Knowledge Grid Cognitive Data Platform Service
The AI-ready data foundation for modern cybersecurity
Knowledge Grid's Cognitive Data Platform helps security teams, AI builders, and technology partners transform massive volumes of security telemetry into structured, time-aware, AI-queryable intelligence.
The Cognitive Data Platform is the broader environment for turning security telemetry into AI-ready knowledge.
The Temporal Data Grid is the underlying data layer that organizes time, behavior, relationships, representation, and uncertainty into machine-native structures.
Knowledge Grid · Cognitive Data Platform
Platform capabilities overview
Logs · Events · Alerts · Identity · Cloud · Network · Applications · Structured & unstructured
Time · Behavior · Relationships · Representation · Uncertainty
- Temporal Data Foundation
- Search & Exploration
- Dashboards & Reporting
- Data Management
- Cognitive Workbench
- AI & Natural Language (KGQL)
- External Integrations
- Agentic AI Workflows
AI agents & agentic workflows · Analysts via natural language · SIEM, SOAR, data lake and reporting handoff
Built for security teams, AI builders, and platform partners
The Knowledge Grid Cognitive Data Platform can support multiple deployment and use-case models:
Security Operations Teams
Direct ingestion and analysis of high-velocity security data.
AI & Cybersecurity R&D
A pre-built foundation for building custom AI query and analysis tools.
MSSP & MDR Providers
The underlying data engine for multi-tenant security services.
Platform Vendors
Embed KG intelligence to make external products AI-ready.
Technology Alliances
Standardized telemetry formats for data lake and lakehouse ecosystems.
How the Cognitive Data Platform Stacks Up Against Other Solutions
Security data infrastructure is evolving. Traditional platforms remain important for collection, storage, search, and operational workflows, but they were not originally designed to create AI-ready intelligence from complex, high-volume cybersecurity data.
Knowledge Grid complements and extends the modern security data stack by adding a cognitive data layer purpose-built for temporal context, relationship correlation, anomaly analysis, data noise reduction, and agentic AI support.
The comparison below illustrates how Knowledge Grid differs from common infrastructure categories and why a new data layer is needed to support AI-ready cybersecurity operations.
| Capability | SIEM / log management | Data lake / lakehouse | Knowledge Grid Cognitive Data Layer |
|---|---|---|---|
| Primary job | Collect, store, search and alert on events | Store and query data at scale | Transform telemetry into AI-ready knowledge at ingest |
| Context across entities, events and time | No: Reconstructed per query | No: Reconstructed per query | Yes: Preserved in the Temporal Data Grid |
| Behavioral baselines | No: Rules and thresholds | No: Not built in | Yes: Learned from your own telemetry |
| Unknown-threat discovery | No: Signature and rule based | No: Requires custom models | Yes: Unsupervised anomaly detection |
| Reusable knowledge for AI agents | No: Raw events and dashboards | No: Raw tables and schemas | Yes: Structured, context-rich data objects |
| Fits the existing stack | Yes: Stays in place | Yes: Stays in place | Yes: Runs alongside — no replacement |
KG Cognitive Data Platform — Core Capabilities
Temporal Data Foundation
KG's patented technology architecture designed for multi-tier data ingestion, approximate query processing, and high-performance log-line conversion at scale.
Security Data Search & Exploration
Rich, multi-vector exploration across structured and unstructured data, enabling high-speed forensic lookups alongside broad telemetry discovery.
Dashboards, Reporting & Visibility
Seamlessly visualize security posture, system performance, and operational trends with high-performance reporting and integrated dashboarding.
Data Management & Customization
Full control over ingest filters, normalization rules, and data routing to optimize storage costs and ensure high-fidelity telemetry delivery.
Cognitive Workbench
An embedded environment for running advanced statistical analysis, machine learning models, and complex behavioral threat detection workflows.
AI & Natural Language Enablement
Query security data using natural language (KGQL), enabling non-experts to build complex reports and derive insights through AI-assisted discourse.
External Integrations
A robust set of APIs and connectors designed to plug KG intelligence directly into existing SIEMs, SOARs, ticketing systems, and data lakes.
Agentic AI Workflow Support
Highly structured, context-rich data objects specifically formatted for Agentic AI reasoning, bypassing the bottlenecks of fragmented raw logs.
What the KG Cognitive Data Platform Provides
Knowledge Grid helps customers and partners get more value from security data by making it structured, contextual, reusable, and AI-ready.
Better Security Analytics
Analyze high-volume telemetry with structured, time-aware context instead of repeatedly reconstructing meaning across disconnected tools, tables, schemas, and indexes.
Unknown-Threat Discovery
Support unsupervised anomaly detection by identifying unusual combinations of behavior across users, devices, networks, applications, events, and time.
AI-Ready Context
Give AI agents and AI-driven workflows structured security knowledge that improves triage, investigation, reasoning, and decision support.
Reduced Data Friction
Create reusable knowledge structures that reduce dependence on repeated queries across fragmented SIEMs, data lakes, lakehouses, indexes, and tables.
Partner-Ready Delivery
Support MSSPs, MDRs, cybersecurity vendors, AI SOC vendors, and data/AI partners with platform capabilities they can use to deliver differentiated security services and solutions.
Pricing & Availability
The KG Cognitive Data Platform is being introduced through controlled early-access and pilot programs with qualified customers and partners.
For organizations interested in evaluating Knowledge Grid, we offer structured pilot discussions to determine fit, technical requirements, expected outcomes, and commercial terms. This allows us to align the platform to real-world customer environments while continuing to refine packaging and pricing ahead of broader availability.