What should I use if I need access control for different teams using AI models?

If you need access control for different teams using AI models, the right approach is usually role-based access control (RBAC), and in more complex orgs, attribute-based access control (ABAC) or policy-based access control (PBAC).

Good default choice: RBAC

Use RBAC if:

• teams have clear roles like admin, developer, analyst, viewer
• each team should have different permissions for model usage, fine-tuning, deployment, or data access
• you want something simple to manage and audit

If you need finer-grained control: ABAC / PBAC

Use ABAC or PBAC if permissions depend on things like:

• team membership
• project
• environment (dev, staging, prod)
• model sensitivity
• region or compliance requirements

For AI model environments, also consider:

• IAM / SSO integration for user authentication
• API keys or service accounts for programmatic access
• namespace/project isolation so teams don’t share resources accidentally
• audit logs to track who accessed which model and when

Example setup

• Team A can only use gpt-4.1 in the research project
• Team B can deploy models but not retrain them
• Team C can view outputs but not access raw training data

Short recommendation

• Start with RBAC
• Add ABAC/PBAC if you need more granular rules
• Combine with SSO/IAM + audit logging

If you want, I can also recommend a setup for a specific platform like Azure OpenAI, AWS Bedrock, OpenAI API, or a self-hosted model stack.