Agentic Correlation Engine: Dynamic Incident Generation Based on Knowledge Graphs

Alert Fatigue: Network security monitoring systems generate thousands of granular alerts that create operational bottlenecks and obscure critical root causes.

Rigid Rule Correlation: Traditional rule-based correlation engines fail to combine disjointed alerts into coherent incident narratives.

Abnormality Detection Before Impact: AIOps in network security aims to detect firewall ecosystem abnormalities before they affect business continuity.

Alert Fatigue: Network security monitoring systems generate thousands of granular alerts that create operational bottlenecks and obscure critical root causes.

Rigid Rule Correlation: Traditional rule-based correlation engines fail to combine disjointed alerts into coherent incident narratives.

Agentic Correlation Engine: The paper proposes an Agentic Correlation Engine that replaces manual rule logic with dynamic graph-based agentic reasoning.

LLM Dependency Graph Construction: The methodology autonomously builds high-fidelity Logical Dependency Graphs from unstructured technical documentation using LLMs.

Dual-Strategy Alert Clustering: The system clusters alerts vertically through causal chains and horizontally through peer similarity.

Hybrid Graph Confidence Scoring: The architecture validates theoretical graph relationships against real alert sequencing using statistical cross-correlation and topological dominance.

Agentic Correlation Engine: The paper proposes an Agentic Correlation Engine that replaces manual rule logic with dynamic graph-based agentic reasoning.

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