Security is in our DNA.
Every decision we’ve made has treated security and privacy as architectural
requirements, not add-on controls. Savo™ was built to protect candid human
narratives by design — with structural separation between identity, content, and
metadata, strict access controls, and AI governance built into the product.
Built with enterprise-grade security in
mind from day one
"Trust is our foundation, with multi-tenant isolation and full encryption." There is another bug I created on this. You may want to reference it to make sure they are the same. No customer data is ever used for Large Language Model (LLM) training.
Participant identity, participant content, and analytical metadata are separated by design.
No component in our architecture implicitly trusts another. Every request is authenticated, every permission is verified, and every data access is scoped.
Every user, every service, every API key operates with the minimum access required. Our role-based access control system enforces five tiers of data sensitivity, from public metadata down to restricted biometric data.
We design as if perimeter defenses will eventually fail — with rapid triage, containment workflows, and layered isolation limiting blast radius.
Your data never trains
our models.
This is non-negotiable and we enforce it technically. Every API call to our LLM
providers includes explicit no-training parameters.
Still have security questions?
Our security and compliance team is ready to walk you through our architecture,
governance policies, and deployment standards.