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A global telecom firm struggling to quickly identify unauthorized network usage amid increasing cyberattacks across multiple subsidiaries.
Challenge
Data centralization is a time-consuming and expensive process with subsidies in different data jurisdictions which use different cloud providers.
Centralized model training creates long lead times for deployment that mean updated models come too late to identify novel attacks.
Lagging time to analysis means that cyber criminals access networks for months before anomalies are detected - creating legal, reputational, and operational risks.
Solution
Devron could deploy Synergy to securely train models across datastores in multiple jurisdictions without exposing data through costly centralization.
With faster access to more training data, time to deployment decreases while Synergy identifies suspicious activity with over 95% accuracy.
Continuous model updates would also allow the proactive identification of unauthorized activity and novel attack vectors.