Dernière mise à jour :
A structured set of guidelines and best practices that organizations follow to ensure their AI systems meet regulatory and ethical standards.
A formalized, often modular, blueprint that maps laws, industry codes, and ethics principles to concrete controls, roles, checkpoints, and documentation templates. It serves as the foundation for policies, training programs, risk assessments, and audits, ensuring consistent application of compliance requirements across all AI initiatives.
Exemple concret :
A multinational corporation adopts a three-pillar compliance framework: (1) Data Privacy (GDPR, CCPA controls), (2) Fairness (bias assessments, remediation), and (3) Transparency (explainability standards). Each new AI project must complete a “compliance checklist” aligned to the framework before kickoff.
