Let your people use AI. Keep your data.
Griffin discovers shadow AI across your estate, enforces data policy before a prompt ever leaves the business, routes each workload to an approved model, and meters every token by team.
The gap
AI adoption has outpaced AI governance.
Confidential data is already leaving
Client records, source code and board material are pasted into public AI tools every working day, outside any inspection point.
Accounts are unmanaged
Staff sign up with personal credentials. Security has no inventory of which tools are in use, by whom, or against what data.
Token spend is ungoverned
Model costs accumulate across teams with no attribution, no ceiling and no way to distinguish valuable workloads from waste.
The platform
Four pillars of a governed AI estate.
Protect
Inspect every prompt, attachment and response in line, and block or redact regulated data before it leaves the business.
Learn moreTransform
Redact, tokenise and minimise payloads so work continues with full utility and without exposing identifiable detail.
Learn moreRoute
Direct each workload to an approved model based on data class, task complexity and the cost ceiling you set.
Learn moreGovern
Record every decision, attribute token consumption to teams and produce evidence your auditors will accept.
Learn moreHow it works
Six steps, applied to every interaction.
01
Discover AI usage
Identify every AI tool, account and integration across the estate.
02
Inspect the interaction
Read the prompt, attachments and context before egress.
03
Classify risk
Determine data class, regulatory exposure and sensitivity.
04
Enforce policy
Allow, warn, redact or block according to your rules.
05
Route to the right model
Select an approved model that fits the workload and budget.
06
Meter and report
Attribute tokens and cost, and retain the audit record.
Where existing controls stop
Your current stack was not designed for the AI interaction.
Each of these categories does useful work, and several vendors are extending into this space. The table below sets out where each approach reaches its limit, so you can judge what your existing controls already cover.
| Approach | Limitation |
|---|---|
| Traditional DLP | Built for files and email. It cannot read the semantics of a prompt or intervene inside an AI conversation. |
| CASB / Secure Web Gateway | Blocks or permits a domain. It has no view of what was sent, no redaction, and no model-level control. |
| AI gateway | Manages API traffic from engineering. It does not cover the browser and desktop usage where most exposure occurs. |
| FinOps platform | Reports spend after the fact. It exercises no control over data, model selection or policy at the point of use. |
| Griffin | Inspects, transforms, routes and meters the interaction itself across browser, desktop and API, with a complete audit record. |
Outcomes
What security leadership gets.
Reduce the risk of confidential data leaving the business.
Increase approved AI adoption instead of driving it underground.
Preserve productivity by transforming data rather than blocking work.
Cut frontier-model spend by routing to proportionate models.
Attribute token cost to the teams that generate it.
Improve audit readiness with a complete record of decisions.
Every enterprise will need a control plane for AI.
Griffin is working with a small number of design partners in regulated sectors. If you are accountable for how AI is used across your organisation, we should speak.