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Connect an enterprise AI assistant by choosing how it will reach each source, enforcing access through the source’s identity and permissions, and testing retrieval and actions before rollout. The main options are indexed connectors, runtime (federated) connectors, and custom API or MCP integrations. Which one fits depends on the platform, edition, source system, data-handling rules, and whether the assistant needs to read information or change records.
Choose how the assistant will connect
These approaches differ in where content is handled, how current it can be, and how much integration work is required. A connector listed in a product catalog is not necessarily enabled in your tenant or suitable for your organization’s data.
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| Connection pattern | How it works | Best fit | Key considerations |
|---|---|---|---|
| Indexed connector | Syncs external content into the platform’s search or knowledge layer. Microsoft describes Copilot connectors that ingest content into Microsoft Graph and configure a schema and search/activity settings. | Unified discovery across connected sources, when indexing is acceptable. | Understand ingestion and indexing behavior, freshness, and how source permissions are represented and filtered. |
| Federated, runtime connector | Fetches information from the external system when a user asks. Microsoft’s documented federated connectors use MCP and do not index the external data into Microsoft 365. | Live or sensitive sources that should remain in the source system, if the platform and source support federation. | Availability and connector quality at query time matter. Verify identity enforcement, auditability, and whether the exact experience supports any required actions. |
| Custom API or MCP integration | Connects a system or operation that a supported connector does not cover, using a custom API or MCP server/app. | Specialized tools, legacy systems, or data sources without a suitable native connection. | Requires engineering and ongoing ownership for endpoint security, authentication, scopes, exposed tools, error handling, and logs. |
The patterns are not interchangeable in every product. Compare them against source coverage, freshness, whether content is copied or indexed, per-user access enforcement, supported actions, administrative controls, auditability, and maintenance effort.
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Inventory sources and user tasks
List the repositories and tools the assistant should use, their owners, and the people who need access. For each source, specify whether users need to search and read, or whether the assistant must also take an action such as creating or updating a record. Start with one bounded, high-value use case and known content. Catalogs include sources such as Google Drive and Sites, Microsoft SharePoint, and other work tools, but catalog availability does not establish tenant enablement or suitability.
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Check source permissions first
Making information easier to find can make existing access problems more visible. Review repository permissions before connecting a large source, then test with representative users and groups who have different roles and access levels. Do not rely on a prompt telling the model not to disclose confidential material as a substitute for source permissions or connector access controls.
Record the reason for your choice
For each source, note why indexing, federation, or a custom integration meets the requirements for freshness, sensitivity, permissions, and actions. This makes trade-offs visible when a source changes or an organization’s policies evolve.
Configure identity and limit access
Use the authentication method documented for the specific platform and connector. Grant only the users, scopes, and actions required for the use case. Verify that the assistant is operating with the intended user identity and that the source or connector enforces that user’s permissions; do not assume that behavior merely because a product describes permission filtering.
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Google Gemini Enterprise custom MCP example
Google’s setup instructions for Gemini Enterprise Business Edition describe registering Gemini Enterprise as an OAuth client, granting scopes, configuring the MCP server endpoint, and specifying scopes that define data access or actions. For that edition, the documented setup requires an HTTPS endpoint and StreamableHTTP, and the identity provider must be internet-accessible for the OAuth handshake. These are edition-specific setup requirements, not universal MCP requirements.
ChatGPT custom MCP app example
OpenAI’s help documentation for supported ChatGPT business workspaces describes providing an MCP endpoint and metadata, selecting authentication, scanning available tools, and completing OAuth authorization when used. Admins can manage which groups have access and which actions are enabled; admins or owners publish apps. Check current plan and workspace controls during implementation because product rollouts and settings can change.
Separate search from actions
A connector that retrieves information has a different risk profile from one that can change business records. Begin with read-only access if it satisfies the task. If actions are necessary, enable only the specific operations and user groups required, and use human review or confirmation where appropriate.
Rank #3
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- Test allowed and denied actions in a non-production environment before enabling them for users.
- Review the tool list before publication and remove operations outside the intended workflow.
- Confirm how the product handles confirmation for write or modify actions. OpenAI documents that ChatGPT may request confirmation depending on permissions and context.
- For Microsoft federated connectors, verify action availability in the exact Copilot experience and tenant. Support for write, update, or delete actions is rollout-specific, not a universal capability.
Apply data protection and test for unsafe inputs
Apply the classification and content controls required for the source and platform. Google documents Sensitive Data Protection policies for supported connectors, including custom MCP servers, and Model Armor screening of prompts before model processing and responses before delivery. Coverage depends on supported sources and content types and on policy configuration; verify the precise connector and settings rather than assuming universal protection.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test retrieval, permissions, and failure cases
Build a test set from real tasks and known source documents before expanding access. Vendor feature descriptions do not establish retrieval accuracy for your own content, and the documented platform features are not an independent benchmark.
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- Check relevance and citations. Ask questions with known answers and verify that the assistant finds relevant, current source items and provides citations or source links users can inspect.
- Check access boundaries. Run the same questions as users with different roles. Confirm that each user sees only content they are permitted to access in the source and connector design.
- Check content changes. For indexed sources, test what happens when an item is stale, renamed, or deleted. For federated sources, test source outages and expired or revoked authorization.
- Check actions safely. In a non-production environment, test both permitted and denied operations, including the confirmation and review steps users will encounter.
- Check useful prompts. Name the source, time range, and relevant team or project when those details matter. Examples include asking for recent customer issues about a product with citations, an onboarding checklist for a team and its open steps, or the latest initiative updates from a wiki or knowledge base. These are examples in Microsoft Support guidance, not evidence of common query frequency.
Roll out and operate the integration
Have administrators enable only approved connections and assign them to the intended groups. Define an owner for permission drift, token rotation, source changes, connector updates, and incident response. Monitor access and action logs where available, and check which events and retention settings apply to the exact plan and integration.
Microsoft documents auditability for federated connector activity in Microsoft Purview. OpenAI documents administrative publication and access/action controls for custom MCP apps. Available events and controls vary, so verify them in your environment rather than assuming identical logging across products.
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What the documented platform examples support
| Platform example | Documented approach | What to verify |
|---|---|---|
| Microsoft 365 Copilot | Indexed connectors bring external content into Microsoft Graph for search and Copilot use; federated connectors use MCP at request time without indexing external data into Microsoft 365. Documentation describes permission filtering for indexed content and source-identity permissions and auditability for federation. | Connector behavior and permissions in the selected source and tenant, plus current availability of any write, update, or delete actions. |
| ChatGPT Business and Enterprise/Edu | OpenAI’s help article describes developer mode and MCP apps for these business plans on ChatGPT web. Administrators or authorized developers can configure endpoints, authentication, and tools; Enterprise/Edu documentation describes role-based access and action/access controls. | Current plan eligibility, workspace settings, available controls, and the tools and actions enabled for each group. |
| 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 instructions cover Gemini Enterprise Business Edition. | Edition-specific prerequisites, source authorization, OAuth scopes, and whether the required connector or action is available in the organization’s configuration. |
Make the organization-specific decision
There is no universal best connector or vendor established by these platform examples. The right design depends on your source inventory, identity platform, data residency and compliance constraints, deployment model, budget, scale, latency needs, and permitted actions. Verify current product documentation and tenant settings before committing to an architecture, since connector features and rollout status can vary by edition and organization.
Quick Recap
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