How do I control which teams can access which AI models?

You generally control access to AI models with a combination of identity, grouping, and policy enforcement. The exact steps depend on the platform, but the pattern is usually:

1) Put users into teams/groups

Create teams such as:

• Engineering
• Support
• Sales
• Research

2) Assign model permissions by team

Define which teams can use which models, for example:

• Engineering → GPT-4, code models
• Support → smaller/cheaper customer-service models
• Research → experimental models
• Sales → approved enterprise models only

3) Enforce access at the model gateway or application layer

Common ways:

• Role-based access control (RBAC): map team roles to allowed models
• Policy-based access control (PBAC): rules based on team, environment, region, data sensitivity, etc.
• API gateway / proxy: all model requests go through a central service that checks permissions before forwarding
• Tenant/workspace isolation: each team only sees the models enabled in their workspace

4) Use environment-specific restrictions

You may want different access in:

• dev vs prod
• internal vs external users
• regulated regions vs non-regulated regions

5) Audit and monitor usage

Log:

• who accessed which model
• when
• from which app/workspace
• token usage/costs

This helps with compliance and cost control.

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Example policy

• Team A: can access gpt-4.1 and gpt-4.1-mini
• Team B: can access gpt-4.1-mini only
• Team C: no access to external models, only internal approved models

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If you tell me which platform you’re using (OpenAI API, Azure OpenAI, Bedrock, Vertex AI, internal model gateway, etc.), I can give you the exact configuration steps.