New or occasional contributor
"I don’t want to get this wrong."
Plain-language guidance, safe defaults, confidence prompts, buddying and clear role limits.
Take the 5-minute triage, then use the declaration generator.
A public-good, government-grade AI knowledge utility
Build safer, fairer and more accountable AI-assisted public services.
A practical portal and toolkit for AI skills, agents, metadata standards, human review, code of practice, secure-by-design delivery, protection by default, quantum readiness and inclusive user-centred design — with the guidance, templates and working tools you need to act today.
100%material AI-assisted work creates evidence
0self-approval for high-risk outputs
H0–H5risk-based human-in-the-loop model
PQCcrypto-agile and quantum-ready by design
Four non-negotiable principles
- Humans remain accountable. AI never grants authority a person does not already hold.
- AI outputs are verified, not trusted by default. Every claim traces to a citable source.
- Public value, equality and accessibility are built in — screened before delivery, not audited after.
- Evidence, metadata and provenance are mandatory for anything material or public-facing.
Start here
Pick the route that matches your job today
Task-first entry points, because people arrive with a decision to make — not an interest in our information architecture.
Experienced practitioner
"Give me the fast, correct route."
Direct links to standards, patterns, conformance packs, test scripts and evidence automation.
Specialist reviewer
"Show me what to challenge."
Source-linked evidence, issue triage, risk decisions and assurance history.
Accountable owner / SRO
"What am I signing?"
Risk overview, approval gates, residual risk, maturity dashboard and public transparency records.
Generate the transparency record and check the MoSCoW roadmap.
Supplier or delivery partner
"What will I be assessed against?"
Published expectations, evidence formats, accessibility and security obligations, exit terms.
Member of the public
"How is AI being used on my case?"
Plain-English explanation, published records, how to challenge a decision and get support.
Common tasks
2 minutes
Decide how much human review my output needs
Answer six questions and get an H0–H5 level, the reviewers required and the evidence to keep.
3 minutesWrite an AI-use declaration and evidence pack
Generate a copy-ready record of sources consulted, checks made and who approved what.
5 minutesScreen a change for equality and accessibility impact
Flag protected characteristics, access needs, assisted digital and exclusion risks early.
10 minutesDraft a public transparency record
Produce an ATRS-shaped record describing the tool, its purpose, data, oversight and risks.
5 minutesCheck quantum and cryptographic exposure
Score harvest-now-decrypt-later risk against NCSC migration milestones.
ReferenceFind the authoritative source for a rule
Search 68 tiered laws, standards, guidance documents and tools.
Not sure whether to use AI at all? Ask three questions first: is there a simpler non-AI way to meet the user need; can you explain the decision to the person it affects; and could you defend this use at a public inquiry? If any answer is no, stop and seek advice.
Combined body of knowledge
Layer 1
Layer 2
Layer 3
Authoritative guidance, standards and reusable intelligence
The corpus is organised as a knowledge graph linking laws, policies, standards, design guidance, security controls, skills, agents and evidence — so that a rule can always be traced to its source and its consequences.
Guidance corpus
Index GOV.UK, GDS, CDDO, departmental, international standards, GitHub and engineering-lab sources with authority tiering, freshness checks and applicability metadata.
Knowledge graph
Link services, repositories, datasets, APIs, controls, skills, agents, people, approvals, models, risks and evidence so impact analysis is a query, not a workshop.
Executable guidance
Translate policy and standards into schemas, conformance packs, tests, scripts, checklists and prompt patterns that run in delivery pipelines.
Authority tiering — how conflicts are resolved
When two sources disagree, the lower tier number wins. Record the conflict and the resolution in the evidence pack.
