There is no single global rulebook for governing advanced AI. The main approaches are binding risk-based laws, voluntary risk-management frameworks, technical standards, organizational and sector oversight, international cooperation, and company-level controls for frontier-model risks. They do different jobs: laws can set obligations, standards and frameworks can help put them into practice, and organizations can apply oversight to specific systems and uses.
How the main approaches differ
Governance approaches vary in legal force, who they cover, which risks they address, and how compliance or performance is checked. A statute or regulation can impose legal duties; a voluntary framework offers a method without itself creating a legal obligation; a standard provides repeatable processes; and an organization’s or developer’s own policy sets controls for its operations. International initiatives seek shared norms and cooperation, but do not necessarily create an enforcement authority.
| Approach | Legal force | Typical focus | How it operates |
|---|---|---|---|
| Risk-based law | Binding within its jurisdiction and scope | Covered providers, deployers, models, or uses | Legal obligations organized around risk |
| Voluntary principles and frameworks | Generally voluntary as frameworks | Organizations managing AI across its lifecycle | Guidance for assessing and managing risk |
| Technical standards and management systems | Usually voluntary; legal effect depends on context | Repeatable organizational processes | Documented management and technical practices |
| Organizational and sector oversight | Depends on applicable laws and institutional rules | Specific agencies, sectors, and deployments | Roles, assessments, audits, decisions, and monitoring |
| International coordination | Varies by instrument; shared principles alone are not a global regulator | Cross-border alignment and cooperation | Principles, cooperation, treaties, and standards work |
| Developer frontier-risk commitments | Company commitments, not public law | Potentially severe risks from advanced models | Company-defined evaluations and mitigations |
1. Binding risk-based laws
A binding law establishes duties for the people, organizations, systems, or uses within its jurisdiction and scope. Risk-based regulation differentiates requirements according to the risks associated with AI rather than treating every system identically. The precise obligations depend on the law and the case; a framework from another jurisdiction cannot substitute for checking the applicable rules.
The EU AI Act
The EU AI Act, Regulation (EU) 2024/1689, is a prominent regional example of a binding legal framework organized around risk. The European Commission states that governance rules and obligations for general-purpose AI models became applicable on 2 August 2025. The AI Pact is a separate voluntary initiative intended to support transition and implementation; it is not the Act itself. Implementation guidance and timetables can change, so organizations assessing a specific duty should consult the Commission’s current AI Act materials and relevant legal advice.
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2. Voluntary principles and risk-management frameworks
These approaches give organizations a structured way to identify, assess, and address risks without themselves functioning as a standalone enforcement regime. They can help make governance part of design, development, use, and evaluation, and may complement binding requirements.
NIST AI Risk Management Framework
The U.S. National Institute of Standards and Technology describes its AI Risk Management Framework (AI RMF) as intended for voluntary use. NIST says it is designed “to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.” NIST released a Generative AI Profile on 26 July 2024 to address risks associated with generative AI. The framework can guide risk-management work, but using it does not by itself establish compliance with every law that may apply.
OECD AI Principles
The OECD AI Principles are intergovernmental policy principles, not a standalone enforcement regime. They emphasize ongoing risk management across the AI lifecycle, responsibility that reflects context, cooperation among actors, and governance approaches that can work across jurisdictions. The principles were updated in May 2024.
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The OECD reports that, as of May 2023, governments had recorded more than 1,000 relevant policy initiatives in over 70 jurisdictions in the OECD.AI national policy database. That is a historical count of initiatives associated with the principles—not a measure of their implementation, effectiveness, or the current total.
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Standards and management systems translate governance aims into repeatable processes: for example, documented responsibilities, risk procedures, and organizational controls. The OECD’s 2025 report identifies ISO/IEC 42001 as an AI management-system standard used in public- and private-sector organizations.
A standard is not automatically a legal requirement. The European Commission says standards are generally voluntary, while harmonised standards cited in the Official Journal can provide legal certainty for demonstrating compliance with the AI Act. Whether a particular standard has that role depends on its status, the jurisdiction, and the law and requirement at issue. Organizations should not assume that adopting any AI standard automatically satisfies their legal obligations.
4. Organizational and sector oversight
Public agencies and private organizations turn broad rules or principles into decisions and controls for their own systems. Practical governance may include named accountable roles, risk assessments, audits, formal approval paths, ongoing monitoring, and escalation procedures. The relevant combination depends on the organization, the use, and the applicable rules.
Why sector and use matter
The same model can present different governance questions in different settings. A public-sector decision affecting people may require a clear route for review and accountability; a different deployment may call for other controls. The OECD’s 2026 analysis of public-sector AI says governments combine binding requirements with softer tools such as guidelines, standards, and ethical principles. It cautions that higher-risk government uses need risk assessments, audit structures, accountability frameworks, and formal decision paths.
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AI-specific rules are not the only relevant rules. Existing laws on privacy, consumer protection, human rights, and competition may also apply to an AI system’s use. Governance therefore involves both the system and the decision or service in which it is used.
5. International coordination and shared norms
AI systems and their impacts can cross borders, so governments and institutions pursue shared principles, cooperation, treaties, and standards work. The OECD principles call for interoperable policy environments and cooperation. Interoperability can make approaches more compatible, but shared language does not mean that countries have identical laws or enforcement.
The UN High-level Advisory Body’s final report, Governing AI for Humanity, released in September 2024, urged the foundations of an inclusive, distributed global governance architecture based on international cooperation. This is a proposed agenda, not an operating global AI regulator with universal authority.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Company-level controls for frontier-model risks
Developers may establish additional policies for evaluating and mitigating severe risks associated with advanced models. OpenAI’s Frontier Governance Framework describes practices including risk assessment and mitigation, model reporting, security management, incident response, external expert input, and framework updates. Such a framework illustrates developer-level governance: it is distinct from public law and is not, by itself, an independently verified guarantee that a model is safe.
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How to assess a governance approach
When comparing policies or deciding what an organization needs, assess the approach against the system, use, and jurisdiction rather than looking for a single universal winner. Useful questions include:
- Legal force: Is the measure a binding law, a voluntary framework, a standard, or a company commitment?
- Coverage: Does it apply to developers, deployers, public agencies, certain uses, general-purpose models, or frontier systems?
- Risk scope: Does it address operational risks, rights and discrimination, misuse, cybersecurity, or severe frontier risks?
- Implementation: Does it call for documentation, assessment, testing, management systems, audits, reporting, or restrictions?
- Accountability: Who reviews performance or compliance, and what processes address a breach or incident?
- Adaptability and interoperability: Can the approach respond to technical change, and how well does it align with requirements elsewhere?
These are comparison dimensions, not a proven scorecard: the available sources do not establish a universal best model or a common empirical ranking of outcomes.
Why governance is usually layered
Different approaches address different gaps. A binding law can establish obligations; a relevant standard or risk framework can help an organization build repeatable processes; sector oversight can assign responsibility for a particular deployment; and a developer’s own controls can address model-specific risks. International coordination can support shared norms across borders. None of these layers automatically replaces the others, and the applicable legal duties remain specific to jurisdiction and scope.
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