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Connect an enterprise AI assistant by choosing how it will reach each source—through indexed sync, runtime federation, or a custom API/MCP integration—then enforce permissions, limit actions, and test the setup with representative users before rollout. The right design depends on your repositories, identity system, data-handling requirements, and the assistant’s product edition; no single connector model fits every organization.

Choose how the assistant will connect

The connection model determines where content is available, how current it is, how access is checked, and what work administrators must maintain. A platform may offer more than one model, while a specific connector or edition may support only a subset.

Pattern How it works Best fit Key trade-off
Indexed connector Copies or syncs external content into a platform search or knowledge layer. Microsoft describes its indexed Copilot connectors as ingesting external items into Microsoft Graph, with a registered schema and configured search or activity settings. Unified discovery across connected content and the platform’s existing sources. Administrators need to understand ingestion, indexing, freshness, and how permissions are represented and filtered. [Microsoft Learn]
Federated, runtime connector Fetches information from an external system when a user asks, without indexing that data into Microsoft 365. Microsoft describes its federated connectors as using MCP, the user’s identity, and the user’s source-system permissions. Live or sensitive information that should remain in its system of record. Results depend on the source, authorization, and connector being available at request time. Action support can vary by rollout and experience. [Microsoft Learn]
Custom API or MCP integration Connects a system or operation not covered by a suitable built-in connector through a custom API or MCP server. Legacy systems, specialized workflows, or sources outside supported catalogs. Requires engineering and security ownership for identity, scopes, tool exposure, error handling, logging, and ongoing maintenance. [OpenAI Help Center; Google Cloud documentation]

Compare candidate designs against data freshness, whether content is copied or indexed, per-user permission enforcement, available read and write actions, administrative controls, auditability, and maintenance effort. A connector appearing in a product catalog does not establish that it is enabled in your tenant or appropriate for your data.

Plan the connection before configuring it

Inventory sources and tasks

List the repositories and business tools the assistant needs, their owners, the user groups involved, and the task each connection should support. Be explicit about whether users need search and read access only, or whether the assistant must also create, update, or delete records. Start with a small, high-value use case and known source content.

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Available catalogs may include sources such as Google Drive and Sites, Microsoft SharePoint, and other work tools. Google’s Gemini Enterprise documentation also describes built-in connectors and chat with files for Google Drive, SharePoint, Outlook attachments, and Box. Catalog availability is not proof of tenant enablement, permission suitability, or support for every action.

Check source permissions first

An assistant can make existing access easier to exercise, so review repository permissions before connecting a large collection. Test with representative users and groups, including people with different roles and access to sensitive material. Microsoft describes permission filtering for indexed connector content and user-identity access for federated connectors, but the behavior must be verified for the particular connector, source, and tenant.

Do not use a model instruction such as “do not reveal confidential documents” as a substitute for source-system access control. The connector and identity design—not a request in the prompt—must determine which content a user is allowed to retrieve.

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Implement the connection in controlled stages

  1. Select a connection model. Use indexing when unified search and the platform’s ingestion model fit the organization’s data policy. Consider federation when live source access and keeping content in place are priorities and both platform and source support it. Use a custom API or MCP integration when a supported connector does not cover the system or operation. Record why the choice meets freshness, sensitivity, and access requirements.
  2. Configure identity and least privilege. Follow the product’s documented authentication method and grant only the scopes and actions the use case needs. Avoid broad credentials where user identity or narrower scopes are available.
  3. Start with retrieval. Enable search or read access first if it meets the need. Treat create, update, and delete operations as a separate capability with separate approval, access, and testing.
  4. Apply data-protection controls. Configure applicable classification and content protections for the sources and connector types in use. Confirm coverage for the actual content and policy configuration rather than assuming a control applies universally.
  5. Test before rollout. Use representative queries, users, groups, content states, and permitted and denied actions. Test in a non-production environment before enabling tools that change records.
  6. Roll out to intended groups and monitor. Have administrators enable approved integrations, grant access to the intended groups, and review available logs and connector changes. Assign an owner for credentials, permission drift, source changes, and incidents.