| Tier | Source type | Examples | Treatment |
|---|---|---|---|
| 0 | Law and statutory instruments | Equality Act 2010, DPA 2018, Accessibility Regulations 2018 | Binding. Non-compliance is not a risk to accept — escalate. |
| 1 | Mandated standards and cross-government policy | Service Standard, Technology Code of Practice, Secure by Design, WCAG 2.2, ATRS | Comply or record a formal, time-boxed exception with an owner. |
| 2 | Recognised international and professional standards | ISO/IEC 42001, NIST AI RMF, OWASP LLM Top 10 | Adopt as good practice; justify divergence. |
| 3 | Community, vendor and research material | Blogs, model cards, toolkits, preprints | Useful signal. Never the sole basis for a control or a public claim. |
Freshness and drift controls
Every indexed source carries
- • Authority tier and issuing body
- • Date checked and next check due
- • Applicability (organisation, service type, data classification)
- • Supersession chain and withdrawal status
- • Linked controls, skills and conformance tests
Drift triggers a review when
- • A cited source is updated, withdrawn or superseded
- • A model or its version changes materially
- • A control fails in production or a conformance test breaks
- • An incident, complaint or FOI request touches the artefact
- • The scheduled review date passes without confirmation
Toolkit
A complete operating kit for AI-assisted public delivery
Every asset is designed to be catalogued, versioned, reviewed and reused. Filter by purpose, or search across names, descriptions and linked standards.
Searches asset names, descriptions and linked standards.
Showing 18 of 18 toolkit assets.
AI asset metadata standard
Machine-readable schemas for skills, agents, prompts, tools, model-use events, evidence packs and source provenance.
Output: JSON Schema + YAML template
Anchored to: PROV-O (opens an external website), CycloneDX ML-BOM (opens an external website), DCAT 3 (opens an external website)
Skill registry
Approved, draft and deprecated skills with owners, sources, risk ratings, compatibility and evidence rules.
Output: Register + lifecycle policy
Anchored to: Model cards (opens an external website), alphagov (opens an external website)
Agent registry and bounding
Bounded agents by autonomy level, permissions, allowed skills, prohibited actions, model constraints and kill-switch owner.
Output: Register + autonomy policy
Anchored to: MCP (opens an external website), OWASP LLM Top 10 (opens an external website)
Evidence pack builder
Source consultation records, AI-use declarations, validation results, human acknowledgements and audit trails.
Output: Generator on this page
Anchored to: ATRS (opens an external website)
DEIA impact screen
Equality, accessibility, assisted digital, digital inclusion, protected characteristic and vulnerability screening.
Output: Screening tool on this page
Anchored to: PSED (opens an external website), WCAG 2.2 (opens an external website)
Quantum readiness assessor
Inventory cryptography, identify long-life data, assess harvest-now-decrypt-later exposure and plan crypto-agility.
Output: Assessment tool on this page
Anchored to: NCSC PQC (opens an external website), NIST PQC (opens an external website)
Human review triage (H0–H5)
Risk-based routing that decides how many eyes, which specialists and what evidence an AI-assisted output needs.
Output: Triage tool on this page
Anchored to: NIST AI RMF (opens an external website)
Prompt and context pattern library
Reviewed prompt patterns with source-citation requirements, refusal conditions, data-handling rules and test cases.
Output: Pattern library + tests
Anchored to: GenAI Framework for HMG (opens an external website)
Conformance packs
Executable checks turning WCAG, Service Standard, Secure by Design and TCoP clauses into automated tests.
Output: CI pack
Anchored to: Pa11y (opens an external website), Secure by Design (opens an external website)
Transparency record generator
Produce an ATRS-shaped public record for any algorithmic or AI-assisted tool in use.
Output: Generator on this page
Anchored to: ATRS hub (opens an external website)
Supplier and procurement assurance kit
Questions, evidence asks and contract clauses for AI suppliers: model provenance, evaluations, accessibility, exit.
Output: Question bank + clauses
Anchored to: ISO/IEC 42001 (opens an external website), EN 301 549 (opens an external website)
Incident, near-miss and drift reporting
Report AI failures, hallucinations, bias incidents, prompt injection and source drift with learning loops.
Output: Process + register
Anchored to: MITRE ATLAS (opens an external website), NCSC secure AI (opens an external website)
Model and data provenance ledger
Signed, tamper-evident record of which model, version, prompt, source and human touched each artefact.
Output: Ledger spec
Anchored to: C2PA (opens an external website), SLSA (opens an external website)
Sustainability and cost-to-serve calculator
Estimate energy, carbon and unit cost of model calls, and test whether a non-AI option is proportionate.
