FINOS AIGF Principles Catalog
This page renders the FINOS AI Governance Framework's principles as a
Gemara PrincipleCatalog. These principles express
the core values the framework's mitigations are designed to uphold.
For the linked mitigation and risk material, see the Guidance Catalog and Vectors Catalog.
Data Protection
Principles governing the handling, classification, and minimization of sensitive data within AI systems.
AIR-PRIN-001Proactive Data Sanitization
Apply filtering and anonymization techniques before data enters the AI processing pipeline, vector databases, or any external service endpoints.
AIR-PRIN-002Data Classification Awareness
Understand and respect the sensitivity levels and access controls associated with source data when determining appropriate filtering strategies.
AIR-PRIN-003Principle of Least Exposure
Only include data in AI systems that is necessary for the intended business function, and ensure that even this data is appropriately de-identified or masked when possible.
Security Architecture
Principles addressing layered defenses and resilience in AI system design.
AIR-PRIN-004Defense in Depth
Implement multiple layers of filtering at data ingestion, during processing, and at output generation to create robust protection against data leakage.
Governance
Principles ensuring transparency, accountability, and auditability of AI data processing activities.
AIR-PRIN-005Auditability and Transparency
Maintain clear documentation and audit trails of what data filtering processes have been applied and why.