Security

SIEM Architecture Diagram

Show how a SIEM ingests, correlates, and alerts on log data from across the environment.

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7 connected components you can rename, recolor, and extend with AI.

Log Sources / EndpointsLog Collectors / ForwardersNormalization PipelineData Store / IndexThreat Intelligence FeedsAlerting & NotificationsAnalyst Dashboards

This diagram depicts a SIEM architecture, the platform that aggregates and analyzes security data for threat detection. It centers on a correlation and analytics engine that ingests logs from many sources, normalizes them, and applies rules to surface threats. The surrounding pieces include log sources and collectors, a normalization pipeline, the correlation engine, threat intelligence feeds, alerting, and dashboards for analysts.

Security engineers, SOC teams, and IT architects use this SIEM architecture diagram to plan deployments, justify log source coverage, and explain detection workflows to stakeholders. It is well suited to platform design, vendor comparisons, and onboarding analysts to how alerts move from raw logs to triaged incidents.

Great for

  • SOC platform design
  • Vendor comparisons
  • Log coverage planning
  • Analyst onboarding
  • Security operations docs

Frequently asked questions

What is a SIEM architecture?+

A SIEM architecture describes how a security information and event management platform collects logs, normalizes and stores them, correlates events, and generates alerts and dashboards for analysts.

What are the main components of a SIEM?+

Log sources and collectors, a normalization pipeline, a data store or index, a correlation and analytics engine, threat intelligence feeds, and alerting with dashboards.

How does a SIEM detect threats?+

It correlates events across many log sources using rules, statistical baselines, and threat intelligence to surface patterns that indicate an attack, then raises prioritized alerts.

Why is log normalization important in a SIEM?+

Normalization converts logs from diverse systems into a common schema so the correlation engine can compare and analyze events consistently across the environment.

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