AI-Based Real-Time Monitoring
24/7 machine learning-powered anomaly detection that identifies threats in real-time before they impact your network.
Intelligent Threat Detection
Traditional rule-based monitoring systems can't keep pace with evolving threats. Our AI-powered platform uses advanced machine learning to detect zero-day attacks, new fraud patterns, and sophisticated threats that signature-based systems miss.
ML-Based Detection
- • Unsupervised learning for anomaly identification
- • Pattern recognition across millions of calls
- • Adaptive models that evolve with new threats
- • Behavioral baseline establishment per account
Real-Time Analysis
- • Sub-second threat assessment
- • Live call stream processing
- • Distributed processing for scale
- • Immediate alert generation
Regulatory Compliance Impact
Advanced monitoring capabilities support regulatory compliance requirements including:
- • 47 CFR §64.1600: STIR/SHAKEN fraud detection requirements
- • FCC Order 19-76: Proactive threat identification and response
- • 18 U.S.C. § 1343: Detecting wire fraud patterns for law enforcement reporting
- • NIST Cybersecurity Framework: Continuous monitoring and incident detection
What We Detect
Account Compromise
Identification of unauthorized account access through login anomalies and credential abuse patterns.
Traffic Anomalies
Detection of unusual call volumes, destination changes, and calling patterns inconsistent with account baseline.
Network Intrusions
Identification of SIP attacks, brute force attempts, and infrastructure compromise attempts.
Zero-Day Threats
Discovery of new attack patterns and previously unknown fraud techniques before they become widespread.
ML Model Pipeline
Feature Engineering
Extract behavioral signals from millions of call events: timing, destination, caller patterns, network metrics.
Baseline Establishment
Models learn normal behavior for each account, including time-of-day patterns, typical destinations, and volume ranges.
Real-Time Inference
Every incoming call is scored against the baseline model. Deviations receive threat scores calculated in milliseconds.
Continuous Learning
Models retrain daily on validated threat data. False positives are incorporated to improve precision over time.
Automated Response
High-threat calls trigger automatic actions: alerting, blocking, rate limiting, or account suspension based on severity.
Comprehensive Dashboard
Real-Time Metrics
- • Live threat detection rates
- • Network health indicators
- • Call volume trending
- • Account risk scores
- • Incident timeline visualization
Reporting & Insights
- • Detailed threat analysis reports
- • Trend identification and forecasting
- • Compliance audit trails
- • Custom alert configuration
- • Historical data retention