model access control
**Model access control** is the set of policies and technical mechanisms that govern **who can use, modify, download, or inspect** a machine learning model. As AI models become valuable assets and potential security risks, controlling access is essential for **security, compliance, and IP protection**.
**Access Control Dimensions**
- **Inference Access**: Who can query the model for predictions? Controlled via API keys, authentication, and authorization.
- **Weight Access**: Who can download or view model weights? Critical for proprietary models — weight access enables fine-tuning, extraction, and competitive analysis.
- **Training Access**: Who can retrain or fine-tune the model? Unauthorized fine-tuning could introduce backdoors or remove safety training.
- **Configuration Access**: Who can modify model parameters, system prompts, or deployment settings?
- **Monitoring Access**: Who can view usage logs, performance metrics, and audit trails?
**Implementation Mechanisms**
- **Authentication**: API keys, OAuth tokens, or mutual TLS to verify identity.
- **Role-Based Access Control (RBAC)**: Define roles (admin, developer, user, auditor) with specific permissions. Users → admin can modify models; developers → can deploy but not modify weights; users → inference only.
- **Attribute-Based Access Control (ABAC)**: Permissions based on user attributes, resource attributes, and environmental conditions.
- **Network Controls**: VPN requirements, IP allowlists, VPC restrictions for sensitive model endpoints.
- **Usage Quotas**: Per-user or per-role limits on request volume, token consumption, or compute usage.
**Special Considerations for LLMs**
- **Prompt Visibility**: Control who can view and modify system prompts that shape model behavior.
- **Fine-Tuning Permissions**: Restrict who can upload training data and create fine-tuned model variants.
- **Model Registry**: Track all model versions, who created them, and who has access to each version.
- **Output Controls**: Different users may have different output filters, safety levels, or feature access.
Model access control is increasingly required by **AI governance frameworks** and regulations like the **EU AI Act**, which mandates transparency and accountability for high-risk AI systems.