Siem Logging

SIEM Logging

Purpose

Configure comprehensive security logging infrastructure using SIEM platforms (Elastic SIEM, Microsoft Sentinel, Wazuh, Splunk) to detect threats, investigate incidents, and maintain compliance audit trails. This skill covers platform selection, log aggregation architecture, detection rule development (SIGMA format and platform-specific), alert tuning, and retention policies for regulatory compliance (GDPR, HIPAA, PCI DSS, SOC 2).

When to Use This Skill

Use this skill when:

• Implementing centralized security event monitoring across infrastructure
• Writing threat detection rules for authentication failures, privilege escalation, data exfiltration
• Designing log aggregation for multi-cloud environments (AWS, Azure, GCP, Kubernetes)
• Meeting compliance requirements for log retention and audit trails
• Tuning security alerts to reduce false positives and alert fatigue
• Calculating costs for high-volume security logging (TB/day scale)
• Integrating security logging with incident response workflows

SIEM Platform Selection

Quick Decision Framework

Choose SIEM platform based on:

Budget Considerations:

• Unlimited budget → Splunk Enterprise Security (enterprise features, proven scale)
• Moderate budget ($50k-$500k/year) → Microsoft Sentinel or Elastic SIEM (cloud-native, flexible)
• Tight budget (<$50k/year) → Wazuh (free, open-source XDR/SIEM)

Infrastructure Context:

• Heavy Azure investment → Microsoft Sentinel (native integration, built-in SOAR)
• Heavy AWS investment → AWS Security Lake + OpenSearch (AWS-native)
• Multi-cloud or on-premise → Elastic SIEM or Wazuh (platform-agnostic)

Data Volume:

• >1 TB/day → Splunk or Elastic Cloud (proven at scale)
• 100 GB - 1 TB/day → Microsoft Sentinel or Elastic SIEM
• <100 GB/day → Wazuh or Sentinel 50 GB tier

Team Expertise:

• Elasticsearch experience → Elastic SIEM (familiar tooling)
• Microsoft/Azure expertise → Microsoft Sentinel (Azure ecosystem)
• Generalists or limited resources → Wazuh (easiest learning curve)

Platform Comparison Summary

For detailed feature comparison, see references/platform-comparison.md.

Detection Rules

Universal Format: SIGMA Rules

SIGMA provides a universal detection rule format that compiles to any SIEM query language (Elastic EQL, Splunk SPL, Microsoft KQL).

SIGMA Rule Structure:

title: Multiple Failed Login Attempts from Single Source
id: 8a9e3c7f-4b2d-4e8a-9f1c-2d5e6f7a8b9c
status: stable
description: Detects potential brute force attacks (10+ failed logins in 10 minutes)
author: Security Team
date: 2025/12/03
references:
  - https://attack.mitre.org/techniques/T1110/
tags:
  - attack.credential_access
  - attack.t1110
logsource:
  category: authentication
  product: linux
detection:
  selection:
    event.type: authentication
    event.outcome: failure
  timeframe: 10m
  condition: selection | count() by source.ip > 10
level: high

Compile SIGMA to Platform-Specific:

# Install SIGMA compiler
pip install sigma-cli

# Compile to Elastic EQL
sigmac -t es-eql sigma_rule.yml

# Compile to Splunk SPL
sigmac -t splunk sigma_rule.yml

# Compile to Microsoft KQL
sigmac -t kusto sigma_rule.yml

Platform-Specific Detection Formats

Elastic EQL (Event Query Language):

sequence by user.name with maxspan=5m
  [process where process.name == "powershell.exe" and
   process.args : ("Invoke-WebRequest", "iwr", "wget")]
  [process where process.parent.name == "powershell.exe"]

Microsoft Sentinel KQL:

SigninLogs
| where TimeGenerated > ago(1h)
| where ResultType != 0  // Failed login
| summarize FailedAttempts=count() by UserPrincipalName, IPAddress
| where FailedAttempts >= 10

