AI Governance Framework Principles
Core principles underpinning the FINOS AI Governance Framework. Each principle represents a foundational value that one or more AIGF mitigations (guidelines) are designed to uphold.
- ID
- AIR-PRIN
- Version
- 0.2.0
- Gemara version
- 1.1.0
- Author
- FINOS-AIGF
Data Protection
Principles governing the handling, classification, and minimization of sensitive data within AI systems.
AIR-PRIN-001 Proactive Data Sanitization
Apply filtering and anonymization techniques before data enters the AI processing pipeline, vector databases, or any external service endpoints.
AIR-PRIN-002 Data Classification Awareness
Understand and respect the sensitivity levels and access controls associated with source data when determining appropriate filtering strategies.
AIR-PRIN-003 Principle 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-004 Defense 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-005 Auditability and Transparency
Maintain clear documentation and audit trails of what data filtering processes have been applied and why.