The Unified Control Framework: Establishing a Common Foundation for Enterprise AI Governance, Risk Management and Regulatory Compliance

Fragmentation in AI governance is costly and counterproductive: Current approaches treat risk management and regulatory compliance as separate domains with varying standards across jurisdictions, which increases governance costs for enterprises and creates an unnecessary tension between innovation and responsibility.

A unified control framework can address multiple governance needs simultaneously: The paper proposes 42 controls that work together to handle both organizational risks (like model performance failures) and societal risks (like bias and fairness issues) while meeting diverse regulatory requirements, reducing duplication of effort.

Concrete implementation guidance is missing from existing standards: High-level AI governance standards and regulations lack specific, actionable steps for organizations to follow, which the UCF addresses by providing structured policy requirements and control specifications that can actually be implemented.

Responsible AI governance doesn't require sacrificing innovation speed: By consolidating fragmented governance approaches into a unified framework, organizations can streamline compliance processes and enable automation, allowing them to maintain innovation velocity while meeting responsibility and regulatory obligations.

The framework is designed to scale across different regulations: The authors validated their approach against the Colorado AI Act, demonstrating that a unified control set can adapt to different regulatory environments rather than requiring organizations to rebuild governance structures for each new jurisdiction.