Private AI for Legal Teams: Secure and Local Options

What Private AI Means

Private AI needs defined limits on model training, retention, storage location and access. A label such as ‘secure AI chatbot’ does not tell you which of those protections applies.

For a legal team, the approval record needs to answer four separate questions:

• Can the provider use prompts, uploaded files or answers to train models?
• What remains after a session, including saved chats, logs and backups?
• Where does processing occur, and where is stored material held?
• Which staff, administrators and providers can access each matter’s material?

A service can exclude training while retaining chat history. Encryption during transmission protects the connection, while encryption at rest protects stored data. Neither alone prevents a provider from reading material during processing.

Confidential computing adds a different protection. A trusted execution environment, or TEE, isolates processing inside protected hardware. An encrypted connection that ends inside that environment can keep prompt content hidden from the service operator.

Location is another separate decision. Australian hosting describes where infrastructure sits. Sovereign hosting also concerns who controls it and which legal powers apply. The data sovereignty guide explains that distinction.

Compare Private AI Options

Compare the deployment model before comparing individual chatbots. The main trade-off is how much control your firm needs against the cost and work of operating it.

Option	Privacy control	Cost structure	Capability	Work for the firm
Enterprise chatbot	Contractual data protections and organisation controls	Seats, sometimes plus usage	Ready-made assistant with provider-selected models	Configure access and retention
Private cloud deployment	Private networking and tenant-controlled resources	Model usage or reserved capacity, plus infrastructure	Custom document workflows and model choice within the service	Build and maintain the application
Australian-hosted service	Local processing and storage within the agreed service	Managed seat bundles or a hosting quote	Depends on hosted models and document features	Approve the service and manage users
Local models	Prompts remain on firm-controlled computers when run offline	Hardware and support, with free local software available	Depends on model size and available memory	Secure devices and maintain models

Enterprise Chatbot Plans

Claude Enterprise is one representative provider-hosted option. It combines a general assistant with organisation access controls and custom retention. Anthropic’s plan comparison excludes model training on enterprise content by default.

The published Enterprise price is USD $20 per seat per month, billed annually, with a 20-seat minimum. Model usage costs extra at API rates. Applicable taxes are excluded. That minimum makes the Enterprise plan a different commitment from buying a personal subscription for each lawyer.

Retention settings also need deliberate configuration. Anthropic’s retention guidance says data is retained indefinitely by default unless a custom period applies. Projects and standalone chats can follow different periods. Enterprise branding alone does not establish Australian hosting or prevent the provider from processing your material.

This route suits a firm that wants a managed assistant for drafting and summarising approved documents. Access to connected document stores still needs matter-level permissions. Giving a chatbot access to the whole firm’s files can defeat the controls already used to separate matters.

Private Cloud Deployments

Microsoft Foundry illustrates the private cloud route. Microsoft’s data policy isolates prompts and responses from other customers and the model providers for models sold by Azure. Foundation-model training requires permission.

Private endpoints can restrict network access to an Azure resource. They do not turn hosted processing into an on-premise model. The deployment type controls processing geography, and features that store files or conversation history have their own retention behaviour.

Azure pricing includes payment for input and output tokens or reserved throughput. Application development, document storage and operations add to that bill. This route fits a legal department with technical support for a controlled internal assistant. It carries more operating work than a ready-made chat service.

Australian-Hosted and Sovereign Services

Green Racks publishes an Australian-hosted example for law firms. Its Sovereign AI Workspace runs local models in its Osborne Park data centre. The provider says prompts, documents and outputs stay in Western Australia, with backups also held there.

The workspace has limited beta access. Its published commercial options combine seat bundles with either a minimum term or a setup fee. GPU hosting is quoted separately. Entry-level workspace capacity is shared, so the provider warns that responses can slow during busy periods.

This option suits a firm that needs managed local hosting without owning the machines. Local storage does not by itself establish a deletion period or exclude operator access. Those controls need to be part of the service agreement alongside the Australian location commitment.

Privacy-Focused and Anonymous Chatbots

Venice separates privacy into distinct modes. Its Anonymous mode hides your identity from the underlying model provider, but Venice says to assume that provider stores the content. Private mode instead uses contractual zero-retention commitments.

Venice’s paid Pro access also includes TEE and end-to-end encrypted modes for supported models. In the latter mode, prompts are encrypted on the device and decrypted inside a verified TEE. Venice says it cannot see that content. The trade-offs include fewer models and the absence of web search and memory in end-to-end encrypted mode.

That distinction changes the choice for confidential work. Selecting a different model can change the privacy mode. A Venice account alone does not give every conversation the same protection or establish Australian processing.

Duck.ai is another anonymous chat service. DuckDuckGo removes identifying metadata before forwarding requests and prohibits its model providers from training on those chats. The protection concerns identification and permitted data use. Client details typed into the conversation still form part of the content sent for processing.

Duck.ai has free access with limits and paid subscription access. Its convenience suits general questions or work with material already cleared for that service. An anonymous session does not supply your firm’s matter permissions or an Australian hosting commitment.

Lumo AI protects saved chat history with zero-access encryption and runs models on Proton-controlled servers. Proton says it erases processing data after generating an answer and excludes training, apart from an expressly consented feedback option. Its privacy documentation places the service in Europe.

Lumo has free and paid plans. Its encrypted history protects stored conversations, while the GPU servers decrypt prompts to process them. That is a different protection from running a model on your own hardware or an attested TEE.

For retention terminology across hosted products, the zero data retention guide separates ordinary chat storage from the terms that apply to model requests.

Local Models in Brief

Local models keep prompts and document processing on the firm’s own computers when cloud features and external connections are disabled. A local large language model, or LLM, can run on a workstation or a firm-controlled server.

Ollama supports a local-only configuration that disables its cloud models and web search. LM Studio documents offline chats and local document processing after model files are downloaded. Both have free local-model options alongside separate cloud services.

The firm pays for suitable hardware and the people who maintain it. Available memory limits which models fit and how much material they can process at once. Device security, saved chats and backups remain the firm’s responsibility. The local LLM guide explains the practical setup and hardware choices.

Choose an Option for Your Firm

Choose an enterprise chatbot when the firm needs a managed assistant and its written data terms fit the work. A small practice can start with a limited approved use, such as rewriting a non-confidential template. Size alone does not decide whether client files belong in it.

A larger legal department with technical staff can justify a private cloud application when it needs controlled access to internal documents. Its budget must cover maintenance and access management as well as model usage.

Choose an Australian-hosted service when local processing and storage are firm requirements and you want the provider to operate the infrastructure. Choose an offline local model when the material must remain on firm-controlled equipment. Sensitive matters can justify that separation even in a small team.

Anonymous chat is useful for keeping account identity away from model providers. For confidential material, the deciding facts are who can read the content and what survives processing. End-to-end encrypted inference addresses provider visibility, but the firm still needs acceptable terms and access controls.

Deployment privacy also says nothing about the accuracy of legal authorities in an answer. If your next decision concerns research capabilities and source coverage, compare legal AI tools separately. Approve each deployment for a defined class of material before staff begin using it on client work.