- 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.
- 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.
Increase Model Deployment Speed
Improve Prediction Accuracy
Lower Cyber Risk
Reduce Potential Financial Losses