Connect ChatGPT to only the tools, accounts, data, and actions a specific workflow needs. For many teams, a supported connected app is the simplest route; use a custom MCP app or an API integration when the workflow requires capabilities or controls the supported app does not provide. Before enabling any route, check workspace and plan availability, provider permissions, who can use the integration, what it can do, and where its data goes.
Choose the integration route that fits the workflow
ChatGPT connected apps, custom apps built with the Model Context Protocol (MCP), and API-based integrations are different ways to connect software and data. They are not interchangeable: availability, setup, authorization, governance, and data handling vary. Decide first what information ChatGPT must access and whether it needs to take actions or only retrieve information.
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| Route | Best fit | What to check |
|---|---|---|
| Supported connected app | A provider integration available in ChatGPT that covers the workflow. | Whether it is available for your plan, region, workspace, and ChatGPT surface; which account is connected and what that account can access. See OpenAI’s account connection and management guidance. |
| Custom MCP app | A custom or third-party tool connection when a supported app does not meet the need. | Whether developer mode and the required MCP capabilities are enabled in your workspace; who can access the app; which tools it exposes; and whether actions can modify data. OpenAI says availability and publishing rules differ between Business and Enterprise/Edu, and describes full MCP support and developer mode as rolling out in beta. Check the current workspace before planning around them. See OpenAI’s MCP app guidance. |
| API-based integration | A separately built integration in which your application uses the OpenAI API to work with business systems. | Your application’s design, permissions, data flow, endpoint behavior, and applicable API retention controls. API data controls are distinct from ChatGPT workspace app settings. See the remote MCP tool configuration reference and API data controls documentation. |
No route is automatically the safest. The right choice depends on the data and actions needed, who manages the connector, how access is authorized, what control and review options exist, and the provider’s data terms.
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“The app is enabled” does not mean every user can access it, or that it can perform only the actions you intended. Treat these as distinct checks:
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- App availability: Whether the workspace has made the app available.
- User access: Which people or groups may use it, where role or group controls are offered.
- Enabled actions: Which tools or operations the app can expose, such as reading information or modifying it.
- Provider authorization: What the connected provider account is permitted to see or change.
- Approval behavior: Whether a user must confirm an action before it runs.
These controls do different jobs. A narrow list of enabled ChatGPT actions does not make a broadly authorized provider account narrow; conversely, a limited provider account does not determine which workspace users may use the app. OpenAI documents app administration and security controls in its admin controls guidance.
Roll out access in deliberate stages
- Define the workflow. List the source systems, data categories, intended users, and actions required. Decide whether a supported app can do the job or whether you need a custom MCP app or API implementation.
- Confirm availability. Ask the workspace owner or administrator to check the plan, region, workspace configuration, and ChatGPT surface. For custom MCP apps, confirm developer mode and publishing options are available for the organization; do not assume a feature shown in documentation is enabled in every workspace.
- Choose the provider account. Connect an account whose existing source-system permissions match the intended use. Read the authorization request before approving it. OpenAI explains how to connect, manage, and disconnect app accounts.
- Inspect custom connectors. Before publishing an MCP app, review its server, tool list, data destinations, provider terms, and operational behavior. Test it before granting broad access. OpenAI Help Center states: “You are responsible for verifying the MCP server and app are safe and appropriate for your organization before publishing.” Its MCP guidance also warns that untrusted MCP servers can create security risks, including prompt injection; safeguards reduce risk but do not remove it.
- Start with constrained access. Where the workspace offers the controls, grant access only to the required role or group and enable only necessary actions. A read-only pilot is a sensible starting point; review write or modify operations separately and require confirmation for consequential actions when the setting is available.
- Run a limited pilot. Use a small authorized audience and representative, non-sensitive data. Check what the app can retrieve or change, how it behaves on errors, and whether prompts or responses might reveal confidential information. Expand only after the results and provider terms are acceptable.
- Record ownership and review points. Document who owns the integration, its provider account, permitted users, enabled actions, relevant retention terms, and how to disable access or disconnect the account. Revisit the setup if permissions, tools, or terms change.
Review provider permissions and custom MCP risk
Authorization begins with the provider account. Connect an account that has only the source-system access needed for the workflow, and examine the requested scopes before granting them. A ChatGPT app’s action settings are not a substitute for checking the provider’s own permissions. If the account or integration is no longer required, remove its access or disconnect it using the account-management options.
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Custom MCP servers deserve additional scrutiny because they may expose tools that read or change information and may send data to a third party. OpenAI places responsibility on the organization to assess a custom or third-party app before publishing it. Review what each tool does, what information it receives, where that information is sent, and what happens when it is invoked. Prompt-injection risks mean instructions or content encountered through an untrusted server can influence behavior; confirmation settings and restricted access help, but are not guarantees of safety. See OpenAI’s apps and connectors security guidance.
Know where business data goes and how it is used
OpenAI says content from Business, Enterprise, and Edu workspaces, including information accessed through apps, is not used to train its models by default. That statement concerns OpenAI’s use of workspace content; it is not a promise about every connected provider. For non-synced apps, information is sent to the third party under that provider’s terms, so review its privacy, security, and retention practices. OpenAI’s workspace apps and connectors guidance also explains that data residency has limits: it does not mean every processing step, system record, or external integration remains within one region.
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API data has a separate set of controls. OpenAI’s API documentation says API data is not used for training unless a customer opts in. It describes default abuse-monitoring logs retained for up to 30 days, as well as endpoint-specific application state and eligibility or approval conditions for retention controls. These are API documentation details, not a blanket retention promise for every endpoint or integration. Remote MCP servers are third parties, and their own retention policies apply to data sent to them. Consult the API data controls documentation for the relevant endpoint and control conditions.
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Use a pre-launch safety checklist
- The workflow, data sources, users, and necessary actions are written down.
- The selected route is available for the target plan, region, workspace, and client surface.
- The connected provider account has only appropriate source-system access, and its requested authorization has been reviewed.
- For custom MCP, the organization has reviewed and tested the server, tools, data destinations, and provider terms.
- App availability, user access, enabled actions, and approval requirements have each been checked independently.
- Write or modify actions have been separately reviewed, with confirmation required where appropriate and available.
- A limited pilot has checked retrieval, changes, error behavior, and possible disclosure through prompts or outputs.
- Ownership, review dates, retention terms, and a disable/disconnect process are documented.
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