Search and filter source-backed AI laws, regulations, standards, guidance and consultations. Each record shows its status, key dates, verification state and official source.
Endorsed at the 4th ASEAN Digital Ministers' Meeting in February 2024, the guide sets seven guiding principles (transparency and explainability, fairness and equity, security and safety, human-centricity, privacy and data governance, accountability and integrity, robustness and reliability) and a four-part governance framework (internal governance structures, human involvement in decision-making, operations management, stakeholder interaction), with national and regional recommendations including an ASEAN working group on AI governance.
Adopted 2 Feb 2024Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Proposals paper: introducing mandatory guardrails for AI in high-risk settings (Australia)
The proposals paper sought views on ten mandatory guardrails for developers and deployers of AI in high-risk settings, a principles-based definition of high-risk AI (with general-purpose AI treated as high-risk), and three regulatory options: adapting existing laws, framework legislation, or a new cross-economy AI Act. The guardrails mirror the Voluntary AI Safety Standard, covering accountability, risk management, data governance, testing, human control, transparency, contestability, supply-chain transparency, records, and conformity assessment.
Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
The Voluntary AI Safety Standard gives Australian organisations ten guardrails for developing and deploying AI safely and responsibly: accountability and governance, risk management, data governance and protection, testing and monitoring, human control and intervention, user transparency, contestability, supply-chain transparency, record keeping, and stakeholder engagement. The guardrails were designed to align with the mandatory guardrails proposed for high-risk settings so that early adopters would be prepared if those became law.
Adopted 5 Sep 2024Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Jordanian Charter of Artificial Intelligence Ethics
The charter sets ethical principles for the development and use of AI in Jordan, including human dignity, fairness, transparency, accountability, safety, privacy and sustainability, addressed to public and private organisations.
Adopted 1 Aug 2022Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
CNIL recommendations on the development of AI systems and the GDPR (AI how-to sheets)
A series of practical guidance sheets, first published in 2024 and extended since, explaining how organisations can develop and train AI systems in compliance with the GDPR: defining a purpose, choosing a legal basis (including legitimate interest for web-scraped training data), data minimisation, retention, data-protection impact assessments, informing people and honouring their rights, and security of training datasets and models.
Adopted 8 Apr 2024Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Етичке смернице за развој, примену и употребу поуздане и одговорне вештачке интелигенције
Adopted in March 2023, the guidelines set principles and requirements for trustworthy and responsible AI in Serbia (human agency, safety, privacy, transparency, non-discrimination, societal well-being, accountability), with self-assessment questions for developers and users, drawing on the EU ethics guidelines and the UNESCO recommendation.
Adopted 23 Mar 2023Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Expanded ASEAN Guide on AI Governance and Ethics – Generative AI
Endorsed in January 2025, the expanded guide addresses risks specific to generative AI (hallucination, deepfakes and misinformation, intellectual property, privacy, security, bias, embedded values) and proposes governance dimensions covering accountability, data, development and deployment, incident reporting, testing and assurance, security, content provenance, safety research and public-interest use, with policy recommendations for member states.
Adopted 17 Jan 2025Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Hong Kong Generative Artificial Intelligence Technical and Application Guideline
Published in April 2025, the guideline covers the technical characteristics and risks of generative AI, governance principles (safety, transparency, accountability, data protection, fairness) and practical application guidance for developers, service providers and users across the AI lifecycle, and is intended as a reference for industry and government.
Adopted 15 Apr 2025Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Methodology for the Risk and Impact Assessment of Artificial Intelligence Systems from the Point of View of Human Rights, Democracy and the Rule of Law (HUDERIA)
Adopted by the CAI in November 2024, HUDERIA is a structured methodology for assessing risks and impacts of AI systems on human rights, democracy and the rule of law across the lifecycle: context-based risk analysis, stakeholder engagement, risk and impact assessment, and mitigation planning with iterative review. It supports implementation of the Framework Convention and can be used by public and private organisations.
Adopted 28 Nov 2024Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
The ICO's guidance explains how UK GDPR and the Data Protection Act 2018 apply when organisations develop or use AI that processes personal data. It covers accountability and governance, lawfulness and fairness, transparency, data minimisation, security, individual rights, and automated decision-making. The guidance is not itself law, but it reflects how the regulator interprets binding obligations and is the reference point in ICO enforcement.
Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
The India AI Governance Guidelines set out a principle-based, pro-innovation approach to governing AI in India. They articulate guiding principles (such as trust, people-first design, fairness, accountability, safety and transparency), propose an institutional framework including an AI governance group and an AI Safety Institute role, favour applying existing laws over a new AI statute, and recommend voluntary commitments, techno-legal measures, risk-based oversight and incident reporting for AI systems.
Adopted 5 Nov 2025Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
ISO/IEC 42001:2023 Information technology – Artificial intelligence – Management system
The first certifiable management-system standard for AI. It specifies requirements for establishing, implementing, maintaining and improving an AI management system within an organisation: context and leadership, AI policy, risk and impact assessment, objectives, resources and competence, operational controls across the AI lifecycle, performance evaluation and improvement, with Annex A controls on data, transparency, human oversight, third parties and responsible use. Certification is voluntary but increasingly requested in procurement; regulators reference it as evidence of governance.