Configure identity and actions by platform

Microsoft 365 Copilot

Microsoft documents both indexed connectors, which bring external content into Microsoft Graph for search and Copilot use, and federated connectors, which use MCP to reach external data at runtime without indexing it into Microsoft 365. Its documentation describes permission filtering for indexed content, source-identity permissions for federated access, and auditability through Microsoft Purview. Verify the exact connector, tenant, and Copilot experience: write, update, and delete support has rollout-specific availability.

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ChatGPT Business, Enterprise, and Edu

OpenAI’s help documentation describes developer mode and custom MCP apps for supported ChatGPT Business, Enterprise, and Edu workspaces on ChatGPT web. The setup includes providing an endpoint and metadata, selecting authentication, scanning the available tools, and completing OAuth authorization where applicable. Administrators or authorized developers configure the connection; admins or owners publish apps. Enterprise and Edu documentation describes role-based access and controls over app access and actions. Review the available safety and action controls—especially for write operations—and confirm current plan and workspace behavior before deployment.

Gemini Enterprise

Google documents built-in connectors and chat with files for Google Drive, SharePoint, Outlook attachments, and Box, with administrator-enabled actions and user authorization. Its custom MCP setup guide is specifically for Gemini Enterprise Business Edition: it describes registering Gemini Enterprise as an OAuth client, granting scopes, configuring the server endpoint, and using HTTPS with StreamableHTTP. The identity provider must be internet-accessible for the OAuth handshake. Those are edition-specific setup requirements, not universal requirements for every MCP integration.

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Protect data and limit what tools can do

Separate reading from writing

A tool that searches or reads information has a different risk profile from one that changes a record. If actions are necessary, expose only the required operations and limit access to the users who need them. Require suitable human review or confirmation for consequential changes. OpenAI documents action controls for custom MCP apps and says ChatGPT may request confirmation for write or modify actions depending on permissions and context; do not assume identical confirmation behavior across products.

Use applicable data and prompt protections

Google documents Sensitive Data Protection policies for supported connectors, including custom MCP servers, and describes Model Armor screening prompts before model processing and responses before delivery. These controls require configuration and apply to supported sources or content types; check the exact connector and policy setup before relying on them.

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Keep custom integrations operationally owned

For a custom connection, define who owns the endpoint and tools, how credentials and OAuth scopes are managed, what gets logged, how failures are handled, and how changes are reviewed. A connection is ongoing software and security work, not just a one-time URL entry.

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Test the experience before expanding access

Build a small test set from real, representative questions and identify the documents or actions expected for each. Check the result using accounts with different permissions; include a user who should not be able to see sensitive test content.

  • Retrieval: Does the assistant find relevant, current material and provide citations or source links that users can inspect?
  • Permissions: Does a user receive only content they are permitted to access in the connected source?
  • Indexing behavior: For indexed sources, test what happens when content is stale, renamed, or deleted.
  • Runtime behavior: For federated sources, test source outages and expired or revoked authorization.
  • Actions: In a non-production environment, verify both allowed and denied operations, including any required user confirmation.

Vendor feature documentation describes available approaches and setup; it does not establish independent retrieval accuracy or guarantee results for your corpus. Validate relevance and workflow behavior with your own content and users.

Write queries that make the source and task clear

Name the source, time range, team, project, or owner when that context is available. Ask for citations so users can check the answer against the original material. Microsoft Support gives examples such as “Summarize the latest customer issues related to and include citations,” “Find the onboarding checklist for [team/project] and tell me what steps are still open,” and “What are the most recent updates on [initiative] from our wiki or knowledge base?” These are examples from Microsoft, not a measure of how frequently users ask such questions.

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Govern access and review changes after launch

Enable only approved connectors and assign them to intended groups. Monitor access and action logs where available, review connector or tool changes, and keep a named owner for ongoing maintenance. Microsoft says federated connector activity can be audited in Microsoft Purview; OpenAI documents administrator publication and action controls. Confirm which events and retention settings are available for the exact plan and integration.

Product behavior and availability can change by edition, tenant, and rollout. Recheck the current product documentation and settings during implementation, particularly before committing to write actions or promising specific audit coverage. The appropriate platform cannot be selected from connector features alone: source inventory, identity platform, data residency and compliance requirements, deployment model, budget, scale, latency, and permitted actions all affect the decision.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.