Output: Calculator + reporting
Anchored to: Greening Government ICT (opens an external website), W3C WSG (opens an external website)
Inclusive research and co-design kit
Recruitment, consent, reimbursement and session plans that reach disabled, digitally excluded and seldom-heard users.
Output: Research kit
Anchored to: Research with disabled people (opens an external website), Assisted digital (opens an external website)
Plain-language and reading-age checker
Test AI-drafted content against GOV.UK style, reading age 9, and inclusive terminology before publication.
Output: Checker on this page
Anchored to: GOV.UK style guide (opens an external website)
Records, retention and FOI readiness
Classify AI interaction logs as records; set retention, disclosure and redaction rules ahead of FOI/SAR requests.
Output: Retention schedule
Anchored to: DPA 2018 (opens an external website)
Capability and competence pathway
Role-based learning paths, licence-to-operate checks and refreshers for anyone using AI on public work.
Output: Curriculum map
Anchored to: DDaT framework (opens an external website)
No assets match. Try a broader term or reset the filter.
Working tools
Do the work here, not in a separate spreadsheet
Six tools that turn the guidance into an artefact you can paste into a ticket, a change record or a board paper. Everything runs in your browser — nothing you type is sent anywhere, stored or transmitted.
Privacy by design: these tools are client-side only. Do not enter personal data, case details or anything above OFFICIAL. Use references and descriptions instead.
1. Human review triage — what level of oversight does this need?
Your result will appear here.
2. AI-use declaration and evidence pack generator
Produces a copy-ready record. Attach it to the artefact, the ticket or the change record.
Your result will appear here.
3. DEIA impact screen — equality, accessibility and inclusion
A screening tool, not a substitute for a full equality impact assessment. It tells you whether you need one.
Your result will appear here.
4. Transparency record generator (ATRS-shaped)
Drafts a public-facing record of how an algorithmic or AI tool is used. Publish it, and link it from the service.
Your result will appear here.
5. Quantum readiness and crypto-agility assessor
Your result will appear here.
6. Plain-language and inclusive content checker
Paste AI-drafted content to check reading difficulty, sentence length, jargon and non-inclusive phrasing before publication.
Nothing leaves your browser. Do not paste personal data.
Your result will appear here.
Assurance
Human-centred controls for trustworthy AI work
Risk-based human-in-the-loop controls make sure AI never grants authority a user does not already possess, and that every material output can be defended after the fact.
The five-stage assurance workflow
- Classify — identify service impact, data sensitivity, equality and accessibility risk, security risk and model/agent autonomy. Use the triage tool →
- Select — apply the standards, source guidance, skills, agents, conformance packs and review level the classification demands.
- Execute — work with least privilege, mandatory source citation, deterministic checks and pair or buddy support where competence is thin.
- Review — 4-eyes or 6-eyes review, specialist assurance, and no self-approval for material or high-risk outputs.
- Learn — capture evidence, incidents, near-misses, feedback, source drift and model changes; feed them back into the corpus.
