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A landmark multilateral political declaration signed at the inaugural AI Safety Summit, in which 28 governments agreed on the existence of frontier AI risks and committed to collaborative international action on AI safety, evaluation, and information sharing.
China AI Standardization White Paper
A non-binding policy document issued by Chinese standards authorities that maps China's AI standardization landscape, identifies standardization priorities, and signals the direction of forthcoming national and international AI standards work.
Guidelines on Transparency Obligations for Providers and Deployers of Certain AI Systems
The European Commission published these guidelines to help providers and deployers of AI systems comply with the transparency obligations established under the EU AI Act, which apply from 2 August 2026. They cover labeling requirements, user notices, logging practices, technical documentation, and internal approval workflows for AI-generated or AI-assisted content. Organizations that develop or deploy AI systems in the EU must follow this guidance to meet their legal obligations under the AI Act.
EU General-Purpose AI Model Training Data Public Summary Template
The European Commission published a standardized template for providers of general-purpose AI models to use when publicly disclosing summaries of their training data. It supports compliance with the transparency obligations for general-purpose AI models established under the EU AI Act. Providers are expected to follow the template structure when meeting their disclosure requirements under that regulation.
FATF AI Anti-Money Laundering Guidance
FATF guidance on the use of AI and machine learning in anti-money laundering, counter-terrorism financing, and proliferation financing compliance, setting expectations for responsible AI adoption in transaction monitoring, customer due diligence, and suspicious activity detection.
FDA AI/ML Software as Medical Device Guidance
FDA's action plan and associated guidance documents establish a regulatory framework for AI/ML-based Software as a Medical Device (SaMD), introducing a total product lifecycle (TPLC) approach, predetermined change control plans, and transparency and monitoring requirements for adaptive AI/ML algorithms used in clinical settings.
Financial Stability Board Recommendations on Agentic AI Controls in Financial Services
The Financial Stability Board issued recommendations calling on corporate boards at financial institutions to establish safeguards for agentic AI systems that can plan, reason, and act with minimal human oversight. The guidance applies to banks, asset managers, insurers, and other regulated financial entities deploying or considering autonomous AI. It identifies board-level governance, defined usage limits, and protective controls as expected baseline practices for agentic AI deployments.
Careful Adoption of Agentic AI Services
This joint guidance from six national cybersecurity authorities across five countries identifies agentic AI systems as presenting a distinct and elevated class of operational and security risks. It applies to any organization deploying or procuring AI agents capable of autonomous decision-making and multi-step task execution. Organizations are advised to restrict agentic AI to low-risk, non-sensitive tasks until security standards and operational practices mature.
FTC AI Enforcement Policy
The FTC's AI enforcement posture, articulated through policy statements, guidance documents, and enforcement actions, applies existing consumer protection and competition statutes to AI-related harms including deceptive AI claims, discriminatory automated decisions, and unfair data practices underpinning AI systems.
G7 Hiroshima AI Code of Conduct
A voluntary international code of conduct issued under the G7 Hiroshima AI Process, setting out eleven guiding principles and corresponding actions for developers and operators of advanced AI systems, with a particular focus on frontier and general-purpose AI models. The code operates as a soft-law commitment mechanism intended to shape responsible AI development globally.
India AI Governance Framework
MeitY's advisory framework establishes responsible AI principles and interim compliance expectations for platforms deploying AI systems in India, with particular focus on preventing harm, ensuring traceability, and requiring government approval before deploying undertested AI models.
Japan AI Guidelines for Business
METI guidelines providing Japanese businesses with practical AI governance standards aligned with the Hiroshima AI Process and international frameworks, addressing risk management, transparency, accountability, and intellectual property considerations across the AI lifecycle.
Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT) in the Use of Artificial Intelligence and Data Analytics in Singapore's Financial Sector
The MAS FEAT Principles establish a voluntary but authoritative framework for financial institutions in Singapore to govern the responsible use of AI and data analytics, structured around four principles: Fairness, Ethics, Accountability, and Transparency.
New Zealand Responsible AI Guidance for Business
The New Zealand Government has published comprehensive guidance to help businesses implement responsible AI practices in commercial contexts. It provides a practical framework covering AI governance structures, risk management processes, and accountability expectations. While non-binding, the guidance reflects government expectations for how enterprises operating in New Zealand should develop and deploy AI systems.
NIST Guidance and Templates for Public-Facing AI Documentation (Initial Public Draft)
NIST released an initial public draft on 29 July 2026 providing guidance and standardized templates for how organizations should document AI systems intended for public-facing use. The draft applies to any organization developing, deploying, or procuring AI systems that interact with or affect the general public. It establishes expectations for model transparency disclosures, internal review workflows, and structured documentation practices.
OMB Memorandum M-26-04: Increasing Public Trust in AI Through Unbiased AI Principles
OMB Memorandum M-26-04 establishes unbiased AI principles that federal agencies must follow when deploying AI systems that interact with or affect the public. It applies to executive branch agencies procuring, developing, or operating AI tools in federal programs. Agencies are required to implement safeguards against algorithmic bias and to promote transparency and accountability in AI-driven federal decision-making.
SEC AI Governance Guidance
The SEC has issued rules, guidance, and proposed rulemaking addressing AI governance obligations for registered investment advisers, broker-dealers, and public companies, focusing on conflicts of interest in predictive data analytics, AI-related disclosures in securities filings, and examination priorities targeting algorithmic systems.
UK AI Growth Lab Regulatory Sandbox - Consultation on Two Models
The UK Department for Science, Innovation and Technology launched a consultation in October 2025 on the AI Growth Lab, a proposed regulatory sandbox enabling companies to test AI innovations under modified regulatory conditions. Two structural models are under consideration: a centrally operated sandbox administered by the government across multiple sectors, and a regulator-operated model in which a designated lead regulator manages each sandbox instance. The initiative is intended to reduce compliance barriers for AI development while maintaining appropriate oversight.
UK ICO Guidance on Artificial Intelligence and Data Protection
The UK ICO's guidance on AI and data protection establishes how the UK GDPR and Data Protection Act 2018 apply to the design, development, and deployment of AI systems that process personal data.
UNESCO Recommendation on the Ethics of Artificial Intelligence
The UNESCO Recommendation on the Ethics of Artificial Intelligence is the first global normative instrument on AI ethics adopted by a UN body, providing a comprehensive ethical framework and specific policy recommendations across eleven thematic areas for all 194 UNESCO Member States.
Verifiable Semiconductor Manufacturing: Governance and Verification Systems for AI Supply Chain Oversight
This guideline from the Oxford Martin AI Governance Initiative addresses governance and verification mechanisms applied to semiconductor manufacturing in the context of AI supply chains. It is relevant to organizations involved in the design, production, procurement, or deployment of AI hardware at scale. The publication outlines approaches for establishing verifiable assurances about semiconductor provenance, integrity, and production standards.