Challenge
Traditional impossible travel detection generated excessive false positives, overwhelming incident response teams with 20+ daily alerts requiring manual triage. Conventional rule-based approaches failed to account for legitimate edge cases like VDI infrastructure and internal networks.
Solution
Developed advanced risk scoring system using Splunk components:
Weighted Risk Factors: Dynamic scoring based on city history, device familiarity, and 30-day patterns
Historical Context: User-specific baseline analysis for legitimate travel patterns
Edge Case Handling: Allowlisting for VDI infrastructure and internal network ranges
Threshold Management: Collaborative risk acceptance with incident response teams
Statistical Validation: Measurable reduction from 20+ daily alerts to 1-2 actionable incidents
The system leveraged Splunk's internal components with sophisticated statistical analysis.
Key Metrics
85% reduction in false positives
95% retention of true positive detections
60% decrease in analyst investigation time
Improved alert confidence score by 75%
Security Impact
Enhanced security team efficiency and effectiveness by reducing noise and enabling focus on genuine security threats.
Results
Achieved 85% false positive reduction validated through historical ticket system analysis. Transformed overwhelming alert volume into manageable, actionable incidents while maintaining security coverage through accepted risk thresholds.
