Cyber Security Use Cases:
Transform Your detection from Alert-Driven Security
To Understanding -Driven Security
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"
Use Case: Data Analytics Service
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.
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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.
Where Knowledge Grid fits
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: The Data Analytics Service.
Knowledge Grid’s Data Analytics Service operates on top of the Cognitive Data Layer and provides:
High-performance analytics on structured, contextual, time-aware knowledge—rather than isolated queries to event searches.
How We Differ from Traditional Log Management Solutions
Data Analytics Service Overview
Sold exclusively by & through Partners - MSSP, MSP, or MDR Providers & Consulting Orgs
Next step
Use Case: Unsupervised Anomaly Detection
Adds a complementary detection layer to existing SIEM, NDR, & XDR platforms—identifying unknown threats and behavioral anomalies that rules and signatures cannot detect.
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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.
This creates a critical blind spot:
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New attack patterns go undetected
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Subtle behavioral shifts are missed
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Complex multi-step attacks evade correlation rules
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Analysts are overwhelmed by alerts—but still lack full visibility
Result:
Even mature SOCs are optimized for known threats, but remain exposed to the unknown unknowns.
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
Where Knowledge Grid Fits: Unsupervised Detection as a Complementary Layer
Knowledge Grid’s Unsupervised Anomaly Detection Service integrates alongside existing detection stacks—without replacing them.
It provides a counter-balanced detection model
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Together, they form a complete detection strategy.
Detect Unknown Unknowns. Complement Existing Detection Capabilities
Sold exclusively by & through MSSP, MSP, or MDR Providers & Consulting Orgs