The H0–H5 human review model
| Level | Name | What it means | Typical examples | Evidence |
|---|---|---|---|---|
| H0 | No review needed | Personal, throwaway, non-published exploration with no personal or sensitive data. | Explaining a concept to yourself; drafting private notes. | None required. |
| H1 | Self-check | Author verifies facts, sources and accessibility before use. Low impact, easily reversible. | Internal meeting notes, first-draft ideas. | AI-use note in the document. |
| H2 | Peer check | A second competent person checks output before it leaves the team. | Internal reports, non-public analysis, low-risk code in a sandbox. | Reviewer name, date, issues found. |
| H3 | Specialist review (4-eyes) | A named specialist in the relevant discipline reviews. No self-approval. | Accessibility, security, legal, statistical or clinical content; production code. | Specialist sign-off + test results. |
| H4 | Multi-specialist review (6-eyes) | Two or more specialisms plus the accountable owner. Independent challenge required. | Public-facing decisions, casework support, anything affecting entitlements. | Panel record, dissent captured, residual risk stated. |
| H5 | Governance board and publication | Formal board approval, published transparency record and monitored deployment. | Automated or semi-automated decisions about people; national services. | ATRS record, DPIA, EQIA, monitoring plan, review date. |
Reviewer checklist
Always check
- Every citation opens, exists and says what the text claims
- Numbers, dates, thresholds and legal references independently verified
- Nothing asserts compliance that has not actually been tested
- No personal or sensitive data was entered into an unapproved tool
- Accessibility and plain-language requirements met, not assumed
- Assumptions, gaps and residual risk stated explicitly
- Reviewer is independent of the author at H3 and above
Common failure patterns
- • Confident fabrication — plausible citations that do not exist
- • Stale authority — quoting withdrawn or superseded guidance
- • Compliance theatre — a checklist ticked without a test run
- • Silent scope creep — an agent doing more than was authorised
- • Automation bias — reviewers rubber-stamping fluent output
- • Averaged users — outputs tuned to a majority that excludes minorities
- • Evidence gaps — no record of who checked what, or when
Accountability at a glance (RACI)
| Activity | Responsible | Accountable | Consulted | Informed |
|---|---|---|---|---|
| Classifying risk and review level | Author | Service owner | Specialist reviewers | Delivery team |
| Verifying sources and outputs | Author | Author | Peer reviewer | Service owner |
| Specialist sign-off (H3+) | Named specialist | Service owner | Author | Assurance board |
| Equality and accessibility screening | Author | Service owner | Equality & accessibility leads | Governance board |
| Data protection and lawful basis | Product owner | Data controller / SIRO | DPO | ICO where required |
| Publishing a transparency record | Service owner | SRO | Comms, legal | Public |
| Incident and near-miss reporting | Anyone who spots it | Service owner | Security, DPO | Assurance board |
Metadata catalogue
Treat skills, agents and code as assets in their own right
Every reusable AI capability should be discoverable, accountable, interoperable and governed like any other public asset — with an owner, a version, a risk rating and a retirement plan.
Catalogue fields
| Field | Why it matters |
|---|---|
asset_id | Stable identifier so evidence, incidents and dependencies can point at it. |
owner | A named person, not a team inbox. Accountability needs a face. |
status | draft / approved / restricted / deprecated / withdrawn. |
version | Semantic versioning; breaking changes force re-review. |
authority_tier | 0–3 tier of the sources the asset relies on. |
risk_level / human_review | H0–H5 floor that consumers must apply. |
source_provenance | Which guidance, data and code it derives from, with dates. |
permissions | Least-privilege scopes, tools and credentials the asset may use. |
prohibited_actions | Explicit red lines an agent must never cross. |
deia_screen | Date and outcome of equality and accessibility screening. |
evaluation | Test suite, pass thresholds and last run. |
compatibility | Models, runtimes and versions it is validated against. |
lifecycle | Review cadence, next review, deprecation and retirement date. |
contact / redress | How to report a problem or challenge an outcome. |
Example skill asset metadata
asset_type: ai_skill
asset_id: review-accessibility-govuk
title: GOV.UK accessibility review assistant
status: approved
version: 2.1.0
owner: a.patel@example.gov.uk
authority_tier: 1
human_review: H3
specialist_review: [accessibility, content_design]
self_approval: false
source_provenance:
- id: wcag22
url: https://www.w3.org/TR/WCAG22/
checked: 2026-07-14
next_check: 2026-10-14
- id: govuk-design-system
url: https://design-system.service.gov.uk/
checked: 2026-07-14
permissions:
tools: [read_page, run_axe]
data_classification_max: OFFICIAL
prohibited_actions:
- claiming_conformance_without_test_evidence
- processing_personal_data
- autonomous_publication
deia_screen:
completed: 2026-06-02
outcome: mitigations_applied
evaluation:
suite: a11y-regression-v4
threshold: 0.95
last_run: 2026-08-01
compatibility:
models: [">=v3.1"]
runtimes: [node20, python3.12]
evidence_rules:
require_citations: true
require_reviewer_signoff: true
retain_for: P7Y
lifecycle:
review_cadence: P3M
next_review: 2026-11-01
deprecation: null
contact: ai-assurance@example.gov.ukInteroperability note: map these fields to DCAT 3 (opens an external website) for cataloguing, PROV-O (opens an external website) for evidence chains and CycloneDX ML-BOM (opens an external website) for supply chain, so registries in different organisations can federate rather than fork.