Adopted 18 Dec 2023Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
National Artificial Intelligence Policy, 2082 (2025) — Nepal
Nepal's National AI Policy sets the government's direction for developing and using artificial intelligence. Based on the published summaries, it aims to build AI infrastructure and skills, promote ethical and responsible AI, strengthen data governance, establish institutional arrangements for AI oversight, and prepare legal and regulatory measures. It is a policy framework, not a law, and does not itself create enforceable obligations on private organisations.
Adopted 1 Aug 2025Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
NIST AI Risk Management Framework (AI RMF 1.0) and Generative AI Profile
The NIST AI Risk Management Framework is a voluntary framework for managing risks to individuals, organisations and society from AI. It organises practices into four functions: Govern, Map, Measure and Manage, and describes characteristics of trustworthy AI such as validity, safety, security, accountability, explainability, privacy and fairness. The July 2024 Generative AI Profile (NIST AI 600-1) adds risks and suggested actions specific to generative AI. Many US procurement requirements and state laws reference the framework as a recognised approach.
Adopted 26 Jan 2023Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
OECD Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449), adopted 22 May 2019 and revised 3 May 2024
The first intergovernmental standard on AI. It sets five values-based principles (inclusive growth and well-being; human rights and democratic values including fairness and privacy; transparency and explainability; robustness, security and safety; accountability) and five recommendations to governments (invest in research, foster an inclusive AI ecosystem, enable an agile policy environment, build human capacity and prepare for labour-market transition, cooperate internationally). The 2024 revision updated the definition of an AI system (adopted by the EU AI Act and other laws) and added references to general-purpose and generative AI, misinformation and environmental sustainability. Adherents include all OECD members and several non-members; the G20 AI Principles (2019) draw on it.
Adopted 22 May 2019Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Artificial Intelligence: Model Personal Data Protection Framework
Published 11 June 2024, the framework gives organisations that procure, implement and use AI systems involving personal data recommendations in four areas: AI strategy and governance (an AI governance committee, procurement due diligence), risk assessment and human oversight (a risk-based approach with levels of human involvement), customisation and implementation of AI models (data preparation, testing, security), and communication and engagement with stakeholders (transparency, explainability, opt-out, feedback). It builds on the 2021 Guidance on the Ethical Development and Use of AI.
Adopted 11 Jun 2024Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
PDPC Advisory Guidelines on Use of Personal Data in AI Recommendation and Decision Systems
These advisory guidelines explain how the Personal Data Protection Act applies when organisations use personal data to develop, test and deploy AI systems that make recommendations, predictions or decisions. They clarify the consent obligation and relevant exceptions (business improvement and research), what to include in notifications to individuals, accountability practices such as documenting data provenance and model development, and expectations for service providers building bespoke AI systems.
Adopted 1 Mar 2024Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Rome Call for AI Ethics (Pontifical Academy for Life, 28 February 2020)
A declaration of six principles for the ethical development of AI (transparency, inclusion, responsibility, impartiality, reliability, security and privacy), first signed in February 2020 by the Pontifical Academy for Life, Microsoft, IBM, FAO and the Italian government, and since joined by other religious leaders, universities and companies. It calls for "algor-ethics" and human-centred design and is used as a reference in faith-based and corporate AI ethics commitments.
Adopted 28 Feb 2020Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
A governance framework for AI agents that reason and act on a user's behalf, in four areas: assessing and bounding risks upfront, making humans meaningfully accountable, technical controls across the agent lifecycle, and enabling end-user responsibility. IMDA treats it as a living document and has since added case studies and input from more than 50 organisations.
Pending review (a factual check against the official source, not a legal review)Official source
Accredits third-party firms that test AI systems, in benchmarking and red teaming, for 12 months at a time; the first five were AIDX Tech, Knovel Engineering, PwC Singapore, Resaro and Vulcan. Accreditation does not certify that a tester is independent or that a tested system is safe.
Pending review (a factual check against the official source, not a legal review)Official source
Anything a government or standards body has actually issued: acts, regulations, executive orders, rules, national strategies, frameworks, guidance, codes of practice, voluntary standards and open consultations. Each record states which of those it is, whether it binds anyone, and links the official source it was read from.
Are these records legally verified?
A record is marked verified only after a named reviewer has opened its official source and confirmed each field. Anything that has not been through that carries "pending review" on the record itself, and the data-health document reports the current totals. Nothing here is legal advice.
How current is the information?
Every record carries the date its source document was published and the date it was last checked. A record not re-checked within the freshness window set by the verification policy is flagged as stale rather than quietly presented as current.
Can I reuse this data?
Yes. The records are published under CC BY 4.0 with attribution, and are available as CSV, newline-delimited JSON and a read-only REST API that needs no key. Cite the record URL, the date accessed and the official source alongside it.
