Private AI, Copilot, or Gemini?

If the work happens in Microsoft 365 or Google Workspace and the business service’s terms fit your requirements, start by evaluating the assistant already integrated with that platform. Consider a private deployment when you need a different processing boundary, a locally operated model, or a specifically scoped document workflow.

This comparison covers Microsoft 365 Copilot, now named Microsoft Copilot for the business service, and Gemini with qualifying Google Workspace business editions. Consumer accounts, preview features, agents, and third-party connections can have different terms. Confirm the exact account, edition, and feature your staff will use before approving client data.

Microsoft states that prompts, responses, and data accessed through Microsoft Graph are not used to train foundation models in Microsoft 365 Copilot. It also states that Copilot surfaces organizational data the user already has permission to view. These commitments are documented in Microsoft’s Copilot privacy guidance.

Google states that Workspace customer data is not used to train or improve the underlying generative AI models outside Workspace without permission. Gemini retrieves Workspace data the user is permitted to access. Read Google’s Workspace AI privacy commitments alongside the terms for your edition.

Permission-aware retrieval still depends on correct source permissions. An overly broad SharePoint group or shared Drive folder remains broadly accessible. Review client-folder membership, external sharing, connected applications, and retention before enabling an assistant over those sources.

A private deployment lets you select the hosting environment, model, approved document sources, and application controls. That flexibility also creates operational work: capacity planning, updates, access testing, recovery, monitoring, and support need named owners.

Data handling depends on the complete configuration. Check whether embeddings, OCR, web search, telemetry, and support tooling call external services. A private model cannot keep files inside the environment if another component sends them out. The deployment architecture guide maps those dependencies.

All three approaches still need human review for consequential output. Evaluate the actual task with known source material, including questions where the correct response is that the available documents do not contain an answer.

Compare total operating cost for the same task and user group. Include licences, infrastructure, implementation, data cleanup, staff review time, support, and ongoing evaluation. A per-seat subscription and a hardware purchase cover different parts of the work.

A firm can approve different tools for different information. For example, an explicitly hypothetical policy could allow a business assistant for ordinary internal drafting and reserve a separate environment for a restricted document collection. The client-data guide helps define the boundary without relying on a product label.

Choose a task, name the data owner, record the service and configuration, and test access and answer quality with representative material. Keep the test result and limitations with the approval record.

If Copilot or Gemini is the right fit, Copilot and Gemini Security Hardening addresses the surrounding access and configuration. If a separate environment is needed, scope managed Private AI. An AI Governance and Workflow Readiness Review can establish the decision criteria before either rollout.