Protect · Cyber · Secure & Scale AI
AI Data Guard
Control data exposure, model output, AI cost and downstream action through a policy point that remains under enterprise authority.
The problem
Shadow AI is a traffic problem before it is a policy problem.
Every team now reaches for AI. Requests leave the estate carrying data that was never meant to leave, to models nobody approved, at a cost nobody planned.
Unsanctioned traffic never reaches a SaaS governance service, so it cannot be governed there.
What Data Mediation changes
Policy applied where data crosses the AI boundary.
An inline Data Mediation point applies policy to every request and response. Sensitive information is masked, requests are constrained, model and token use is governed and outputs are inspected or redirected before they create impact.
What it does
Control at the AI boundary.
Sensitive data masked before it leaves
Each model receives what policy permits and nothing more. No application changes. No data migration.
Shadow AI seen at the edge
A Data Mediation point at internet egress sees every model-bound request. Each can be blocked, coached, mediated or rerouted to an approved model.
AI cost governed by policy
Model choice and token use follow the organisation’s rules rather than individual habits.
Outputs checked before they act
Model responses are inspected and can be redirected before they trigger downstream action.
Every model, one policy
AI traffic over HTTPS and SSE is one more protocol Data Mediation supports, so one policy covers every model.
Listen before acting
Listen Only gives full visibility of AI traffic without blocking or altering it.
Secure & Scale
AI scales with confidence once its interactions are secure.
AI cannot scale confidently without securing its interactions with enterprise systems. Data Mediation secures them in the data path, so adoption accelerates instead of waiting for every risk to be closed first.
Where is the control applied?
AI policy lives in three places. Only one sees everything.
| Where the policy lives | How it is applied | What it reaches |
|---|---|---|
| Policy on paper | Written down, agreed and reviewed. | Not enforced on any request. |
| Policy in vendor platforms | Enforced inside each vendor’s own product. | Only traffic that reaches that vendor. |
| Policy in the network | Enforced at the Data Mediation point. | Every request and every response, for every model. |
Not SaaS. AI Data Guard runs inside the organisation’s own environment: cloud, sovereign, on-premises or air-gapped. Nothing is sent to TomorrowX.
Built for
For the people accountable for AI risk.
- Chief Information Security Officer
- Chief AI Officer
- Chief Data Officer
- Chief Information Officer
- Chief Risk Officer
- Head of Security Architecture
- Enterprise Architect
- Data Protection Officer
- Head of AI Platform
- Head of Cloud FinOps
Works with
- Any AI model or provider, external or private: Claude (Anthropic), OpenAI, Gemini (Google), open-weight and locally hosted models among them
- Any protocol: AI traffic is one more protocol on a multi-protocol platform
- Identity providers such as Okta
- Data Mediation for NVIDIA OpenShell
Plans
Start by seeing the traffic.
TomorrowX licenses its software through selected partners, who configure, deliver and support it. US public-sector customers buy through Carahsoft.
Proof of value
AI Data Guard proven on the organisation’s own data and use case.
Contact sales →Listen Only
Full visibility of AI traffic at a Data Mediation point. Observes; never blocks or alters.
Contact sales →Active
Policy enforced at the Data Mediation point: masking, model and token governance and output inspection.
Contact sales →Additional Data Mediation points
The same controls extended to further network locations as adoption grows.
Contact sales →
Partners and customers
Delivered through partners. Proven with customers.
Services, marketplace and public-sector partners deliver Data Mediation, with OEM and managed-service models available. TomorrowX also engages directly with selected customers on world-first deployments and research activities.
Partners and delivery models → · Enterprise history and customers →
From the library
The full library →Next in Protect · Cyber