When a firm uses a public AI service, the prompt and any attached material are sent to infrastructure operated by that service provider. With on-premises AI, the computing equipment and model are hosted within the firm’s own premises or network boundary, so requests can be processed locally rather than sent to a public AI endpoint.
That difference can matter when teams work with client documents, internal records or commercially sensitive information. But “on-premises” is a description of deployment location. It does not automatically establish that every component is private, that a system is secure, or that a particular use complies with data protection or professional obligations.
What local processing changes
A local installation gives the firm more direct control over the path between its users, documents and AI system. A carefully designed setup can keep prompts and source files inside its network. It can also make it possible to set access rules around existing identity systems and to decide which folders or repositories an AI workflow may read.
Those outcomes depend on the actual design. A local model might still call an outside service for updates, telemetry, authentication or a feature that was not obvious at first. A connected application may transfer information even when its language model runs locally. During evaluation, ask for a clear map of each component and its data flows—not just a label on the model.
What local deployment does not remove
Information can still be exposed through a compromised account, an overly broad permission, a lost device, an insecure backup or a poorly handled export. Local systems need patching, monitoring, backups, access reviews and a plan for incidents. Staff need to know what the tool can see and what they are allowed to do with its answers.
Nor does an on-site server decide whether a use is lawful. A firm still needs to identify an appropriate purpose and basis for processing, consider relevant confidentiality duties, document decisions and involve its data protection or legal advisers where needed. The right assessment depends on the work and the information involved.
Models and hardware have limits
Local AI is not one fixed capability. Models differ in size, quality, language support and the tasks they handle well. Hardware affects response time, capacity and which models are practical to run. Some demanding frontier models require resources that are not available in a small office installation; choosing locally does not mean every model can be brought on-site.
For many firms, the better question is not “Can we run the largest model?” but “Can a suitable model help with this bounded task, using the information we can safely make available?” A test using representative, approved material can expose gaps before a workflow becomes part of daily work.
Questions worth asking
- Where does every part of the system run, and what information leaves the network?
- Which users, files and actions can the system access?
- How are software updates, backups, logs and incidents handled?
- What should a human verify before an answer is used or sent?
- How will the firm assess accuracy and suitability for its particular work?
Private AI can be a useful way to put automation closer to sensitive work. The responsible approach is to define the boundary precisely, test the workflow honestly and keep ordinary security and governance practices firmly in view.