SIEM Architecture Guide: Data Flow, Analytics & SOAR

SIEM Architecture Overview

A comprehensive breakdown of data flow, normalization, and security analytics

SIEM Architecture Diagram

Data Sources

Normalization & Enrichment

Correlation & Analytics

Endpoints|Network|Cloud|SaaS

Parser|Enricher|Schema Map|Threat Intel

UBA|ML|SOAR|Dashboards

Correlation & Analytics

Correlation Engine|Endpoints|SaaS

Correlation & Analytics

SOAR Playbooks

Data Sources Ingestion

Endpoints: Workstations, servers, and mobile devices generating logs.|Network: Firewalls, routers, switches, and intrusion detection systems.|Cloud: Public and private cloud infrastructure logs (AWS, Azure, GCP).|SaaS: Application logs from Office 365, Salesforce, etc.

Normalization & Enrichment Layer

The Parser converts raw logs into structured formats for easier analysis.

The Enricher adds context (e.g., Geo-IP, User Department) to raw data.

Schema Mapping standardizes fields across different log sources.

Correlation & Analytics Engines

UBA (User Behavior Analytics): Detects anomalies in user actions.

Machine Learning (ML): Identifies unknown threats patterns.

Real-time Correlation: Links disparate events to find attack chains.

SOAR Capabilities

Security Orchestration, Automation, and Response (SOAR) streamlines incident handling.

Automated Playbooks

Incident Triage

Response Actions

Threat Intelligence Integration

Integrating external Threat Intel feeds allows the SIEM to recognize known bad IP addresses, malware hashes, and phishing domains instantly.

Protocols & Connectivity

Syslog (514): Standard for network device logging.

HTTPS (443): Secure transport for web and API logs.

API: Flexible integration for SaaS and Cloud.

Operational Workflow

1. Ingest

2. Normalize

3. Correlate

4. Respond

Summary

Effective SIEM architecture transforms raw data into actionable intelligence through a structured pipeline of ingestion, normalization, and advanced analytics, culminating in automated SOAR response.