Built on a privacy-first architecture

Role-Based Access Control (RBAC) streamlines access management by assigning predefined roles such as data scientist, administrator, or analyst to users, automatically granting appropriate permissions and system access based on their organizational responsibilities. This approach simplifies security administration while ensuring users receive only the access levels necessary for their specific job functions.

Attribute-Based Access Control (ABAC) provides fine-grained control over data and model access by assigning permissions based on specific metadata attributes such as classification levels, user roles, department affiliations, or security clearances. This dynamic approach allows organizations to create sophisticated access policies that automatically adapt to changing contexts, ensuring that sensitive AI resources are only accessible to authorized personnel who meet the precise criteria defined by the system’s security framework.

By implementing Axonis’ document-level security, organizations can maintain strict control over information distribution, protect intellectual property, and ensure compliance with regulatory requirements while preventing unauthorized users from accessing entire sets of sensitive data. Document-level security allows administrators to control who can view, edit, or access specific documents within a system based on user authorization levels and organizational policies.

Axonis has the ability to apply field-level security, allowing organizations to restrict sensitive information on a granular level. This security approach enables administrators to hide, mask, or anonymize particular data fields such as social security numbers, salary information, or personal identifiers based on user roles and permissions. By implementing field-level security, organizations can ensure that users only see the data elements necessary for their job functions while maintaining compliance with privacy regulations and protecting confidential information from unauthorized access.

Homomorphic encryption in federated learning enables secure collaborative model training by allowing computations to be performed directly on encrypted data without requiring decryption. This approach addresses critical privacy concerns in federated learning by ensuring that sensitive information from individual participants remains protected throughout the training process, while still enabling the server to perform necessary mathematical operations like averaging gradients or combining model weights.