UCD, DEIA and accessibility
Designed for real people, varied needs and accountable teams
Inclusion is a delivery activity, not a compliance report. This section states what we commit to, how we test it, and how you can hold us to it.
Design and research practice
Start with user needs
Every asset traces to a named user need and a measurable outcome. If we cannot name the need, we do not build the feature.
Research with the full range of users
Recruit disabled participants, assistive technology users, people with low digital confidence and people in vulnerable circumstances — in every round, not a one-off "accessibility round".
Test with assistive technology
Manual testing with screen readers, magnification, voice control, switch access and keyboard-only. Automated tools catch a minority of real barriers.
Design for the whole journey
Including offline, assisted digital, third-party support, and what happens when the AI is unavailable or wrong.
Write for reading age 9
Plain English, short sentences, no unexplained acronyms, inclusive terminology, and alternative formats on request.
Measure exclusion, not just satisfaction
Track drop-out by channel and need, assisted digital volumes, complaint themes and accessibility issue ageing — and publish them.
Accessibility commitments for this portal
What we build in
- Semantic HTML, landmarks and a logical heading order
- Full keyboard operation with a visible, high-contrast focus indicator
- Text contrast of at least 4.5:1 and 3:1 for interface components
- Light, dark and high-contrast themes, plus a larger-text setting that persists
- Respect for
prefers-reduced-motionand no motion-only meaning - Responsive to 320 CSS px and to 400% zoom without loss of content
- Status messages announced through ARIA live regions
- No colour-only meaning; icons always paired with text
- Descriptive link text that makes sense out of context
- A print stylesheet that expands links and collapsible content
How we test and report
- Automated checks in the pipeline on every change
- Manual keyboard and screen reader testing before release
- Periodic testing with disabled participants
- A published accessibility statement listing known gaps and fix dates
- A named contact and a 5-working-day response commitment
- Escalation route to the Equality Advisory and Support Service
Accessibility statement guidance → (opens an external website)
DEIA in AI specifically
Known equity risks of AI-assisted delivery. Performance that degrades for under-represented groups; language models that default to majority dialects and names; outputs written above the reading age of the audience; automation that removes the human route people in crisis rely on; accessibility regressions in AI-generated code and content; and reviewers who trust fluent text more than accurate text.
What we do about them. Screen every change with the DEIA tool; disaggregate evaluation by affected group where lawful and proportionate; keep a human route that cannot be removed by efficiency targets; test AI-generated interfaces with assistive technology before merge; publish limitations honestly; and give people a real way to challenge an outcome.
Code of practice and conduct
Responsible use of AI assistive technology
This code complements behavioural codes of conduct and the Civil Service values. It defines operational rules for model-backed tools, direct AI use, coding assistants, multi-agent orchestration and AI-generated artefacts.
Core duties
- Stay accountable. You own every output you use, send or publish. "The AI wrote it" is never a defence.
- Disclose material assistance. If AI shaped the substance, structure, analysis or code, say so.
- Verify before you rely. Open every citation. Recalculate every figure. Test every claim of compliance.
- Protect data. Only approved tools, only permitted classifications, only the minimum necessary.
- Cite sources. Traceable to a tiered, dated, authoritative source — not to the model.
- Preserve evidence. Keep the record for as long as the retention schedule requires.
- Work within competence. AI does not qualify you to make specialist judgements.
- Escalate uncertainty. Raising a doubt early is always cheaper than defending a mistake late.
Prohibited behaviours
- • Entering unauthorised personal, special category, commercially confidential or classified data into any tool
- • Bypassing, weakening or "temporarily" disabling a review control
- • Self-approving material or high-risk work
- • Concealing AI involvement where it was material
- • Presenting unverified output as assured, tested or compliant
- • Using AI to generate evidence, sign-offs, approvals or audit records that did not happen
- • Granting an agent broader permissions than the person operating it holds
- • Using AI to profile, rank or make decisions about people without an approved lawful basis and published record
- • Copying licensed or copyright material into outputs without permission and attribution
- • Ignoring a known defect because a deadline is closer than the consequence
Multi-agent and multi-skill rules
- One accountable orchestrator per task, with a named human owner.
