Lilly Tech Systems
AI-Powered Alert Triage
Deploy intelligent alert classification systems that automatically categorize, prioritize, and route security alerts, dramatically reducing analyst workload and response times.
Automated Classification
ML models classify incoming alerts into actionable categories based on historical analyst decisions:
| Category | Action | Example |
|---|---|---|
| True Positive - Critical | Immediate analyst attention + auto-containment | Active ransomware encryption detected |
| True Positive - High | Priority queue for next available analyst | Credential theft attempt from unknown IP |
| True Positive - Medium | Standard investigation queue | Suspicious PowerShell execution on workstation |
| Benign/FP | Auto-close with documentation | Legitimate admin tool triggering detection rule |
Priority Scoring Model
Alert Attributes
Score based on detection rule confidence, attack technique severity, and historical true positive rate for this alert type.
Asset Context
Weight by target asset criticality, data sensitivity, and business impact. A hit on a domain controller scores higher than a test server.
Threat Intelligence
Boost scores when alert indicators match active threat campaigns, known APT infrastructure, or recently published vulnerabilities.
Behavioral Context
Factor in whether the associated user or entity has other recent anomalies, creating compound risk scores.
Intelligent Routing
Skill-Based Routing
Route alerts to analysts based on expertise. Network alerts go to network specialists; malware alerts go to reverse engineers.
Workload Balancing
AI distributes alerts evenly across the team, accounting for current caseload, shift timing, and analyst availability.
Escalation Logic
Automated escalation when alerts remain unacknowledged or when correlated signals suggest an emerging incident.
Auto-Resolution
Automatically close alerts that match known false positive patterns, with full audit trail for compliance.