Powering The Systems that Power AI
Modern platforms—from SOC environments to data pipelines to agentic AI—are evolving fast.
But they all depend on the same thing: A data layer that can support reasoning, context, and decision-making.
Most weren’t built for that.
Knowledge Grid provides the missing foundation—transforming fragmented telemetry into structured, high-signal knowledge that enables platforms to perform at a higher level.
Use Case: Agentic SOC
Agentic SOC doesn’t fail because of AI—it fails because of the data layer.
Problem
The Problem: AI Can’t Reason on Raw Security Data
Modern SOC environments are flooded with telemetry—logs, alerts, events, and signals from dozens of tools. While AI and LLMs promise automation, most deployments fall short for one core reason:
The data isn’t structured for reasoning—it’s fragmented, noisy, and lacks context.
This creates fundamental challenges:
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Fragmented telemetry: Data spread across SIEMs, EDRs, cloud logs, identity systems
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Lack of context: Events lack relationships, history, and behavioral meaning
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High false positives: Rules and models trigger without understanding significance
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Limited AI effectiveness: LLMs summarize—but don’t reason or decide reliably
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Analyst bottlenecks: Humans still required to correlate, investigate, and validate
Result:
Even with AI, SOC teams remain reactive, overwhelmed, and unable to scale.
Where Knowledge Grid fits
Instead of feeding AI raw logs, Knowledge Grid provides:
Higher Fidelity Detection
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Fewer false positives
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Detection based on behavior and context, not just rules
Faster Investigations
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Pre-correlated, high-signal data reduces analyst effort
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AI agents can triage and investigate autonomously
Scalable Security Operations
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Reduce dependence on human analysts
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Enable autonomous or semi-autonomous SOC workflows
More Reliable AI Outputs
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LLMs grounded in structured knowledge
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Improved consistency, explainability, and trust
Cost & Performance Efficiency
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Smaller data footprint (~5%)
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Faster query, analysis, and response times
The Data Foundation for Agentic SOC
Agentic SOC requires more than AI—it requires knowledge.
Next step
Use Case: Cognitive Data Pipeline
Transform your data pipeline into an AI-ready intelligence layer.
Knowledge Grid enables pipeline platforms to move beyond routing and normalization—delivering structured, high-signal knowledge that powers agentic AI, advanced analytics, and next-generation security operations.
Problem
Modern data pipeline platforms have become critical infrastructure for security and observability. They excel at:
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Collecting and routing telemetry
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Normalizing formats
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Reducing cost and storage overhead
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Delivering data to SIEMs, lakes, and analytics platforms
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But as organizations adopt Agentic AI and LLM-driven workflows, a new gap has emerged:
Even well-optimized pipelines still produce data that is:
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Structurally normalized—but not contextually enriched
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Searchable—but not semantically linked
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Available—but not decision-ready
Result:
AI systems downstream (SIEM, XDR, LLMs, copilots) still struggle with:
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Poor context
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Inconsistent outputs
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Limited reasoning capability
Where Knowledge Grid fits
The Opportunity: Evolve the Pipeline into an AI Data Foundation
Data pipeline vendors are uniquely positioned to become the control point for AI-ready data.
Instead of stopping at routing and normalization, pipelines can:
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Pre-structure data for AI consumption
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Add contextual relationships
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Reduce noise while preserving meaning
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Deliver knowledge, not just data
This transforms the pipeline from a transport layer into an intelligence layer.
Outcomes for Customers
Better AI Performance
Reduced Downstream Load
Faster Time to Detect/Insight
Unified Data Foundation
Outcomes for Pipeline Vendors
New Product Category: AI-Ready Pipelines
Expanded Revenue
Stronger Strategic Positioning
Differentiation vs Competitors
The Cognitive Data Pipeline: Enhances Traditional Pipeline Engines
Knowledge Grid integrates as a complementary layer—enhancing existing pipeline investments without replacing them. This enables partners to offer differentiated, AI-ready data services while preserving their core architecture.
Next step
Use Case: AI Agent Enablement Layer
Transform your data pipeline into an AI-ready intelligence layer.
Knowledge Grid enables pipeline platforms to move beyond routing and normalization—delivering structured, high-signal knowledge that powers agentic AI, advanced analytics, and next-generation security operations.
Problem
Modern data pipeline platforms have become critical infrastructure for security and observability. They excel at:
-
Collecting and routing telemetry
-
Normalizing formats
-
Reducing cost and storage overhead
-
Delivering data to SIEMs, lakes, and analytics platforms
-
But as organizations adopt Agentic AI and LLM-driven workflows, a new gap has emerged:
Even well-optimized pipelines still produce data that is:
-
Structurally normalized—but not contextually enriched
-
Searchable—but not semantically linked
-
Available—but not decision-ready
Result:
AI systems downstream (SIEM, XDR, LLMs, copilots) still struggle with:
-
Poor context
-
Inconsistent outputs
-
Limited reasoning capability
Where Knowledge Grid fits
The Opportunity: Evolve the Pipeline into an AI Data Foundation
Data pipeline vendors are uniquely positioned to become the control point for AI-ready data.
Instead of stopping at routing and normalization, pipelines can:
-
Pre-structure data for AI consumption
-
Add contextual relationships
-
Reduce noise while preserving meaning
-
Deliver knowledge, not just data
This transforms the pipeline from a transport layer into an intelligence layer.
Outcomes for Customers
Better AI Performance
Reduced Downstream Load
Faster Time to Detect/Insight
Unified Data Foundation
Outcomes for Pipeline Vendors
New Product Category: AI-Ready Pipelines
Expanded Revenue
Stronger Strategic Positioning
Differentiation vs Competitors
Give AI Systems the Context to Act Autonomously with Confidence
The Cognitive Data Layer replaces fragmented, multi-step data preparation with a single, efficient foundation that delivers structured, AI-ready knowledge at scale.