Modular Pretraining Enables Access Control

This paper introduces a modular pretraining approach that enables fine-grained access control over AI model capabilities, allowing organizations to selectively activate or restrict specific model functionalities post-deployment. The technique addresses enterprise security and governance requirements by decoupling model components during training, enabling permission-based access to different model behaviors without retraining.

This paper introduces a modular pretraining approach that enables fine-grained access control over AI model capabilities, allowing organizations to selectively activate or restrict specific model functionalities post-deployment. The technique addresses enterprise security and governance requirements by decoupling model components during training, enabling permission-based access to different model behaviors without retraining. This approach is particularly relevant for regulated industries requiring granular control over AI system outputs and compliance with data protection requirements.

Legal AI must preserve privilege, satisfy ethics rules, and keep client data within a defensible perimeter. QuettaMinds helps law firms and legal departments deploy AI that is structurally compliant, not just policy-compliant.