SIEM Cheat Sheet

Updated 2026-04-30

Security Information and Event Management (SIEM) is a centralized security monitoring platform that aggregates, normalizes, correlates, and analyzes log data from across an organization's IT infrastructure to detect threats, investigate incidents, and support compliance requirements. SIEM enables real-time threat detection by applying correlation rules and behavioral analytics to millions of security events, transforming raw log data into actionable intelligence that reduces Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR). The most critical insight for SIEM practitioners in 2026 is that effective detection depends more on high-quality, well-tuned correlation rules and contextualized enrichment than on sheer log volume—a SIEM with 50 strategically selected data sources and precision-tuned detections outperforms one ingesting everything without purpose.

What This Cheat Sheet Covers

This topic spans 15 focused tables and 105 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core SIEM ComponentsTable 2: SIEM Query LanguagesTable 3: Log Formats and StandardsTable 4: Correlation Rules and Detection LogicTable 5: Alert Tuning and False Positive ReductionTable 6: SIEM Use Cases and Detection ScenariosTable 7: SIEM Architecture PatternsTable 8: Data Source OnboardingTable 9: SIEM Compliance and ReportingTable 10: User and Entity Behavior Analytics (UEBA)Table 11: Threat Intelligence IntegrationTable 12: SOAR and Incident Response AutomationTable 13: SIEM Performance and ScalabilityTable 14: Advanced SIEM TechniquesTable 15: SIEM Metrics and KPIs

Table 1: Core SIEM Components

Every SIEM is really a pipeline, and these are its stages—the journey a single log line takes from a noisy endpoint to an actionable alert on an analyst's screen. Following the rows top to bottom is following that flow: raw logs are collected, normalized into a common schema, parsed into fields, correlated, indexed for fast search, enriched with context, and finally surfaced through alerts and dashboards, with tiered storage keeping it all affordable.

Component	Example	Description
Log Collection	Syslog forwarder: `rsyslog` Agent: Splunk UF API: Azure Monitor	• Gathers security event data from endpoints, network devices, firewalls, cloud platforms, and applications using agents, agentless methods, or API pulls • supports multiple ingestion protocols
Data Normalization	Convert Cisco ASA log → CEF Parse Windows Event ID 4624	• Transforms logs from diverse sources into a standardized schema (e.g., CEF, LEEF, OCSF) with consistent field names and formats • enables cross-source correlation
Log Parsing	Regex: `(?P<user>\w+) from (?P<ip>\S+)` Grok pattern: `%{TIMESTAMP_ISO8601:ts}`	• Uses regular expressions, Grok patterns, or built-in parsers to extract structured fields from raw log text (e.g., username, IP, timestamp) • foundation for searchability
Event Correlation	Failed logins × 10 in 5min → alert Login from IP1, then IP2 ≠ geo	• Applies rule-based or statistical logic to connect related events across time and sources • detects attack patterns that single events miss (e.g., brute force, lateral movement).
Indexing	Splunk indexes by `_time`, `host`, `sourcetype` Elasticsearch inverted index	• Organizes ingested data into searchable structures (inverted indexes, columnar stores) for fast query performance • supports field-based filtering and time-range lookups

Table 1: Core SIEM Components

Component	Example	Description
Log Collection	Syslog forwarder: `rsyslog` Agent: Splunk UF API: Azure Monitor	• Gathers security event data from endpoints, network devices, firewalls, cloud platforms, and applications using agents, agentless methods, or API pulls • supports multiple ingestion protocols
Data Normalization	Convert Cisco ASA log → CEF Parse Windows Event ID 4624	• Transforms logs from diverse sources into a standardized schema (e.g., CEF, LEEF, OCSF) with consistent field names and formats • enables cross-source correlation
Log Parsing	Regex: `(?P<user>\w+) from (?P<ip>\S+)` Grok pattern: `%{TIMESTAMP_ISO8601:ts}`	• Uses regular expressions, Grok patterns, or built-in parsers to extract structured fields from raw log text (e.g., username, IP, timestamp) • foundation for searchability
Event Correlation	Failed logins × 10 in 5min → alert Login from IP1, then IP2 ≠ geo	• Applies rule-based or statistical logic to connect related events across time and sources • detects attack patterns that single events miss (e.g., brute force, lateral movement).
Indexing	Splunk indexes by `_time`, `host`, `sourcetype` Elasticsearch inverted index	• Organizes ingested data into searchable structures (inverted indexes, columnar stores) for fast query performance • supports field-based filtering and time-range lookups