Purpose and Use Cases

An explanation of why connecting AI risks to governance controls is necessary for translating ethical principles and regulatory requirements into practice. The crosswalk can be used as a baseline diagnostic to identify risk coverage gaps, an implementation guide for translating risk assessments into policies and processes, a training resource to educate stakeholders, and a foundation for developing internal governance checklists or maturity models.

Key Insights and Themes

A discussion of patterns observed in the AIRGC. For instance, some risk areas (like privacy, security, and bias) are well-covered by existing controls, whereas others (such as AI “race” dynamics, environmental impacts, or future advanced AI risks) have less established governance measures. This section highlights where governance frameworks are robust and where new controls or emphasis might be needed.

Gaps and Limitations

A brief and honest overview of the AIRGC’s limits. The analysis is a high-level alignment and does not provide detailed implementation instructions, sector-specific guidance, or proof of real-world effectiveness for each control. It also notes that both the risk landscape and governance best practices.

Overview of Source Frameworks

We share an accessible overview of MIT’s AI Risk Repository (which categorizes AI-related harms into 7 domains and 23 subdomains ) and Kavanagh’s AI Governance MegaMap (which maps 6 major (AI) governance standards and laws into 12 unified control domains ). We will emphasize the academic rigor and breadth of these sources, underscoring their value as comprehensive references.