Syslog & Event Correlation

Most environments drown in syslog spam and unfiltered SNMP traps. The problem isn’t collecting events — it’s making sense of them. I build correlation frameworks that ingest, filter, enrich, and surface only the signals that matter.

My Approach

• Centralized Ingestion – syslogs, SNMP traps, and API feeds flow into a unified pipeline.

• Noise Reduction – filters strip duplicate, low-value, or irrelevant events.

• Contextual Enrichment – events tagged with device, interface, severity, and related metrics for clarity.

• Correlation Logic – link related events across devices (e.g., link flap + neighbor down → single root cause).

• Alerting Pipelines – only actionable, enriched events are forwarded to dashboards or notifications.

Advancing Further

I continue to expand methodology toward:

• Adaptive Filters – dynamically adjust thresholds to prevent alert fatigue.

• AI-Powered Correlation – using embeddings/intent models to cluster and explain event storms.

• Closed-Loop Automation – triggering automated responses (e.g., rerun diagnostics, capture data) based on correlated events.

Why It Matters

Unfiltered syslogs are just noise. By structuring and correlating events, I deliver signal-rich insights that engineers can trust — turning walls of text into real-time, actionable intelligence.