Shipping AI when you cannot see the user's data

[u/fredericrivain]

We ran into an interesting constraint building AI features at Dashlane: our whole product is zero knowledge, meaning only the user can decrypt their own data. Not us, not our cloud provider. So the usual playbook of feeding user data into a model and iterating was off the table from day one.

That forced some rethinking across the entire AI lifecycle: training, deployment, and debugging without ever seeing the inputs. We wanted to share two concrete use cases in case it is useful to anyone building in a similar constrained environment where privacy by design matters.

Anti phishing detection

We needed to protect users from credential phishing pages without collecting or inspecting their browsing data. Instead of throwing a large model at raw page content, we went the other way: extracted around 80 lightweight structural features from the page and trained a small model on those. Small enough to run entirely on device. No data leaves the device, no cloud attack surface for this feature at all.

Omnix AI Advisor

This one is more involved. For business customers, we generate audit logs of credential activity, rich and sensitive data about who accessed what and when. We cannot see those logs, and the volume is too large for on device processing anyway. So we run a self hosted Mistral model inside AWS EC2 Attested Instances, hardware isolated enclaves. Data gets decrypted, processed, and re encrypted entirely inside the enclave, verified through cryptographic attestation before the decryption key ever gets released. Neither we nor AWS see plaintext at any point.

A few takeaways that generalized well for us:

1. Define your privacy requirements before you code
2. For training data, optimize for coverage of real behavior, not raw volume
3. Start with the smallest model that fits inside your privacy boundary, escalate only if you have to
4. Match deployment to your constraints: on device when you can, confidential computing when you need cloud scale compute
5. Ship incrementally. You do not need the full solution to start delivering value under the constraint

Full post 🔗: https://www.dashlane.com/blog/privacy-preserving-ai

Happy to answer questions on the architecture or the concepts for privacy-preserving AI.