AI Governance
Know what actually attaches to your AI use case.
Most intake processes return a risk rating. A rating tells you how much review to do — it does not tell you what you are required to produce, who must be named, or which of the generative and agentic risks apply. This returns all four.
Ten questions, deterministic rules published in full, anchored to the EU AI Act, NIST AI RMF, the NIST Generative AI Profile, ISO/IEC 42001 and the OECD principles. Nothing you enter leaves your browser.
Three pillars, three instruments
AI Intake
Classify a use case. Lane, modules, overlays and the obligations that attach.
Below on this pageAI Accountability
Could you name the four owners in sixty seconds? Eleven weighted checks.
Run the check →Responsible AI
Score nine control themes, get a maturity level and the controls that close your weakest three.
Assess controls →All three rest on the same published method — theoperating modeland thestandards registercovering the EU, UK, US, Asia-Pacific, Canada and the international instruments.
Three pillars
AI Intake
Every proposed or discovered use case is captured, classified and routed, with traceability from idea to decision.
AI Accountability
Named individuals own each system across its life, with evidence, approvals and an escalation path.
Responsible AI
Risk is mapped and mitigated across validity, safety, fairness, transparency, privacy, oversight and the generative and agentic surfaces.
The four routes
Stop — prohibited-practice review
The screen flagged a practice that may be prohibited outright. This is not a risk rating to be mitigated; it goes to legal review before any further work, and may not proceed at all.
Immediate — legal review before any build, procurement or pilot
High-risk lane
Full assessment across every applicable module, plus independent second-line review. If you are the provider, a conformity route applies before this may be placed on the market.
Scheduled assessment — expect weeks, and plan for it
Standard lane
Full assessment at proportionate depth, with the modules and overlays listed below.
Typically 10–20 working days
Fast lane
Attestation-only. Register it, name the owners, confirm the boundaries, proceed. The point of a fast lane is that most AI is genuinely low-risk and should not queue behind the things that are not.
Under 10 working days
The 10 classification dimensions
- 01Prohibited-practice screen — Does the system do any of the following?
- 02Your role — What is your organisation’s role in relation to this system?
- 03Type of system — What kind of AI is this?
- 04Data sensitivity — What is the most sensitive data the system touches?
- 05Who is affected — Who is on the receiving end of this system’s output?
- 06Material consequences — What does the output actually do to a person?
- 07Human oversight — How much human involvement is there in practice?
- 08Impact of error — If the system is confidently wrong, what is the worst realistic outcome?
- 09Reversibility — If it acts wrongly, how easily can the effect be undone?
- 10Deployment context — Where does the system sit?
The published rules
- Rule 1 · Prohibited pre-screen
If any prohibited-practice flag → Stop — legal review. Nothing downstream can override this. - Rule 2 · High-risk short-circuit
If the output determines an outcome affecting a person, OR special-category data is involved with real-world effect, OR impact of error is critical, OR the system is autonomous and hard to reverse → High-risk lane with independent review - Rule 3 · Generative overlay
If the system is generative, or built on a third-party foundation model → Adds the generative overlay — grounding, confabulation, prompt injection, provenance, IP, vendor model change - Rule 4 · Agentic overlay
If the system plans, calls tools, holds memory or acts without per-step approval → Adds the agentic overlay — autonomy classification, agent identity, tool permissions, memory, blast radius, kill-switch - Rule 5 · Fast lane
If no personal data AND internal only AND nobody externally affected AND not generative or agentic AND reversible AND low impact of error → Fast lane — attestation only - Rule 6 · Default
If anything else → Standard lane
Obligations the classifier maps
- AI literacy — Everyone. Providers and deployers alike, at every risk level, including fast-lane systems.
- Transparency to people — Where a person interacts with the system, or where it generates or manipulates content they may take to be genuine.
- Fundamental rights impact assessment — Deployers of certain high-risk systems, notably public bodies and those providing essential services.
- Conformity assessment and declaration — Providers of high-risk systems, before the system may be placed on the market or put into service.
- Registration — High-risk systems, registered in the EU database before being placed on the market or put into service.
- Quality management system — Providers of high-risk systems.
- Post-market monitoring — High-risk systems, for the whole time they remain in service.
- Serious incident reporting — Providers of high-risk systems, on becoming aware of a serious incident or malfunction.
- Record-keeping and logging — High-risk systems, with automatic logging over the system’s lifetime.
