correlating-security-events-in-qradar
Teaches your AI agent to protect apps against attacks.
What it does
This tool helps security analysts find suspicious patterns in network and system events using IBM QRadar. It allows them to investigate incidents, create rules to detect multi-stage attacks, and tune alerts to avoid false warnings.
When to use it
- ✓Investigating a security alert in QRadar.
- ✓Creating a new rule to detect a specific type of attack.
- ✓Reducing the number of irrelevant alerts (false positives).
Example of what to ask your AI
“How can I set up QRadar to detect if a user is repeatedly trying to guess passwords across multiple machines?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses a query language called AQL to search and analyze event data. It also allows creating rules that combine different event types to identify malicious activity.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill correlating-security-events-in-qradarOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/correlating-security-events-in-qradar/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Correlating Security Events in QRadar
When to Use
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.
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
Step 1: Investigate an Offense with AQL
Open an offense in QRadar and query contributing events using AQL (Ariel Query Language):
SELECT DATEFORMAT(startTime, 'yyyy-MM-dd HH:mm:ss') AS event_time,
sourceIP, destinationIP, username,
LOGSOURCENAME(logSourceId) AS log_source,
QIDNAME(qid) AS event_name,
category, magnitude
FROM events
WHERE INOFFENSE(12345)
ORDER BY startTime ASC
LIMIT 500
Pivot on the source IP to find all activity:
SELECT DATEFORMAT(startTime, 'yyyy-MM-dd HH:mm:ss') AS event_time,
destinationIP, destinationPort, username,
QIDNAME(qid) AS event_name,
eventCount, category
FROM events
WHERE sourceIP = '192.168.1.105'
AND startTime > NOW() - 24*60*60*1000
ORDER BY startTime ASC
LIMIT 1000
Step 2: Build a Custom Correlation Rule
Create a multi-condition rule detecting brute force followed by successful login:
Rule 1 — Brute Force Detection (Building Block):
Rule Type: Event
Rule Name: BB: Multiple Failed Logins from Same Source
Tests:
- When the event(s) were detected by one or more of [Local]
- AND when the event QID is one of [Authentication Failure (5000001)]
- AND when at least 10 events are seen with the same Source IP
in 5 minutes
Rule Action: Dispatch new event (Category: Authentication, QID: Custom_BruteForce)
Rule 2 — Brute Force Succeeded (Correlation Rule):
Rule Type: Offense
Rule Name: COR: Brute Force with Subsequent Successful Login
Tests:
- When an event matches the building block BB: Multiple Failed Logins from Same Source
- AND when an event with QID [Authentication Success (5000000)] is detected
from the same Source IP within 10 minutes
- AND the Destination IP is the same for both events
Rule Action: Create offense, set severity to High, set relevance to 8
Step 3: Use AQL for Cross-Source Correlation
Correlate authentication failures with network flows to detect lateral movement:
SELECT e.sourceIP, e.destinationIP, e.username,
QIDNAME(e.qid) AS event_name,
e.eventCount,
f.sourceBytes, f.destinationBytes
FROM events e
LEFT JOIN flows f ON e.sourceIP = f.sourceIP
AND e.destinationIP = f.destinationIP
AND f.startTime BETWEEN e.startTime AND e.startTime + 300000
WHERE e.category = 'Authentication'
AND e.sourceIP IN (
SELECT sourceIP FROM events
WHERE QIDNAME(qid) = 'Authentication Failure'
AND startTime > NOW() - 3600000
GROUP BY sourceIP
HAVING COUNT(*) > 20
)
AND e.startTime > NOW() - 3600000
ORDER BY e.startTime ASC
Detect data exfiltration by correlating DNS queries with large outbound flows:
SELECT sourceIP, destinationIP,
SUM(sourceBytes) AS total_bytes_out,
COUNT(*) AS flow_count
FROM flows
WHERE sourceIP IN (
SELECT sourceIP FROM events
WHERE QIDNAME(qid) ILIKE '%DNS%'
AND destinationIP NOT IN (
SELECT ip FROM reference_data.sets('Internal_DNS_Servers')
)
AND startTime > NOW() - 86400000
GROUP BY sourceIP
HAVING COUNT(*) > 500
)
AND destinationPort NOT IN (80, 443, 53)
AND startTime > NOW() - 86400000
GROUP BY sourceIP, destinationIP
HAVING SUM(sourceBytes) > 104857600
ORDER BY total_bytes_out DESC
Step 4: Configure Reference Sets for Context Enrichment
Create reference sets for dynamic whitelists and watchlists:
# Create reference set via QRadar API
curl -X POST "https://qradar.example.com/api/reference_data/sets" \
-H "SEC: YOUR_API_TOKEN" \
-H "Content-Type: application/json" \
…
FAQ
What is QRadar?
QRadar is a security system that collects and analyzes event data from networks and computers to detect threats.
What are 'false positives'?
False positives are security alerts that seem to indicate a problem but are actually normal or harmless events.
Do I need to be a QRadar expert to use this?
Some basic knowledge of QRadar and the AQL language is helpful, but the tool is designed to simplify the event correlation process.
Details
Original description by the author
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. '
Popularity
- GitHub stars
- 35k
- Ranking position
- #5610
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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