Encrypted context, memory, and vector DB for every model and agent
Knowledge bases, vector stores, conversation history, agent memory. Every piece of context an AI reaches for is plaintext the moment it's loaded.
For engineering leadership
AI infrastructure you can describe in one diagram.
Keys sprayed across teams. Each app integrates models its own way. No single place that knows what was asked, what came back, or whether it was allowed.
One gateway, one policy plane, one trace. Real platform, not a wiki of integrations.
Use cases for this audience
8 scenarios
Fine-tune on customer data without ever seeing it
Domain models need real customer data to be useful. Training in plaintext means you hold what you swore you wouldn't.
Stop the wrong agent action at runtime, not in a dashboard
Agents act with standing access. The most you get back today is a chart explaining what already broke.
Govern what your agents reach for
MCP servers, plugins, and SaaS tools quietly extend agent blast radius. Almost nobody has a policy at that boundary.
One endpoint for every model, with policy at the door
Each team hits its own provider its own way. Keys sprayed across services, prompts egressing in the clear, no single place that can say what was asked or whether it was allowed.
Pick the encryption posture per workload, not per gateway
Some routes need raw speed. Others need FHE all the way through. You shouldn't have to pick one for the whole org.
Let AI review code that never leaves your machine
To use an AI code tool you usually hand it your source, secrets, and proprietary logic. And trust a retention promise.
Stop your model from being cloned out the back door
Distillation attacks turn your API into someone else's competing model in weeks.
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See encrypted AI security in action.
FHE-native inference. Runtime agent guardrails. Continuous red teaming. One platform. Book a working session with the team.