Use AI on your data without handing it over
Somebody in your business has already found an AI tool that saves them four hours a week. They summarise charts with it, or draft responses, or pull the important lines out of a forty-page report. It works, it is genuinely useful, and it is almost certainly reading real client information.
The uncomfortable questions arrive later. Where did that text go on its way to the model? Who else could read it in transit? Which model actually answered — the approved one, or whatever the vendor swapped in last Tuesday? And if a client asks what their information was used for, is there a record, or only a browser history?
Safe AI Connections keeps the usefulness and removes the exposure. Prompts and answers travel sealed, so nothing readable sits on a wire, in a log, or in a queue along the way. The model on the other end proves its identity before anything is shared with it. And every exchange is written to a record you can review — so the answer to "what did we send, to what, and when" is a document rather than a shrug.
The engagement, in four lines
- Sealed pipelines between your data and the AI models or agents you use
- Identity checks on the AI itself before anything is shared (model attestation)
- A sealed, reviewable log of every AI input and output
- Works with the AI tools you already pay for — we sit between, we don't replace
Three things happen before your data reaches a model
None of them change how the tool feels to the person using it. They change what exists in the middle.
The prompt is sealed
Whatever your staff send — a chart, a contract clause, a customer thread — is locked before it leaves your system. Nothing readable exists on the way to the model, including in the places nobody thinks about: proxy logs, message queues, retry buffers.
The model is checked
Before anything is shared, the far end proves which model it is and which policy it is running under. If the identity does not match what you approved, the exchange does not happen and the refusal is logged. An unattested peer receives nothing.
The answer comes home sealed
The response travels back the same way and opens inside your system. Both halves of the exchange are written to your audit chain, so a question about what was sent six months ago has a specific answer instead of an estimate.
A prompt on its way to a model, held up to the light
The same envelope carries your prompt to the model and the answer back. Borrow our eyes and move the lens anywhere you like — the content never resolves.
Under the lens: size, timing, route and key fingerprints. The content region stays noise wherever you put it. We can prove delivery; we cannot read it.
Drag the lens with a pointer, or focus the panel and steer it with the arrow keys.
"Which model answered?" should not be an act of faith
The part of AI governance that most policies skip is identity. Vendors change defaults, route traffic to fallbacks, and version models without telling anyone whose job it is to care. We treat the model the way we treat any other peer on a route: it identifies itself, cryptographically, before it is trusted with anything.
The check runs on every exchange, not once at setup. It records the agent identity, the digest of the weights it is serving, and the prompt policy in force — the three things that actually determine what a model will do with what you give it.
When a check fails, nothing is sent. The attempt is written to the same audit chain as your successful exchanges, which means your record shows not only what you shared, but what you declined to share and why. That second half is usually the more useful one in a review.
If you already run a governance policy — approved models, approved use cases, retention limits — this is the mechanism that makes it enforceable rather than aspirational.
// written before a single byte is shared
peer agent-runtime.client
agent_id agt_5f1c
weights-digest 9ab4…7e02
prompt-policy pol_intake_v4
result ✓ attested
// and when it does not match
peer agent-runtime.client
agent_id agt_5f1c
weights-digest c17f…0b93 (unexpected)
result ✗ refused · nothing sentWhat this service does not claim
We do not encrypt your data from the model it is meant for — a model that cannot read a prompt cannot answer it. What we remove is everything else: the readable copies in transit, the unverified peer at the far end, and the missing record afterwards. If your requirement is that no external model may ever see a particular field, the honest answer is redaction before the route, and we will help you draw that line.
Keep the tools. Remove the exposure.
Bring the AI tools your team already uses. We map which of them touch client information, seal those routes, and put an attestation check in front of each model — usually in days, without changing a single workflow.