Imagine a Fintech world where your KYC, Credit Scoring, AML or any other AI-driven solution is protected against data leaks.
With Wodan AI’s technology, Fintech providers can easily:
There are many Fintech (and Insurtech) use-cases for the secure AI platform of Wodan AI. Every financial services business that cares about data privacy and fears data leaks when using AI should be talking to us.
Some applications we provide solutions for are:
Wodan’s software solution called “Dropnir” (the golden ring of Wodan that gives him all the power) sits in between your application and the AI model.
Dropnir serves as a thin layer between the application and the model. The customer doesn’t notice any difference in behavior from talking to the model or to Dropnir.
The software is easy to install (as a container) and serves as a thin API layer.
The model performs its magic but it does it over encrypted data! The model then sends an answer back to Dropnir, again fully encrypted. The owner of the model was never able to view the request, the data, or the answer.
Dropnir decrypts the result using the private key from the customer, and provides a clear-text readable answer to the customer’s application.
Wodan’s SaaS platform is used for metrics collection on how much data and requests are transmitted through Dropnir, and provides easy pay-per-use billing.
Why FHE in Federated Learning?
1. Enhanced Privacy:
FL already ensures data stays on the client side, but transmitting gradients or model updates to a central server can still leak sensitive information through inference attacks.
FHE adds an extra layer of security by ensuring that even the server cannot access the raw gradients or updates—it only processes encrypted data.
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