It is an operating model in which AI components run on infrastructure the organisation controls itself or has operated on its behalf.
Local AI infrastructure
On-premises AI: design your own models and data paths
On-premises does not automatically mean offline, nor automatically compliant. Its advantage is design control: data paths, permissions, models and operating processes can be deliberately defined.
Technical prerequisites
What is clarified before starting
- Owners for infrastructure and operations
- Network and access-control concept
- A process for updates, logging and backups
Typical use cases
Where this architecture is used
- Use cases involving sensitive internal information
- AI usage inside established internal networks
- Gradual development of in-house AI capability
At a glance
Technical context without false precision
| Data path | governed through architecture and network segmentation |
|---|---|
| Models | selection and approval within your own operating framework |
| Operations | your own responsibility with optional maintenance |
Scope and limitations
What this solution intentionally does not promise
Local operation does not automatically eliminate every risk. Inputs, output quality, supply chain, user access and operations still need evaluation.
FAQ
Frequently asked questions
No. Purpose, legal basis, technical and organisational measures, and contracts still require a concrete assessment.
Sources and technical context
These sources support technical context. They are not a project commitment and do not replace individual assessment.