Common guideposts to promote interoperability in AI risk management

The OECD published a policy paper (OECD Digital Economy Papers) that provides a comparative analysis of the commonalities across major AI risk management frameworks. As the report's own abstract states, it 'provides an analysis of the commonalities of AI risk management frameworks' and 'demonstrates that, while some elements may sometimes differ, all the [frameworks share a core]' — cutting through divergent national and sectoral approaches to identify the shared guidance that underpins them. The paper then distils common 'guideposts' that, per the title, are intended to 'promote interoperability in AI risk management' — offering regulators and developers a basis for aligning and harmonising overlapping frameworks. It is grounded in the OECD AI Principles and is a Tier 1 reference point in the OECD's ongoing interoperability workstream connecting national risk-management standards and the EU AI Act.

For an executive or policy lead whose AI governance stack has to satisfy multiple frameworks (NIST AI RMF, ISO/IEC, EU AI Act, sectoral rules), this is an authoritative OECD synthesis of where those frameworks actually agree — useful for aligning internal AI risk taxonomy and reducing duplication in assurance evidence.

Have the AI governance lead map the OECD common guideposts against your current AI risk-management framework and regulatory evidence pack, and flag any gap where your controls do not yet align to the identified cross-framework commonalities.