A five-layer framework for AI governance: integrating regulation, standards, and certification

Governance requires a structured bridge between laws and practice: The paper identifies a critical gap where AI regulations exist but lack clear pathways for implementation. The five-layer framework connects high-level regulatory principles to specific standards, assessment methods, and certification processes—addressing the compliance confusion that currently exists.

One framework can serve multiple governance contexts: Through case studies on AI fairness and incident reporting, the research demonstrates that the framework adapts to both global standards and region-specific needs, suggesting that organizations don't need entirely separate compliance systems for different jurisdictions.

Clear implementation roadmaps improve compliance and risk management: By providing a structured pathway from regulations to practical applications, the framework equips policymakers, regulators, and industry stakeholders with actionable guidance—moving AI governance beyond abstract principles to concrete, enforceable requirements.

Certification and standards are essential missing links: The framework highlights that regulations alone are insufficient; the progression through standardization, assessment methodologies, and formal certification processes creates accountability mechanisms that help close enforcement gaps currently plaguing AI governance.

Structured governance builds public trust: By establishing clear, transparent pathways for ethical AI implementation and compliance verification, the framework supports policies designed to build public confidence in AI systems and promote their responsible use across society.