The AI Data Layer for Cyber Security
Knowledge Grid turns high-volume security telemetry into AI-ready context for detection, correlation, and autonomous analysis.
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Make security telemetry AI-ready without replacing your stack
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Detect unknown threats without rule authoring
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Give AI agents structured context, not raw log chaos
Security Data Is Not Optimized for AI
The Scale Problem
Telemetry Explosion
Legacy systems are collapsing under the weight of surging telemetry and unstructured data creating blind spots that mask actual threats.
There is so much data, the attackers are essentially hiding in the data.
The Ops Problem
Fragmented Observability
Legacy Tools were built for humans & dashboards.
Tool sprawl and siloed environments make it impossible to operationalize agentic AI, preventing LLMs from accessing the unified context they require to function.
The Security Problem
Static Rules Obsolescence
Traditional rules-based detection systems cannot identify unknown threats. Modern attackers constantly evolve techniques that have never been seen before (until it's too late).
Security teams and AI workflows need to quickly sift through patterns in massive data volumes to find the "unknown unknowns".
The AI Data Layer Has Been Missing
While AI has rapidly advanced, the data infrastructure supporting it has not kept pace.
Most organizations still rely on traditional data stacks built for storage and reporting—not for the real-time, high-context data access that AI agents and modern analytics require.
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As a result, teams are forced to bolt AI onto systems that were never designed to feed models with clean, structured, and context-rich information at scale.
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The missing piece is a dedicated AI data layer—one that can transform massive streams of raw telemetry into compact, high-signal knowledge that AI systems can query, reason over, and act on in real time.
Technology Overview
The Knowledge Grid platform is a Modern Data Stack that unifies three core capabilities: the Temporal Data Grid, native LLM/Agentic AI, and Unsupervised Anomaly Analysis.
Together, they deliver an AI-ready data foundation that speeds analysis, improves detection accuracy, and eliminates the need to rip and replace Traditional Data infrastructure.
Temporal Data Grid - organizes high velocity telemetry over time for fast search and correlation
Agentic AI/LLM Integration - converts raw data sources into structured, context-rich inputs for AI workflows
Unsupervised Anomaly Detection - AI-based detection provides the ability to find unknown threats hidden in massive volumes of data
The Agentic Age
"The tectonic shift in AI innovation is catalyzing an evolution in data infrastructure"
2.52T
Projected 2026 Worldwide AI Spending*
82%
of Enterprises Not ready for AI workloads*
1%
Of IT Leaders say
no major operating model changes underway*
*Source: Gartner