Local AI: Using Artificial Intelligence Without Sharing Data

Many organisations want to use artificial intelligence on their internal information, yet hesitate to send contracts, records, reports or emails to an external service. There is an alternative: run the models on the organisation's own equipment, so that the data does not leave it.

• With a local model, information is processed inside the organisation and is not transferred to a third party.
• Not every model is suited to real work; they should be evaluated on concrete tasks before adoption.
• It is now possible with hardware that a small business can afford.

The underlying problem

Cloud AI services are convenient, but they involve sending information outside the organisation. For many uses that is not a problem. For others it is: customer data, financial information, employment records, clinical records or documents carrying a duty of confidentiality. In those cases the prior question is where the information is processed, who can access it and what happens to it afterwards.

The legal obligations that apply depend on each organisation and its sector, and interpreting them is a matter for legal counsel. What technology can do is reduce exposure by design: if the data does not leave, there is no third party from whom to demand guarantees.

What "local" means

A local model is an AI model that runs on equipment the organisation controls, whether in its offices or on its own infrastructure. The user types a query, the machine processes it and the answer comes back, without passing through an external service.

This brings three things:

• Control. The organisation decides who has access, what is logged and for how long it is kept.
• Confidentiality. The information stays within its perimeter.
• Independence. The organisation does not depend on an external provider's terms to keep using the tool.

What it is for, and what it is not

Local models help with language tasks on the organisation's own information:

• Summarise long documents and locate information within them.
• Draft communications or reports for human review.
• Classify and sort correspondence or requests.
• Answer internal queries about documented policies and procedures.

They also have limits. They can be wrong, invent data that sounds plausible and omit relevant information. For this reason AI is used as support for people's judgement, with review of what it produces, as set out in AI risk analysis in mining. No promise is made that it will improve a particular result; this is confirmed in each organisation's actual work.

Evaluate before adopting

IT Lab works on local AI and evaluates which models are viable for real work. The criterion is practical: it is not enough for a model to work in a demonstration; it must respond well on the organisation's tasks, with acceptable response times and on the equipment actually available. A reasonable evaluation considers:

• Which tasks are to be supported and how usefulness will be measured.
• What quality of response is needed and which errors are tolerable.
• Which equipment is available and what would additionally be required.
• Who will review the results and how problems will be recorded.

Test data and development

When trying an AI tool, the first trials should avoid using real information unnecessarily. The principle is the same as in software development: work with synthetic or anonymised data during evaluation, as explained in Synthetic test data, and move to real information only when the configuration is tested and access is defined.

Sectors with sensitive information

In organisations that handle especially delicate data, such as those in healthcare, local AI is a natural way to explore uses without widening the circuit through which information flows. The sector context is described in Clinical data security. Even so, running a model inside the organisation does not remove the need to control who accesses it and what data is given to it.

Practical checklist

• Identify one or two concrete tasks where AI would add value.
• Classify the information involved and decide which must not leave the organisation.
• Test first with synthetic or anonymised data.
• Define who may use the tool and what record is kept.
• Establish that relevant results are reviewed by a person.
• Measure real usefulness in the work before widening use.
• Periodically review whether the model still does its job.

Frequently asked questions

Is very expensive equipment needed? For a small organisation, affordable options exist. What is suitable depends on the tasks and is defined in the evaluation.

Is a local model as capable as a cloud one? Not always. For many language tasks on the organisation's own information it is enough; for others it may fall short. That is why it is evaluated on real work.

Do we have to give up the cloud entirely? No. The two can be combined: what is sensitive stays local and the rest stays where it suits.

Who is responsible for what the model says? The person who uses it. The tool supports; it does not decide.

General guidance; it does not replace legal advice.

IT Lab cares for its clients' data as much as they do, or more, and supports the adoption of AI without information leaving the organisation. Let's talk.