Design & Risk Identification
AI initiatives are assessed for regulatory exposure, data sensitivity, and operational impact prior to development or deployment.

Controlled Development & Validation
Model development follows documented validation, testing, and security review procedures.

Lifecycle Monitoring
Continuous monitoring ensures model integrity, performance stability, and risk oversight throughout operation.

Risk-Based Categorization
AI use cases are categorized based on impact, criticality, and regulatory sensitivity.

Human Oversight
Defined human review mechanisms ensure accountability and ethical alignment.

Documentation & Traceability
Model documentation and traceability controls support audit readiness and transparency.

Data Protection Controls
Security and data protection safeguards are embedded within AI pipelines.

Misuse Prevention
Preventive controls mitigate risks of unauthorized or unintended system use.

Regulatory Alignment
The framework is designed to align with emerging AI regulatory standards and enterprise governance expectations.