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Knowledge Grid FAQs: The AI Data Platform for Cybersecurity
Knowledge Grid introduces a new approach to preparing cybersecurity data for AI, analytics, and advanced detection. Because the platform occupies a different layer of the security data stack, it is natural to have questions about what it does, how it works, and where it fits.
Explore answers to common questions about the Knowledge Grid Cognitive Data Platform, our services, integrations, deployment model, and approach to AI-ready cybersecurity data.
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Technical FAQ & Platform Infrastructure
Deep dive into the architecture of the Cognitive Data Grid, Rough Set Theory applications, and how we enable Agentic AI for modern cybersecurity stacks.
What is a Cognitive Data Grid?
How does Rough Set Theory enhance anomaly detection?
Is Knowledge Grid a SIEM replacement?
What are the performance metrics at scale?
How does it support Agentic SOC workflows?
How is the KG Cognitive Data Platform different from a SIEM or log management tool?
How is this different from a data lake or security data platform?
Why can't I just point an LLM at my existing security data?
Is the technology proven?
How does this complement an existing MDR or MSSP offering?
How does the partner program work?
A Cognitive Data Grid is a new category of security data infrastructure that transforms high-velocity, continuously changing security telemetry into AI-ready data at the moment of ingest. Traditional systems store raw logs and force analytics tools — and AI — to make sense of them after the fact. A Cognitive Data Grid does the structuring and enrichment upfront, so the data feeding your detection, investigation, and AI workflows is already organized for machine reasoning. The KG Cognitive Data Platform is the product that delivers this.
Rough Set Theory provides a mathematical framework for dealing with vaginality and uncertainty in data. In Knowledge Grid, we use it to establish 'Lower' and 'Upper' approximations of normal behavior. This allows for high-precision, unsupervised anomaly detection that surfaces zero-day threats without requiring labeled training datasets.
No. Knowledge Grid is a Cognitive Data Layer that sits alongside or beneath your existing stack. It improves the utility of your current SIEM and XDR tools by providing them with AI-ready, contextualized data, extending their life and effectiveness for autonomous security operations.
Our patented Rough-Set architecture enables Approximate Query (AQ) performance that is 10–100x faster than legacy SQL or NoSQL databases. This allows for real-time semantic enrichment of high-velocity telemetry without incurring the compute costs associated with traditional data reprocessing.
By preserving the temporal shape of data and extracting environmental ground-truth, Knowledge Grid provides the 'memory' and 'context' that LLMs and agents lack. This enables agents to generate investigation narratives and take autonomous actions with a fraction of the false-positive rates seen in first-generation security AI.
SIEMs and log management tools are built to collect, store, and search logs. They're excellent at retention and retrieval, but the data they hold remains raw — every query, correlation rule, and analytics layer has to interpret it from scratch. The KG Cognitive Data Platform sits at the data layer beneath those workflows and makes the telemetry itself intelligent: structured, enriched, and ready for both analytics and AI as it arrives. We're not asking you to rip out your SIEM. We make the data underneath everything work harder.
Data lakes and security data platforms solve where your data lives and how much it costs to keep. They don't change what the data is. Once it lands, it's still raw telemetry waiting to be interpreted. The KG Cognitive Data Platform is about readiness, not just storage: it converts temporal security data into a structured, queryable, machine-reasonable form at ingest, so the value is in the state of the data, not merely its location.
You can - but you may not be able to trust the results and the LLM will likely miss things. This is because LLMs don't reason well over raw, high-velocity temporal data. Security telemetry is a relentless stream of continuously changing events, and feeding that directly to a model produces noise, hallucination, and cost — not insight. The KG Cognitive Data Platform solves the problem underneath the model: it makes temporal data AI-ready at ingest, so anything you place downstream — an LLM, an agent, an analytics engine — is working with data it can actually reason over. The failure was never AI itself. It was the data infrastructure beneath it.
The core temporal data engine is protected by seven issued US patents, with two additional patents pending. The patented methods cover granulated summaries, approximate query, compressed metadata, key-value-pair storage, and log parsing — the foundational techniques that make high-velocity temporal data AI-ready at ingest.
The platform makes the data underneath a managed offering dramatically more useful. For an MDR or MSSP, that means faster, higher-fidelity detection and investigation across clients, less noise for analysts to wade through, and AI and agentic workflows that actually work because they're running on AI-ready data. It's an infrastructure advantage you deliver on top of — not a competing tool your team has to displace.
Technology and channel partners — including MDR and MSSP providers — engage through the Strategic Partner Program. Because the KG Cognitive Data Platform operates at the data layer and complements rather than competes with existing security offerings, it strengthens what partners already deliver: cleaner, AI-ready data makes their detection, investigation, and managed services measurably more effective. Referral pathways are available for partners who want to introduce clients without operating the platform directly.