humans, not to machines performing AI.
Temporal Intelligence
High-Signal Data Structures
Better Detection and AI Outcomes
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Knowledge Grid stores semantic, time-based patterns and relationships that matter in cybersecurity.
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Feature summaries, histograms, and correlations reduce noise and make data more useful AI workflows & LLM ingestion.
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Support faster investigation, stronger context, and discovery of unknown unknowns.
Section: Core use cases
Purpose-built for Cyber Security Outcomes
Security Analytics
Unsupervised Anomaly Detection
Vendor Security Platforms
Agentic AI SOC & Orchestration Workflows
Built by a team with deep Data Science, Big Data & Cyber expertise
Knowledge Grid is supported by our expert research and development team based in Warsaw, Poland.
We are the world's leading experts in Rough Set Mathematics Theory, which underlies our Temporal Data Grid, anomaly detection, and advanced data structures designed to improve how machines analyze and interpret complex data.