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.