- Least-privilege tools and credentials, time-boxed and revocable.
- Explicit, pinned skill and model versions — no silent upgrades in a governed workflow.
- Separation of duties: the agent that produces must not be the agent that approves.
- Declared prohibited actions and a tested kill switch with a named owner.
- Full step-level logging: which skill, which model, which source, which permission, which human.
- Untrusted content is data, never instruction. Treat retrieved web, document and email content as hostile by default.
- Bounded autonomy: agents propose irreversible actions; humans commit them.
Data, privacy and security rules
- • Establish and record the lawful basis before processing personal data. Complete a DPIA where required.
- • Minimise: use references, synthetic or redacted examples instead of real records wherever possible.
- • Never let an AI tool retain data for training unless that has been assured and contracted.
- • Treat AI interaction logs as records: classify, retain, and prepare for FOI and subject access requests.
- • Apply Secure by Design activities to AI components as to any other system component.
- • Threat-model for prompt injection, data poisoning, model extraction and excessive agency.
- • Report incidents and near-misses through the normal security and data protection routes.
AI-generated code and infrastructure
- Treat generated code as untrusted third-party contribution: review, test, scan and attribute it.
- No merge without a human reviewer who understands the code and can maintain it.
- Check licences and provenance of anything that looks copied.
- Run dependency, secret and SAST scanning; record the results in the evidence pack.
- Accessibility and performance tests apply equally to generated interfaces.
- Record the model, prompt pattern and version in the commit or pull request.
Speaking up, redress and no-blame reporting
Anyone may pause an AI-assisted process they believe is unsafe, unfair or unlawful, without needing permission and without detriment. Near-misses should be reported as readily as incidents — they are the cheapest learning available.
Members of the public affected by an AI-assisted outcome must be able to find out that AI was involved, understand the outcome in plain English, request a human review, and escalate to an independent route. If any of those four is missing, the service is not ready.
Resource library
Guidance, standards, toolkits and solutions — indexed and tiered
68 curated sources, each with its authority tier and a note on what it is actually useful for. Search, filter, then follow the link to the source of truth.
Searches titles, publishers, topics and descriptions.
Showing 68 of 68 resources.
| Resource | Publisher | Type | Tier | Use it for |
|---|---|---|---|---|
| Generative AI Framework for HMG (opens an external website) | DSIT / GDS | Policy | Tier 1 | Baseline principles and practical guardrails for building generative AI in government. |
| AI Playbook for the UK Government (opens an external website) | DSIT | Policy | Tier 1 | Ten principles plus per-technology guidance for safe, effective public sector AI adoption. |
| Algorithmic Transparency Recording Standard (ATRS) (opens an external website) | DSIT / CDDO | Standard | Tier 1 | Mandatory-style template for publishing how an algorithmic tool is used in decision-making. |
| Data Ethics Framework (opens an external website) | DSIT | Policy | Tier 1 | Structured ethical appraisal for public sector data and AI projects. |
| Service Standard (14 points) (opens an external website) | GDS | Standard | Tier 1 | The assessment backbone for any public service — align AI features to each point. |
| Government Service Manual (opens an external website) | GDS | Guidance | Tier 1 | End-to-end practice guidance for user research, design, technology and operations. |
| Government Design Principles (opens an external website) | GDS | Guidance | Tier 1 | Ten principles — start with user needs, do less, design with data, do the hard work to make it simple. |
| Technology Code of Practice (opens an external website) | CDDO | Standard | Tier 1 | Criteria for technology spend approval — interoperability, open standards, security, sustainability. |
| Digital, Data and Technology Capability Framework (opens an external website) | GDS | Toolkit | Tier 1 | Map AI-related skills to recognised roles and progression levels. |
