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Advanced AI is governed through a portfolio of approaches, not one global rulebook. Binding laws, voluntary risk frameworks, standards, organizational oversight, international cooperation and developers’ own frontier-risk controls operate at different levels and can be layered together.
The main approaches at a glance
| Approach | Legal or institutional force | Primary focus | Typical governance tools |
|---|---|---|---|
| Risk-based law | Binding within its jurisdiction | Regulated organizations, models or uses defined by law | Legal obligations, restrictions and oversight |
| Voluntary principles and risk frameworks | Generally voluntary | Organizations managing AI across its lifecycle | Risk identification, evaluation and mitigation |
| Technical standards and management systems | Usually voluntary; legal effect depends on context | Repeatable organizational processes | Documented controls and management procedures |
| Organizational and sector oversight | Varies by institution and applicable law | Specific agencies, industries and deployments | Accountability, review, monitoring and escalation |
| International coordination | Shared norms and cooperation; no single global AI regulator | Cross-border policy and interoperability | Principles, agreements and joint standards work |
| Developer frontier-risk commitments | Company-level commitments, not public law | Severe risks associated with advanced models | Assessments, security measures and incident response |
These approaches are not mutually exclusive. A law can establish obligations, a standard can help an organization implement processes, and internal oversight can assign responsibility for applying them.
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Binding risk-based laws
How this approach works
A risk-based law sets enforceable requirements and distinguishes among systems, uses or levels of risk. Its reach depends on the law’s jurisdiction and definitions: an obligation that applies to one category of model or use should not be assumed to cover every AI system in the same way.
The EU AI Act as an example
The EU AI Act, Regulation (EU) 2024/1689, is a regional example of binding risk-based regulation. The European Commission says governance rules and obligations for general-purpose AI models became applicable on 2 August 2025. That date concerns those rules and obligations; it is not a claim that every provision of the Act began applying on that date. For a specific compliance decision, consult the Commission’s current implementation information and the law itself, since timetables and guidance can change.
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The AI Pact is separate from the Act. The Commission describes it as a voluntary initiative intended to support transition and implementation, not as a substitute for binding legal obligations.
Voluntary principles and risk-management frameworks
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 meant “to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.” In practice, a framework of this kind gives an organization a structured way to consider risks throughout an AI system’s lifecycle; it does not itself create a law or enforcement regime.
NIST released a Generative AI Profile on 26 July 2024 to address generative-AI-specific considerations within its risk-management work. The framework may inform internal practice, but whether an organization must follow particular controls depends on its own obligations and choices.
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OECD AI Principles
The OECD AI Principles are intergovernmental policy principles, not a standalone enforcement system. Their approach emphasizes ongoing lifecycle risk management, responsibility that takes context into account, cooperation among actors and governance that can work across jurisdictions. The principles were updated in May 2024.
The OECD reported that more than 1,000 relevant policy initiatives had been recorded in over 70 jurisdictions as of May 2023 in the OECD.AI database. This is a historical count of initiatives associated with the principles, not a measure of their effectiveness or a current total.
Technical standards and management systems
Standards translate broad governance goals into repeatable processes and documentation. ISO/IEC 42001 is an AI management-system standard identified in an OECD 2025 report as being used in public- and private-sector organizations. A management-system standard can help an organization establish and maintain governance processes; adopting one does not, by itself, establish that every AI risk is controlled or that a system is legally compliant.
The legal role of a standard depends on the standard, its status and the relevant jurisdiction. The European Commission says standards are generally voluntary, while harmonised standards cited in the Official Journal can provide legal certainty for compliance with the AI Act. That distinction matters: it would be inaccurate to treat every AI standard as a legal requirement or to assume that following any standard automatically satisfies every applicable law.
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Governance also happens inside the organizations that build, buy or deploy AI. Governments and organizations can assign named accountable roles, require risk assessments, conduct audits, record formal decisions, monitor systems after deployment and define escalation routes when problems arise. Sector regulators and existing legal duties may shape those controls even where there is no AI-specific rule for a particular deployment.
An OECD 2026 analysis of public-sector AI describes governments as combining 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. This is especially important where an AI-assisted decision affects people’s rights or access to public services: responsibility should remain traceable to the institution and decision-makers using the system.
AI use can also engage laws that were not written specifically for AI, including rules on privacy, consumer protection, human rights and competition. The relevant obligations depend on the use case and jurisdiction, so an organization should assess the deployment rather than rely on a general label such as “AI-compliant.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.International coordination and shared norms
International governance includes common principles, treaty work, standards development and cooperation among governments and institutions. The practical aim is to reduce conflicting approaches and make oversight more interoperable when AI systems, developers and impacts cross borders.
In September 2024, the UN High-level Advisory Body’s final report, Governing AI for Humanity, urged the creation of foundations for an inclusive, distributed global governance architecture based on international cooperation. It is a proposal and agenda for future coordination, not evidence that a single global AI regulator with enforcement authority already exists.
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Company-level controls for frontier risks
Developers may publish specific policies for assessing and mitigating severe risks from advanced models. OpenAI’s Frontier Governance Framework, for example, describes risk assessment and mitigation, model reporting, security management, incident response, external expert input and updates. Such commitments can address developer-specific risks and controls that may not be detailed in general principles.
A company framework remains distinct from public law. Its existence alone does not show that its measures are independently verified, that all risks are controlled or that other developers follow the same approach.
How to compare governance approaches
No approach can be ranked as universally best on the available evidence. To assess whether a combination is appropriate, ask:
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches- What is binding? Separate statutes and regulations from voluntary frameworks, standards and company commitments.
- Who and what are covered? Check whether requirements concern developers, deployers, public bodies, a particular use, general-purpose models or frontier systems.
- Which risks are addressed? Consider operational failures, rights and discrimination, misuse, cybersecurity and severe frontier risks rather than treating “AI risk” as one category.
- How are controls put into practice? Look for suitable assessments, testing, documentation, management processes, audits, reporting or restrictions.
- Who is accountable? Identify who checks compliance, who makes deployment decisions and what happens after a breach or incident.
- Can approaches adapt and interoperate? Consider whether processes can respond to technical change and whether requirements can work across jurisdictions.
The central distinction is between layers of governance: laws define binding duties within their reach; frameworks and standards can help organizations structure implementation; institutional oversight assigns responsibility in real deployments; and international or company-level initiatives address coordination and risks that may not be covered by one law alone.
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