# SIEM Architecture Guide: Data Flow, Analytics & SOAR
> Learn how SIEM architecture transforms raw logs into actionable intelligence through normalization, correlation, machine learning, and SOAR automation.

Tags: siem, cybersecurity, soar, threat-intelligence, security-analytics, data-normalization, incident-response
## SIEM Architecture Overview
* Comprehensive breakdown of data flow, normalization, and security analytics.

## Data Sources Ingestion
* **Endpoints:** Workstations, servers, and mobile devices.
* **Network:** Firewalls, routers, switches, and IDS.
* **Cloud:** AWS, Azure, GCP infrastructure logs.
* **SaaS:** Application logs from Office 365, Salesforce.

## Normalization & Enrichment Layer
* **Parser:** Converts raw logs into structured formats.
* **Enricher:** Adds context like Geo-IP and User Department.
* **Schema Mapping:** Standardizes fields across sources.

## Correlation & Analytics Engines
* **UBA:** Detects anomalies in user behavior.
* **Machine Learning:** Identifies unknown threat patterns.
* **Real-time Correlation:** Links disparate events into attack chains.
* **Efficiency Comparison:** Traditional rules (65% detection) vs. Hybrid Analysis (98% detection).

## SOAR Capabilities
* Streamlines incident handling via automated playbooks, incident triage, and manual/automated response actions.

## Threat Intelligence Integration
* External feeds recognize known bad IPs, malware hashes, and phishing domains instantly.

## Protocols & Connectivity
* **Syslog (UDP 514):** Standard network device logging.
* **HTTPS (TCP 443):** Secure web/API transport.
* **API:** Flexible SaaS and Cloud integration.

## Operational Workflow
1. Ingest
2. Normalize
3. Correlate
4. Respond
* Demonstrated reduction in MTTR (Mean Time To Respond) from 120 minutes in Jan to 30 minutes in May.
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