Carbon Black Response and Splunk Integration
Integrating Carbon Black Response with Splunk for enhanced threat detection, hunting, and incident response capabilities.
Originally published on Red Canary Blog
Read the full article: Carbon Black Response and Splunk Integration
Why Integrate EDR with SIEM?
Combining Carbon Black Response (EDR) with Splunk (SIEM) provides:
- Centralized visibility
- Enhanced correlation
- Better threat hunting
- Unified incident response
Integration Benefits
Comprehensive Visibility
- Endpoint telemetry in Splunk
- Correlation with network data
- User activity context
- Application logs
Advanced Hunting
Hunt across:
- Process execution
- Network connections
- File modifications
- Registry changes
Automated Response
Trigger Carbon Black actions from Splunk:
- Isolate endpoints
- Kill processes
- Ban hashes
- Retrieve files
Implementation
Data Collection
Stream Carbon Black data to Splunk:
- Process events
- Binary information
- Network connections
- Sensor status
Correlation Rules
Build detections that leverage both platforms:
- EDR process data + SIEM network logs
- Endpoint behavior + threat intelligence
- User activity + file access
Response Automation
Automate response actions:
- Alert → Investigation → Response
- Automated containment
- Evidence collection
- Reporting
Use Cases
Threat Hunting
Query Carbon Black data in Splunk for advanced hunts.
Incident Response
Centralized investigation across all data sources.
Compliance Reporting
Unified reporting on security events and responses.
Detection Engineering
Build detections that span endpoint and network.
Best Practices
- Index only necessary Carbon Black data
- Use summary indexing for performance
- Build reusable search macros
- Document correlation logic
- Test response automations
Read the full guide: Carbon Black Response and Splunk Integration
Related Modules
CBR-Queries
Collection of useful, up to date, Carbon Black Response Queries for threat hunting and detection.
ShellSweep
ShellSweeping the evil. PowerShell/Python/Lua tool to detect potential web shells using entropy analysis, machine learning, and YARA rules.