- Human oversight — High-risk systems, designed so oversight is effective rather than nominal.
- General-purpose model obligations, inherited — Anyone building on a third-party foundation model. The provider’s obligations do not become yours, but their documentation is how you discharge your own.
- Retention versus minimisation — Any high-risk system processing personal data.
Does this reach you at all?
- — The EU AI Act applies extraterritorially. A provider or deployer established outside the EU is in scope where the system’s output is used in the EU — which catches a great many organisations that assume they are out of scope.
- — The provider and deployer split is the first question, not a detail. Badging someone else’s system as your own can make you the provider.
- — Free and open-source models carry partial exemptions, which do not extend to systems placed on the market as high-risk, nor to prohibited practices.
- — Sectoral regimes — model risk management in financial services, medical device regulation, employment law — sit on top and frequently bite sooner.
The full operating model — 9 control themes mapped across four frameworks, the 12 generative risk categories, 11 agentic controls, shadow-AI discovery and a 5-level maturity model — is on the framework page.
Common questions
- What is an AI intake process?
- An AI intake process is the single structured front door through which every proposed or discovered AI use case enters formal governance. It captures the context needed to understand the proposal, classifies its risk, routes it to the right depth of review, and ends in a documented decision. It handles both submitted intake, where a person proposes something, and discovered intake, where scanning, procurement data or vendor disclosure surfaces AI that never came through the door.
- How do I know if my AI system is high-risk?
- Work through the classification dimensions in order. The decisive questions are whether the output determines an outcome affecting a person’s rights, access, livelihood or safety; whether special-category personal data is involved with real-world effect; whether the worst realistic error causes harm to health, safety or fundamental rights; and whether the system acts autonomously in ways that are hard to reverse. Any one of those routes the system to the high-risk lane. A prohibited-practice screen runs first and overrides everything.
- What is the difference between an AI provider and an AI deployer?
- A provider develops an AI system, or has one developed, and places it on the market or puts it into service under its own name. A deployer uses an AI system under its own authority. The obligations differ substantially — conformity assessment, registration and serious-incident reporting largely fall on providers, while fundamental rights impact assessment and operational oversight fall on deployers. Putting your own name on a third party’s system can make you the provider.
- Who should be accountable for an AI system?
- Four named individuals, not teams: a business owner accountable for value and intended use, a technical owner accountable for operational integrity and holding standing authority to pause, a model or agent owner accountable for model selection, prompts, tool permissions and memory, and a data owner accountable for provenance, licensing and right to use. Second-line functions challenge and report; internal audit assures. Risk accepted by a committee is risk accepted by nobody.
- What is shadow AI and how do you find it?
- Shadow AI is unsanctioned use; ambient AI is capability quietly embedded by vendors into tools you already bought. Neither arrives through the front door, and together they are the largest real exposure in most organisations. Discovery combines procurement and expense analysis, network and SaaS telemetry, identity and OAuth grant review, vendor disclosure requests, repository scanning, and periodic blameless amnesties. The number that matters is the ratio of discovered to submitted — if it is not falling, the front door is not working.
- How should agentic AI be governed differently?
- Classify autonomy first, because it determines every other control. Then give the agent its own non-human identity with scoped credentials, whitelist its tools under least privilege, bound what it may remember, cap consequential transactions, and contain blast radius so data-modifying actions are reversible by default. Agentic governance assumes agents will occasionally be creative in unproductive ways, so controls constrain blast radius rather than relying on intent — because intent is the thing you cannot inspect.
- When should a human be in the loop rather than on the loop?
- In the loop, meaning a human approves each output before it has effect, where the output is binding on a person, materially affects rights, access, pricing or employment, or is effectively irreversible. On the loop, where the output influences a decision a person still makes, or where actions are reversible within a useful window. Over the loop, with sample and aggregate review, where output is internal, low-consequence and high-volume. Oversight that leaves no evidence of a human ever disagreeing with the system is decorative.
- Does the EU AI Act apply to organisations outside the EU?
- Yes, in defined circumstances. The Act reaches providers and deployers established outside the EU where the output produced by the system is used within the EU. Organisations frequently assume they are out of scope on the basis of where they are headquartered, which is the wrong test.
Indicative guidance, never a legal determination. Obligations are described by category rather than by date, because the EU AI Act’s phase-in schedule has been amended — verify current effective dates against the Official Journal before relying on them.