| Open source guidance (coding in the open) (opens an external website) | GDS | Guidance | Tier 1 | When code should be open or closed, and how to publish safely. |
| GDS API technical and data standards (opens an external website) | GDS | Standard | Tier 1 | How to design, document and version APIs that agents and skills can call. |
| WCAG 2.2 (opens an external website) | W3C | Standard | Tier 1 | The normative success criteria — target AA as a minimum, AAA where feasible. |
| ARIA Authoring Practices Guide (APG) (opens an external website) | W3C WAI | Guidance | Tier 1 | Accessible patterns for tabs, accordions, comboboxes, dialogs and more. |
| EN 301 549 accessibility requirements (opens an external website) | ETSI/CEN/CENELEC | Standard | Tier 1 | The procurement-facing accessibility standard referenced by UK/EU regulations. |
| Accessibility requirements for public sector websites and apps (opens an external website) | GOV.UK | Guidance | Tier 1 | What the 2018 regulations require, including the accessibility statement duty. |
| Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018 (opens an external website) | legislation.gov.uk | Law | Tier 0 | The underlying legal duty. |
| Equality Act 2010 (opens an external website) | legislation.gov.uk | Law | Tier 0 | Protected characteristics, reasonable adjustments and discrimination duties. |
| Public Sector Equality Duty guidance (opens an external website) | EHRC | Guidance | Tier 1 | Due regard duty — evidence it in every AI-assisted decision or service change. |
| Make your service accessible: an introduction (opens an external website) | GDS | Guidance | Tier 1 | Practical starting point for teams new to accessibility duties. |
| Publish an accessibility statement (opens an external website) | GOV.UK | Toolkit | Tier 1 | Model wording and required disclosures for non-accessible content. |
| Research with people with disabilities and access needs (opens an external website) | GDS | Guidance | Tier 1 | How to recruit and run inclusive sessions, including with assistive technology users. |
| Helping people to use your service (assisted digital) (opens an external website) | GDS | Guidance | Tier 1 | Assisted digital support and digital inclusion planning. |
| Inclusive Design Principles (opens an external website) | Paciello/TPGi et al. | Guidance | Tier 2 | Seven principles: provide comparable experience, be consistent, give control, offer choice. |
| Inclusive Design Toolkit (opens an external website) | Microsoft | Toolkit | Tier 3 | Persona spectrum and exclusion-mapping activities for workshops. |
| Home Office accessibility "dos and don’ts" posters (opens an external website) | UK Home Office | Toolkit | Tier 1 | Print-ready posters for autistic users, screen readers, dyslexia, low vision, D/deaf users. |
| GOV.UK Design System (opens an external website) | GDS | Toolkit | Tier 1 | Tested, accessible components and patterns — reuse before you invent. |
| GOV.UK Prototype Kit (opens an external website) | GDS | Toolkit | Tier 1 | Rapid, realistic prototypes for usability testing without production risk. |
| GOV.UK style guide (content design) (opens an external website) | GDS | Guidance | Tier 1 | Plain English, reading age, formatting and inclusive terminology. |
| WebAIM contrast checker (opens an external website) | WebAIM | Tool | Tier 3 | Verify 4.5:1 text and 3:1 non-text contrast before shipping. |
| Pa11y automated accessibility testing (opens an external website) | Pa11y | Tool | Tier 3 | Add automated WCAG checks to your pipeline — never as a substitute for manual testing. |
| axe DevTools (opens an external website) | Deque | Tool | Tier 3 | Browser-based rule engine used widely for triage. |
| Guidelines for secure AI system development (opens an external website) | NCSC + international partners | Guidance | Tier 1 | Secure design, development, deployment and operation across the AI lifecycle. |
| Secure by Design (UK government) (opens an external website) | Cabinet Office / GSG | Standard | Tier 1 | Mandated activities and artefacts for digital service security in HMG. |
| Cyber Assessment Framework (CAF) (opens an external website) | NCSC | Standard | Tier 1 | Outcome-based objectives A–D for essential services and government systems. |
| Post-quantum cryptography guidance and migration timeline (opens an external website) | NCSC | Guidance | Tier 1 | Discovery by 2028, high-priority migration by 2031, complete by 2035. |
