Every regime that reaches you

Five values-based principles and five policy recommendations, endorsed by a large group of jurisdictions and revised to address safety, information integrity and intellectual property.

So what — The common vocabulary underneath most national AI policy. If your principles do not map to these, you will struggle to explain yourself internationally.

The broadest multilateral ethics instrument on AI, adopted by member states, covering human rights, environment, gender equality and readiness assessment.

So what — The reference point when operating in jurisdictions with no AI law of their own, and the one that takes environmental and societal impact most seriously.

The first international treaty on AI, open to signature beyond Europe, obliging parties to adopt measures protecting human rights, democracy and the rule of law in the AI lifecycle.

So what — Treaty-level rather than guidance, so it shapes national law in signatory states over time rather than applying to you directly today.

Voluntary commitments for organisations developing advanced AI systems: risk identification, red teaming, incident reporting, transparency reporting and content authentication.

So what — A useful external benchmark for frontier and foundation-model work, and the clearest statement of what "responsible developer" is taken to mean at state level.

A certifiable management system standard for AI, with management-system clauses and an Annex A control set organised under control objectives spanning policy, organisation, resources, impact assessment, lifecycle, data, information for interested parties, use, and third-party relationships.

So what — The natural vehicle for a quality management system obligation, and the only AI standard you can currently certify against.

Guidance on managing risk specific to AI, aligned to the ISO 31000 risk management structure.

So what — The companion to 42001. Where 42001 tells you to manage risk, this tells you how — and it is the bridge to enterprise risk management your risk function already uses.

Standardised vocabulary and concepts for AI systems.

So what — Unglamorous and genuinely useful. Most governance arguments are definitional, and a shared vocabulary ends a surprising number of them.

Lifecycle processes for AI systems, extending established software and systems lifecycle standards to AI-specific stages.

So what — Maps AI governance onto the engineering lifecycle your delivery teams already follow, rather than running alongside it.

Management system for information security, with an annexed control set.

So what — AI security obligations do not replace information security ones. Most organisations should extend an existing 27001 scope rather than build a parallel regime.

Risk-tiered regulation of AI systems — prohibited practices, high-risk obligations including conformity assessment, registration, quality management, logging, human oversight and post-market monitoring, transparency duties, general-purpose model obligations and an AI literacy duty.

So what — The only comprehensive binding AI law of its kind, and it applies extraterritorially where output is used in the Union. Phase-in has been amended; verify current dates against the Official Journal.

Lawful basis, data minimisation, purpose limitation, impact assessment, data-subject rights, automated decision-making safeguards and cross-border transfer.

So what — Predates AI and governs most of it anyway. The automated decision-making provisions and the right to explanation bite long before the AI Act does.

Medical devices, machinery, vehicles, financial services and similar regimes, into which AI obligations are embedded rather than standing alone.

So what — Where AI is a component of a regulated product, the product regime usually governs first and the AI obligations ride on top of its conformity route.

Five principles applied through existing regulators rather than a single statute: safety, security and robustness; appropriate transparency and explainability; fairness; accountability and governance; and contestability and redress.

So what — The UK deliberately chose regulator-led application over a horizontal law. That means your obligations come from your sector regulator, not from one AI act — which is easy to miss if you are reading EU material.

How UK data protection law applies to AI, including lawful basis, fairness in AI, explaining decisions, and an AI and data protection risk toolkit.

So what — The most practically useful UK material, and the one a UK regulator will actually measure you against.

Model identification, model risk governance, development and validation standards, independent review and ongoing monitoring for regulated firms.

So what — If you are a regulated firm, your AI governance must reconcile with model risk management rather than sit beside it. Two registers is a finding.

Public evaluation research and open-source evaluation tooling for advanced models.

So what — A credible external reference for what model evaluation looks like when done seriously, and useful input to your own pre-release evaluation design.

A voluntary framework structured as Govern, Map, Measure and Manage, with categories and subcategories and an accompanying playbook.

So what — Voluntary, but it has become the de facto structure for AI risk programmes worldwide, including in organisations with no US presence.

Twelve generative-AI-specific risk categories with suggested actions mapped to the AI RMF core functions.

So what — The best available completeness check for generative risk. If your assessment template cannot answer against all twelve, it has a gap.

A comprehensive catalogue of security and privacy controls for information systems.

So what — Where AI security controls need to land inside an existing control catalogue rather than a new one, this is usually the catalogue.

Obligations attaching to consequential automated decisions — notably in employment screening, insurance, lending and biometric processing — including bias auditing and candidate notification duties in some jurisdictions.

So what — The most commonly missed US exposure. There is no single federal AI law, so the binding obligations arrive state by state and sector by sector.

Model risk management expectations in banking, medical device pathways for AI-enabled software, and consumer protection enforcement against unfair or deceptive AI claims.

So what — Existing regulators have been explicit that current law already applies to AI. Waiting for an AI-specific statute is not a strategy.

Practical governance guidance covering internal structures, risk-based decision-making, operations management and stakeholder communication, extended for generative AI.

So what — The most implementable of the national frameworks, and the one most often borrowed by organisations that find NIST too abstract.

An open-source testing framework and toolkit for validating AI system behaviour against governance principles.

So what — A rare example of governance expressed as runnable tests rather than a questionnaire. Worth studying even if you never adopt it.

Consolidated guidance for developers, providers and users of AI, oriented to voluntary adoption and agile governance.

So what — Represents the light-touch, multi-stakeholder end of the spectrum, and distinguishes obligations by role in a way many frameworks do not.

Registration and filing requirements for algorithms and generative services, content labelling, security assessment and training-data requirements.

So what — Among the most prescriptive regimes in force. If you serve users there, filing and labelling obligations attach regardless of where you are based.

An algorithmic impact assessment requirement with tiered obligations for federal automated decision systems, covering notice, explanation, human intervention and recourse.

So what — One of the earliest working examples of tiered impact assessment, and still one of the clearest models for how to scale obligations to consequence.

The most common security weaknesses in LLM applications, including prompt injection, insecure output handling, training-data poisoning, model denial of service, supply-chain vulnerability and excessive agency.

So what — The single most practical security reference for generative systems, and "excessive agency" is the closest the security community has come to naming the agentic problem.

A knowledge base of adversary tactics and techniques against AI systems, structured like ATT&CK.

So what — Turns "we did a threat model" into something testable, using a vocabulary your security team already works in.