Global AI Governance Frameworks
Global AI governance is built on non-binding frameworks, international standards, and multi-stakeholder agreements rather than enforceable treaties. The OECD AI Principles (2019, updated 2024) form the most widely adopted international baseline, incorporated into national AI strategies across more than 40 countries. The UNESCO Recommendation on the Ethics of AI (2021) adds a normative foundation covering human rights, sustainability, and cultural diversity. The G7 Hiroshima AI Code of Conduct and Bletchley Declaration on AI Safety reflect commitments by leading economies to frontier model governance and safety evaluation.
These frameworks carry no direct legal force, but they shape national legislation, inform corporate AI ethics commitments, and provide the normative vocabulary that regulators draw on when they do legislate. The EU AI Act's risk tiers and the NIST AI RMF's governance functions both trace their intellectual lineage to the OECD Principles. For organizations operating across multiple jurisdictions, fluency in these international frameworks supports a governance posture that translates across markets.
The Financial Stability Board and FATF have published sector-specific global guidance on AI in financial services and anti-money laundering, respectively, creating softer compliance expectations for systemically important institutions. The UN's emerging work on AI governance — including the Global Digital Compact — signals growing international attention to AI's impact on development, rights, and geopolitical stability.
Key themes
- 1.OECD AI Principles as the global normative baseline
- 2.G7 Hiroshima Code of Conduct on frontier model governance
- 3.UNESCO Recommendation on AI Ethics — human rights and sustainability
- 4.Financial Stability Board and FATF sector-specific guidance
Regulatory frameworks and guidance(12)
Bletchley Declaration on AI Safety
At the inaugural AI Safety Summit, 28 governments signed this political declaration. They recognized frontier AI risks and committed to international cooperation on safety, evaluation, and information sharing.
Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law
This treaty is the first internationally legally binding instrument dedicated to AI governance, adopted under the auspices of the Council of Europe. It applies to AI systems deployed by public authorities and private actors operating within signatory states. Parties are required to protect human rights, uphold democratic principles, and ensure the rule of law throughout the AI lifecycle.
FATF AI Anti-Money Laundering Guidance
FATF guidance addresses AI and machine learning in anti-money laundering, counter-terrorism financing, and proliferation financing compliance. It sets expectations for transaction monitoring, customer due diligence, and suspicious activity detection.
Sound Practices for Responsible Adoption of Artificial Intelligence (Consultation Report)
The Financial Stability Board proposes 12 practices for responsible AI adoption throughout its lifecycle. They cover banks, insurers, and other regulated financial entities developing or deploying AI. Institutions should map them to governance, model risk, third-party oversight, and lifecycle controls.
G7 Hiroshima AI Code of Conduct
The G7 Hiroshima AI Process issued this voluntary international code of conduct. It sets eleven principles and corresponding actions for advanced AI developers and operators, particularly frontier and general-purpose models. The code uses voluntary commitments to guide responsible development.
Indonesia Presidential Regulation on the National AI Roadmap and AI Ethics
In August 2026, Indonesia outlined a planned Presidential Regulation establishing a National AI Roadmap and binding ethics framework. If finalized, it would cover enterprises operating AI in Indonesia. Expected requirements include ethics reviews, internal controls, and documented risk assessments.
ITU Focus Group on Trust and Identity for Humans and Agentic AI
ITU launched a Focus Group on trusted digital identity and accountable behavior throughout agentic AI lifecycles. It addresses agent identification, credentials, authorization, and agent-to-agent interactions. The work concerns organizations developing or deploying agents with delegated authority or external system access.
OWASP Top 10 for Large Language Model Applications
OWASP’s LLM Top 10 identifies application security risks. These include prompt injection, insecure output handling, training-data poisoning, denial of service, and supply-chain vulnerabilities. Development and security teams use it to prioritize controls.
The Role of Investors in AI Governance
Oxford Martin’s AI Governance Initiative examines investor responsibilities for AI safety and accountability. It covers financing and oversight by institutional investors, venture capital, and private equity. Investors can use it in due diligence, stewardship, and portfolio management.
Singapore Consensus on Global AI Safety Research Priorities
The Singapore Consensus sets shared international priorities for AI safety research. It emerged from a government-convened multilateral summit involving governments and organizations. The non-binding agenda guides national safety programs and research funding bodies.
Global Dialogue on AI Governance (UN General Assembly Resolution A/RES/79/325)
UN Resolution A/RES/79/325 established the Global Dialogue on AI Governance. The forum welcomes member states, civil society, private businesses, and other stakeholders. Submissions through April 30, 2026 will inform discussions of global AI challenges and priorities.
Verifiable Semiconductor Manufacturing: Governance and Verification Systems for AI Supply Chain Oversight
Oxford Martin’s AI Governance Initiative examines semiconductor governance and verification in AI supply chains. The guidance addresses large-scale hardware design, production, procurement, and deployment. It outlines assurances for provenance, integrity, and manufacturing standards.