Splunk SPL:

index=web_logs sourcetype=access_combined
| rex field=uri "(?<sql_keywords>union|select|insert|update|delete)"
| where isnotnull(sql_keywords)
| stats count by src_ip, uri
| where count > 5

For comprehensive detection rule examples, see:

• examples/sigma-rules/ - Universal SIGMA detection rules
• examples/elastic-eql/ - Elastic-specific queries
• examples/microsoft-kql/ - Microsoft Sentinel queries
• examples/splunk-spl/ - Splunk searches
• references/detection-rules-guide.md - Complete guide

Log Aggregation Architecture

Centralized Architecture

Single SIEM instance for all logs. Use when:

• Single region deployment
• Small to medium volumes (<1 TB/day)
• Single cloud provider or on-premise
• Limited security team (1-10 analysts)

Architecture:

Application Servers → Log Shippers (Filebeat/Fluentd)
                   ↓
              Log Aggregator (Logstash/Fluentd)
                   ↓
          SIEM Platform (Elasticsearch/Splunk/Sentinel)
                   ↓
            Security Analysts (Dashboard/Alerts)

Distributed Architecture (Multi-Region)

Regional SIEM instances with global aggregation. Use when:

• Multi-region global deployments
• Data residency requirements (GDPR, sovereignty)
• High volumes (>1 TB/day per region)
• Low-latency requirements for regional analysis

Architecture:

Global SIEM (Correlation, Threat Intelligence)
    ↓
Regional SIEM (US-East) | Regional SIEM (EU-West) | Regional SIEM (APAC)
    ↓                        ↓                          ↓
Local Logs               Local Logs                 Local Logs

Cloud-Native Architecture

Leverage managed cloud services. Use when:

• Cloud-first organization (AWS/Azure/GCP)
• Want to avoid managing infrastructure
• Elastic workloads with variable log volumes
• Budget for cloud service costs

AWS Example:

CloudTrail + VPC Flow Logs + GuardDuty
              ↓
       AWS Security Lake (S3 Data Lake)
              ↓
    OpenSearch (Analysis) | Athena (SQL Queries)

For deployment examples, see:

• examples/architectures/elk-stack-docker-compose.yml
• examples/architectures/fluentd-kubernetes-daemonset.yaml
• examples/architectures/aws-security-lake-terraform/
• examples/architectures/wazuh-docker-compose.yml
• references/cloud-native-logging.md

Log Aggregation Tools

Fluentd (Cloud-Native): CNCF project for Kubernetes and multi-cloud environments. Use for containerized applications.

Logstash (Elastic Stack): Native Elasticsearch integration. Use for advanced parsing (grok patterns) and data enrichment.

For complete configuration examples, see examples/logstash-pipelines/ and references/cloud-native-logging.md.

Log Retention and Compliance

Compliance Requirements

Storage Tiering Strategy

Hot Tier (SSD, Real-Time):

• Last 7-30 days
• Real-time indexing and fast queries
• Most expensive ($0.10/GB/month)

Warm Tier (HDD, Recent):

• 30-90 days
• Read-only indices, occasional searches
• Moderate cost ($0.05/GB/month)

Cold Tier (S3/Blob, Archive):

• 90 days to retention limit
• Searchable snapshots, rare queries
• Cheapest ($0.01/GB/month)

Example Cost Optimization:

500 GB/day log volume, 1-year retention

Hot (30 days):   15 TB @ $0.10/GB = $1,500/month
Warm (60 days):  30 TB @ $0.05/GB = $1,500/month
Cold (275 days): 137.5 TB @ $0.01/GB = $1,375/month

Total: $4,375/month = $52,500/year

vs. Hot-only: $18,250/month = $219,000/year
Savings: 76% ($166,500/year)

For detailed retention policies and cost optimization, see:

• references/log-retention-policies.md
• references/cost-optimization.md
• scripts/cost-calculator.py