unified intelligence and operations layer

Unified intelligence and operations layer

Integrating Splunk, Elastic, and XDR platforms with Equitus.ai KGNN at the data center level provides a unified intelligence and operations layer—where KGNN acts as the semantic fusion and AI inference engine, while the others serve as telemetry, observability, and security endpoints. Here's a breakdown of how they differ and how Equitus.ai can harmonize their strengths:

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🔍 1. Key Platform Capabilities

Platform	Core Function	Data Focus	Integration Method
Splunk	Log and event correlation, SIEM	Indexed time-series data, logs	REST APIs, forwarders
Elastic (ELK Stack)	Search, observability	Logs, metrics, traces (unstructured or semi-structured)	Elastic Beats, Logstash
XDR (e.g., Palo Alto Cortex, CrowdStrike Falcon, Microsoft Defender)	Threat detection/response across endpoints	Enriched security telemetry, alerts	APIs, syslog, EDR connectors

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🧠 2. Role of Equitus.ai KGNN (Knowledge Graph Neural Network)

• KGNN transforms raw telemetry + observability data into actionable knowledge by:

  • Linking disparate sources (log events, alerts, metrics) into context-rich entities.

  • Mapping behavior and dependencies across users, processes, and systems.

  • Using inference on graph topology to detect anomalies, root cause, or mission impact.

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🧩 3. Integration Architecture at the Data Center Level

🔗 Data Ingestion Layer

• Splunk Forwarders, Logstash Pipelines, or XDR Webhooks send data to an Equitus Ingestion Node.

• Use Kafka or Apache NiFi as middleware for stream processing into the KGNN pipeline.

🔀 Translation and Normalization

• Equitus.ai converts logs/alerts into ontology-based triples or structured events:

  • "User_A" accessed "Server_12" via "SSH" → converted to semantic graph edges.

  • Applies enrichment with Threat Intelligence, IAM data, and Business Logic.

🧠 Knowledge Graph Core (KGNN Engine)

• Events are linked into a real-time knowledge graph, enriched with:

  • Mission context (e.g., CBP border sensor → Fusion Center → Cloud node)

  • Asset classification (e.g., mission-critical vs auxiliary)

  • EVW/EEV metrics for automated risk/value scoring.

📊 Action & Visualization Layer

• Splunk dashboards and Elastic Kibana panels receive graph insights via API push or shared dashboards.

• Equitus generates mission impact maps, attack path reconstructions, and value-based prioritization for SOC and NOC operators.

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✅ Benefits of Integration

• Operational Awareness: Equitus graph connects endpoint alerts (XDR) with network logs (Elastic) and system events (Splunk).

• Reduced Dwell Time: KGNN infers hidden relationships and threat paths faster than traditional correlation engines.

• Mission-Centric Response: Enables commanders and IT leadership to prioritize based on EVW/EEV scoring, not just alert volume.

• Multi-Tenant Flexibility: Ideal for shared services across government or defense enclaves.

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📡 Sample Use Case

Scenario: A failed login event on a critical database server is seen in Splunk, while Elastic detects a spike in resource use on the same node, and XDR flags a process anomaly.

Equitus.ai KGNN:

• Links the failed login to a known threat actor via graph reasoning.

• Infers lateral movement potential based on known privilege escalation paths.

• Issues a mission impact alert prioritized by business/mission criticality.

• Sends this to SOC dashboards in Splunk and Elastic, and auto-generates an incident response plan.

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🔐 Contact & Implementation Support

To explore Equitus.ai KGNN integrations with Splunk, Elastic, and XDR in your data center:

• 📧 Contact: info@equitus.ai

• 🌐 Website: https://equitus.ai

• 📞 Sales & Engineering: Available for DoD/IC, Critical Infrastructure, and Commercial DCs

Would you like a system architecture diagram or integration whitepaper for stakeholder presentations?