correlating-security-events-in-qradar

name	correlating-security-events-in-qradar
description	Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.
domain	cybersecurity
tags	["soc","qradar","siem","aql","correlation","offense-management","ibm"]
subdomain	soc-operations
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06"]

Correlating Security Events In Qradar

Overview

Cybersecurity skill for correlating security events in qradar. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "correlating security events in qradar"
• "SOC analysts need to investigate QRadar offenses and correlate events across mul"
• "Detection engineers build custom correlation rules to identify multi-stage attac"
• "Alert tuning is required to reduce false positive offenses and improve signal qu"

Use this skill when:

• SOC analysts need to investigate QRadar offenses and correlate events across multiple log sources
• Detection engineers build custom correlation rules to identify multi-stage attacks
• Alert tuning is required to reduce false positive offenses and improve signal quality
• The team migrates from basic event monitoring to behavior-based correlation

Do not use for log source onboarding or parsing — that requires QRadar administrator access and DSM editor knowledge.

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• IBM QRadar SIEM 7.5+ with offense management enabled
• AQL knowledge for ad-hoc event and flow queries
• Log sources normalized with proper QID mappings (Windows, firewall, proxy, endpoint)
• User role with offense management, rule creation, and AQL search permissions
• Reference sets/maps configured for whitelist and watchlist management

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
     {k: re.findall(v, text)  k, v  IOC_PATTERNS.items()}