AI Model Governance

Develop a governance framework for AI models used in public and commercial contexts that emphasizes risk assessment, safety controls, transparency, and accountability. Include model lifecycle phases (design, training, deployment, monitoring), testing protocols (red-teaming, adversarial testing, data provenance), governance layers (ethics reviews, external audits, regulatory compliance), incident response and remediation, and international cooperation. Compare approaches used in multiple jurisdictions and propose a practical, scalable plan that reduces misuse while preserving innovation.