The Cognitive Data Platform for AI-Ready Security Data
Traditional and modern data stacks store, index, and query data well. AI-driven security needs something they were not built to provide: reusable knowledge structures that preserve time, behavior, relationships, and context.
Knowledge Grid transforms fragmented security telemetry into structured, time-aware knowledge that AI systems can reason over directly. The Cognitive Data Layer sits between your telemetry sources and the upstream analytics, detection, SOC, and AI workflows that depend on it.
3 Technology Pillars — Integrated Learning System
Knowledge Grid's three technology pillars operate as an integrated learning system that turns raw security telemetry into better decisions over time.
Temporal Data Grid
First structures high-volume data into a compact, temporally aware foundation.
Learn more →PILLAR 02Cognitive Workbench
Then enriches that data with assets, behaviors, relationships, and environmental context to build durable knowledge of what is normal.
Learn more →PILLAR 03Anomaly Detection & Analysis
Applies that knowledge to identify, prioritize, and explain what is truly unusual.
Learn more →Every analyst verdict feeds back into the system, continuously refining the environment model, reducing false positives, accelerating investigations, and making detection more accurate with continued use.
Temporal Data Grid
Structures high-volume telemetry into a compact, temporally aware foundation.
Cognitive Workbench
Enriches that data with assets, behaviors, relationships and environmental context to build durable knowledge.
Anomaly Detection & Analysis
Applies that knowledge to identify, prioritize and explain what is truly unusual.
CONTINUOUS LEARNING LOOP
Each pillar hands richer knowledge to the next, and outcomes return to the foundation.
Why We're Different
Conventional telemetry stacks are optimized for ingestion and storage first. As data volumes grow, organizations are forced into expensive tradeoffs: retain less history, index only selected fields, parse only what is already known, or pay for ever-larger compute pools to keep queries acceptable.
The KG Cognitive Data Platform takes the opposite approach. It understands that the bottleneck is not only where the data is stored, but how it is represented. By converting data into compact summaries and contextual structures at ingestion time, the platform shifts cost away from post-processing analysis and repeated full-data searches toward reusable knowledge objects.
Structured Data Is Not the Same as Knowledge
TRADITIONAL & MODERN DATA STACKS
Data is stored. Context is lost.
FRAGMENTED TELEMETRY
Logs & EventsEndpoints / EDRIdentityNetwork / SecurityCloud PlatformsApplicationsDatabasesThird-Party ToolsTRADITIONAL DATA STACKS
Store · Index · Query · Report
COGNITIVE DATA LAYER
Data becomes structured, contextual, AI-ready knowledge.
COGNITIVE DATA LAYER
Context · Relationships · Behavior · Time
OUTCOMES FOR SECURITY & AI TEAMS
Faster, deeper investigationsHigher-fidelity anomaly detectionAI agents with better context and reasoningConsistent, reusable knowledge across use casesBetter security outcomesThe Cognitive Data Platform Makes Security Data More Useful — Improving Security Outcomes & Lowering Analysis Costs
SIEMs, data lakes, lakehouses, warehouses, databases, indexes, and cybersecurity platforms remain important parts of the security architecture. Knowledge Grid is not designed to replace them, but enhance them.
The issue is that these systems are generally optimized for storage, search, query, reporting, dashboards, and operations. Even when data is normalized, indexed, tabular, or columnar, it is not necessarily organized as reusable knowledge for AI-driven security reasoning.
Knowledge Grid complements existing data stacks by adding a Cognitive Data Layer that makes security data more useful for analytics, anomaly detection, and AI workflows, while lowering compute costs stemming from post-ingestion data re-processing.
AI Needs More than Data — It Needs Knowledge
AI is only as powerful as the data behind it — Knowledge Grid transforms fragmented telemetry into structured, high-signal knowledge, creating the data foundation required for accurate analytics, effective anomaly detection, and trusted agentic AI.
Knowledge Not Data
AI is only as effective as the data it learns from.
Better data doesn't just improve AI — it defines it.
Models are powerful, but data determines their accuracy.
Decision Making
AI can't reason without context — and context comes from data.
Agentic AI needs structured knowledge, not raw data.
Quality & Outcomes
Clean, structured data drives trustworthy AI outcomes.
Poor data can lead to confident but wrong decisions.
Built as the Foundation for Platform and Cybersecurity Use Cases
Knowledge Grid's platform use cases create the data foundation that supports higher-value cybersecurity services. The Cognitive Data Platform enables AI-ready data transformation, cybersecurity platform enhancement, and agentic SOC workflows. Built on top of that foundation, Knowledge Grid offers cybersecurity use cases such as Unsupervised Anomaly Detection and Security Data Analytics.
Platform Use Cases
Cybersecurity Use Cases Built on the Platform
Build the AI-Ready Foundation for Cybersecurity
Security data is too important to remain fragmented, noisy, and difficult for machines to use. Knowledge Grid transforms telemetry into structured knowledge so security teams, platforms, and AI workflows can detect, analyze, and act with better context.