Security teams are surrounded by tools designed to detect, alert, and investigate.
Yet even the most advanced environments still face the same challenges:
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Too many alerts, not enough context
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Detection limited to predefined rules and known patterns
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Investigations that require manual correlation across systems
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Increasing pressure to adopt AI—without the data foundation to support it
"The problem isn’t a lack of tools...
It’s a lack of structured, contextual data"
Find the Threats Your Tools Are Never Going to Catch
Use Case: Unsupervised Anomaly Detection (UAD)
Adds a complementary detection layer to existing SIEM, NDR, & XDR platforms—identifying unknown threats and behavioral anomalies that rules and signatures cannot detect.
Problem
The Problem: Detection is Limited to What You Already Know
Managed security providers—MSSPs, MDRs, and cyber service firms—have built their detection capabilities around:
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Rules and correlation logic
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Threat intelligence feeds (IOCs)
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Signature-based detection
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Predefined behavioral analytics
These approaches are effective—but inherently constrained because:
They can only detect what has already been defined, observed, or anticipated.
Where Knowledge Grid fits
The Detection Gap: Known vs Unknown Threats
Traditional detection answers:
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“Is this activity matching a known bad pattern?”
But modern attacks require answering:
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“Is this behavior fundamentally different from what is normal?”
Without this capability, attackers exploit:
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Novel techniques
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Low-and-slow activity
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Legitimate tools used in abnormal ways
Next step
What Unsupervised Anomaly Detection (UAD) Catches
UAD looks across multiple dimensions of security telemetry to uncover unusual behavior, emerging threats, and hidden patterns that rules, signatures, and predefined searches often miss.
Use Case: Data Analytics Service (DAS)
Knowledge Grid’s Data Analytics Service delivers insight on structured, high-signal knowledge—enabling faster investigations, deeper understanding, and AI-ready analytics that traditional platforms cannot support.
Problem
The Problem: Log-Centric Analytics Limits Insight and Scale
Most organizations rely on log-centric analytics platforms to support:
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Security investigations
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Operational monitoring
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Compliance reporting
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Observability and troubleshooting
These platforms have evolved significantly—but they still share a common architectural constraint:
They are built around storing, searching, and querying event data—not structuring it for deeper understanding or AI-driven reasoning.
Our Solution: Analytics on Structured Knowledge
To move beyond incremental improvements, analytics must evolve from:
Searching and querying events → Understanding structured knowledge
This means data must be:
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Pre-correlated across entities
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Organized across time and behavior
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Reduced to high-signal representations
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Ready for both human and machine reasoning
Where Knowledge Grid fits
Next step
Why Our Data Analytics Service is Different
Knowledge Grid’s Data Analytics Service is not a SIEM replacement or a traditional alert-correlation engine; it complements existing security tools by transforming raw telemetry into time-aware, AI-ready intelligence that helps teams investigate faster, uncover deeper context, and make better security decisions.
It can integrate with existing SIEM infrastructure through APIs, data exports, S3-based pipelines, or other customer-defined integration paths to enrich downstream investigations and security workflows.