The challenge
What needed to change
Static PBX rules missed evolving fraud patterns and generated noisy alerts. Security teams needed fast detection that could adapt to normal calling behavior for each customer environment.
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CybersecurityMachine-learning voice protection
PBX Threat Detection System
An AI-powered detection system that identifies and blocks toll fraud, unauthorized access, and suspicious PBX call patterns in real time.
99.7%
Detection Rate
<0.1%
False Positives
50ms
Avg Response
The solution
How we approached it
We built a streaming service that learns calling baselines, scores anomalous events, and applies automated controls through Asterisk and FreePBX integrations. Redis supports low-latency decisions and fast feature access.
Delivery highlights
What we delivered
Adaptive baselines for users, trunks, destinations, and time windows
Real-time scoring integrated with PBX call controls
Investigation views with explainable detection signals
Feedback workflows for continuous model refinement
Services
Threat researchMachine learningSecurity engineering
Business impact
Outcomes that lasted beyond launch
The implementation focused on measurable improvement and an operating model the client could continue to evolve.
01
Blocked high-risk calls before significant charges accumulated
02
Reduced alert fatigue through environment-specific models
03
Improved coverage without replacing existing PBX systems
More work
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