ChatGPT Enterprise Role-Based Access: Who Can Do What?

ChatGPT Enterprise provides workspace-level access controls: administrators can manage who belongs to the workspace, which features are available to users, and which connected integrations are permitted. Mapping those controls to your team's actual responsibilities is the governance work that makes them effective.

Access decisions are only useful if they are documented. A tool like fenbs gives your team a place to record which access was granted, to whom, and why, so the reasoning is available when an auditor asks or when a setting needs to change.

What roles ChatGPT Enterprise supports

ChatGPT Enterprise workspaces have administrative roles that control who can manage settings, add users, and configure integrations. Users in the workspace can access the features that the administrator has enabled. For the specific roles and permissions available in your plan, check the OpenAI help center↗ (opens in a new tab), as these are updated as the product evolves.

The distinction that matters in practice is between administrators and regular users. Administrators can change settings that affect the whole workspace. Regular users interact with ChatGPT within the constraints the administrator has set. If too many people have administrative access, the workspace becomes harder to govern because any of them can change a setting without coordinating.

Map access to responsibilities

The useful question is not just who has access but what they are supposed to do with it. Match each access level to the responsibilities it supports. An administrator who manages user accounts is using admin access for a clear purpose. An administrator who was given that role during setup and has never used it is an unnecessary risk.

For regular users, check whether the features available to them match their work. If certain integrations are enabled for the whole workspace but only one team uses them, consider whether they should be restricted to that team or whether the whole organization needs to understand what those integrations can access.

Review the access list periodically. People change roles and leave organizations. An access list that has not been reviewed in six months is likely to include people who no longer need what they have.

Connected tools and access boundaries

Access controls in ChatGPT Enterprise extend beyond the workspace to the integrations it connects with. When the workspace is connected to documents, email, or project tools, the AI can access those data sources during a session. The people who can enable integrations are usually administrators, so that capability is itself an access control decision.

For each connected integration, document who approved it, what data it can access, and whether that access is appropriate for all workspace users or only some. A document connector that gives ChatGPT access to your organization's entire shared drive is a broad access grant that deserves deliberate consideration.

The AI governance topic hub at /blog/topic/ai-governance collects related guides on access, data handling, and decision records for AI tools.

Record access decisions, not just settings

Settings in an admin console change without leaving a record in the platform. Documenting access decisions separately ensures you have a history: who had access, what they could do, and why it was approved.

This matters during off-boarding. When someone leaves the organization, you need to know what access to revoke. A list of active users in the admin console tells you who has access now; a decision record tells you what was approved and for what reason, which helps if questions arise after they have left.

It also matters during security reviews. An auditor who asks "who had access to the ChatGPT workspace and what were they permitted to do" needs a documented answer, not a current snapshot of settings that may have changed.

Review access decisions at a regular interval, not just when something goes wrong. Quarterly is a practical starting point. Each review should confirm that the people with elevated access still need it, that connected integrations are still approved, and that the documented decisions still match the actual settings. Any gaps found during review should be closed and the record updated.

When the review is complete, note the date, who conducted it, and whether any changes were made. That record is itself a form of governance evidence. An organization that reviews access on a consistent schedule is in a significantly stronger position than one that relies on the assumption that nothing has drifted since the initial setup.

How to do this with fenbs

1. Create a free account. Start a project for AI governance and access records.
2. Add a Decision for each significant access grant: who, what access, approved by whom, and when.
3. Add a task for the next quarterly access review, with a named reviewer.
4. Connect your AI apps over MCP from Integrations. They read the Decisions, so the access rules are visible during AI-assisted work.
5. After each review, update the access Decisions and note any changes in the History.

A personal board is free and a reasonable place to track your own access rules. A team board gives each member a role and keeps the History of every change, with the person who made it.