Private AI for Accounting Practices

Private AI limits who can use your practice’s information and how it is handled. The deployment, written terms and settings determine those limits.

A promise that client data never leaves the practice needs a precise explanation. Data sent to a provider’s servers has left your computers, even when the provider promises to exclude it from model training.

What Private AI Means

Private AI describes restrictions on access to data and its use by an AI service. Treat the phrase as the start of an assessment. Ask the vendor to define exactly what it protects.

Separate the contractual promise from the technical arrangement. A contract can prohibit training on your prompts. Network restrictions can prevent an AI server from sending prompts to an external service. Each answers a different question.

A no-training promise alone does not explain how long conversations remain stored. It also leaves open who can access them for support or security investigations.

For example, consider a hypothetical practice using AI to summarise a client letter. A staff member uploads the letter to a business account. The provider excludes it from training but retains the chat. That arrangement still involves external storage and processing.

The Office of the Australian Information Commissioner’s guidance on commercially available AI products calls for due diligence on personal-information handling. It also recommends against entering personal information into publicly available generative AI tools.

Private AI Deployment Options

The deployment option determines where the model runs and who operates it. Compare that arrangement separately from the promises covering information entered into it.

A business plan can provide useful restrictions without allocating separate computing hardware. Ask whether isolation applies to stored documents, model processing or both. A separate workspace or login says little about the underlying arrangement.

A dedicated instance can separate workloads while leaving support access and monitoring with the provider. The contract must describe that access. Confirm whether a separate instance includes the model itself or only the application around it.

Practice-controlled cloud infrastructure gives your IT team more configuration responsibility. A model in your cloud account can still call an external service. Map each connection before treating the whole arrangement as private.

A private cloud can also operate outside your premises. The NIST definition of cloud computing allows private cloud infrastructure to be managed by the organisation or a third party, on or off premises. The label does not establish an Australian storage location.

A local model is useful when processing must stay on practice hardware. The local LLM guide covers that setup. Include document extraction and chat storage in the same assessment, because the model is only one part of the work.

Private AI Platforms for Enterprise and Small Practices

An enterprise private AI platform can add central staff administration, access records and policies for connected documents. Ask for a demonstration using the plan you would buy. An enterprise label alone establishes none of those features.

For a larger practice, test access across teams. A staff member with permission to use AI must only retrieve documents they are authorised to read. Test a restricted folder using a separate staff account and fictional documents.

A small practice can start with one narrow task and an approved business service where its terms meet the task’s requirements. Drafting a generic client reminder needs less information than summarising an identifiable client file.

Choose a dedicated environment when an identified access or contractual requirement warrants the extra cost and administration. Choose practice-controlled infrastructure only when someone can maintain it. Local ownership brings responsibility for updates and recovery as well as privacy settings.

Consider a hypothetical four-person practice drafting standard correspondence. Start with fictional examples and approved templates. If that solves the task, avoid adding client records simply to make the output more personalised.

Testing a Vendor’s Privacy Claim

Test a vendor’s privacy claim by asking for written answers tied to the exact service and plan. Compare those answers with its contract and the settings available to your administrator.

Use these questions when the vendor says your information stays private.

Then trace one fictional client document through the proposed service. Identify where it is uploaded, where the model reads it and where the answer is saved. Include any search connector or document-reading service in that trace.

Ask the vendor to demonstrate retention settings and deletion using the fictional file. A deleted chat disappearing from the screen proves only that visible removal. Use the written deletion terms to establish what happens to other copies.

Keep the service’s answers with your approval record and set an owner for reviewing changes. Use an AI risk assessment for the wider decision about accuracy, staff use and client obligations. Approve the deployment only for the information and task your assessment actually covers.