Designing a user rights management system for an AI application building platform involves defining roles, permissions, and access controls to ensure secure and efficient collaboration. Here’s a structured approach:
1. Role-Based Access Control (RBAC)
- Define Roles: Identify key user types (e.g., Admin, Developer, Data Scientist, Viewer).
- Admin: Full access to manage users, resources, and billing.
- Developer: Can create/edit AI models and deploy applications.
- Data Scientist: Access to datasets, model training, but not infrastructure settings.
- Viewer: Read-only access to projects.
- Assign Permissions: Map actions (e.g., "deploy model," "delete dataset") to roles.
2. Permission Granularity
- Resource-Level Control: Restrict access to specific projects, datasets, or models.
- Example: A Data Scientist can only modify models in their assigned project.
- Action-Level Control: Define allowed operations (e.g., read, write, delete).
3. Authentication & Authorization
- Use OAuth 2.0 or SAML for identity verification.
- Integrate with LDAP/Active Directory for enterprise users.
4. Audit & Compliance
- Log user actions (e.g., "User X deleted dataset Y") for security reviews.
- Enforce data retention policies.
5. Dynamic Access Management
- Allow temporary access (e.g., for external collaborators) with expiry.
- Enable self-service role requests with approval workflows.
6. Example Workflow
- A Developer logs in, creates a new AI model, and deploys it to a test environment. A Viewer can see the deployment status but cannot modify the model.
Recommended Tencent Cloud Services
- CAM (Cloud Access Management): Implement RBAC and fine-grained permissions.
- Tencent Cloud IAM: Manage identities and access across services.
- Cloud Audit (CloudAudit): Track user activities for compliance.
This design ensures scalability, security, and usability for AI platform users.