| Post-Quantum Cryptography standards (FIPS 203/204/205) (opens an external website) | NIST | Standard | Tier 1 | ML-KEM, ML-DSA and SLH-DSA — the algorithms your crypto inventory should target. |
| OWASP Top 10 for LLM Applications (opens an external website) | OWASP | Standard | Tier 2 | Prompt injection, insecure output handling, supply chain and agency risks. |
| MITRE ATLAS adversarial ML knowledge base (opens an external website) | MITRE | Toolkit | Tier 2 | Tactics and techniques for threat modelling AI systems. |
| Secure Software Development Framework (SP 800-218) (opens an external website) | NIST | Standard | Tier 2 | Baseline secure SDLC practices to bind AI-generated code to. |
| SLSA supply chain integrity framework (opens an external website) | OpenSSF | Standard | Tier 2 | Provenance levels for build artefacts — extend to model and skill artefacts. |
| CycloneDX ML-BOM / AI-BOM (opens an external website) | OWASP CycloneDX | Standard | Tier 2 | Machine-readable bill of materials for models, datasets and AI components. |
| Guidance on AI and data protection (opens an external website) | ICO | Guidance | Tier 1 | Lawful basis, fairness, transparency and accountability for AI processing. |
| AI and data protection risk toolkit (opens an external website) | ICO | Toolkit | Tier 1 | Risk statements and controls mapped to the AI lifecycle. |
| Data protection impact assessments (DPIA) (opens an external website) | ICO | Toolkit | Tier 1 | When a DPIA is mandatory and what a defensible one contains. |
| Data Protection Act 2018 (opens an external website) | legislation.gov.uk | Law | Tier 0 | UK GDPR implementation, including automated decision-making safeguards. |
| ISO/IEC 42001 AI management systems (opens an external website) | ISO/IEC | Standard | Tier 1 | Certifiable management system for responsible AI — useful for supplier assurance. |
| ISO/IEC 23894 AI risk management (opens an external website) | ISO/IEC | Standard | Tier 1 | AI-specific application of ISO 31000 risk management. |
| ISO/IEC 27001 information security management (opens an external website) | ISO/IEC | Standard | Tier 1 | Control baseline most suppliers will already hold. |
| AI Risk Management Framework (AI RMF 1.0) + GenAI Profile (opens an external website) | NIST | Standard | Tier 1 | Govern, Map, Measure, Manage — pairs cleanly with UK assurance practice. |
| EU AI Act (Regulation 2024/1689) (opens an external website) | EUR-Lex | Law | Tier 0 | Risk tiers and obligations that may bind UK bodies operating in the EU market. |
| OECD AI Principles (opens an external website) | OECD | Policy | Tier 2 | Internationally agreed values-based principles for trustworthy AI. |
| Recommendation on the Ethics of AI (opens an external website) | UNESCO | Policy | Tier 2 | Global normative instrument covering human rights and inclusion. |
| Understanding AI ethics and safety (opens an external website) | The Alan Turing Institute | Guidance | Tier 2 | The SUM values and FAST Track principles used across UK public sector. |
| AI Standards Hub (opens an external website) | BSI / NPL / Turing | Toolkit | Tier 2 | Searchable database of AI standards in development and published. |
| Ada Lovelace Institute research (opens an external website) | Ada Lovelace Institute | Research | Tier 3 | Public attitudes, algorithmic impact assessment and deliberative methods. |
| Open Data Institute (opens an external website) | ODI | Research | Tier 3 | Data ethics canvas and data institution patterns. |
| Model Cards for Model Reporting (opens an external website) | Mitchell et al. | Research | Tier 2 | The reference format for documenting intended use and performance by group. |
| Datasheets for Datasets (opens an external website) | Gebru et al. | Research | Tier 2 | Provenance, composition and collection questions for training/eval data. |
| PROV-O provenance ontology (opens an external website) | W3C | Standard | Tier 1 | Entity–activity–agent model for machine-readable evidence chains. |
| DCAT 3 data catalogue vocabulary (opens an external website) | W3C | Standard | Tier 1 | Describe datasets and services so registries can federate. |
| C2PA content provenance and authenticity (opens an external website) | C2PA | Standard | Tier 2 | Cryptographic manifests for AI-generated or edited media. |
| Model Context Protocol (opens an external website) | MCP | Standard | Tier 3 | Open protocol for connecting assistants to tools and data with explicit scopes. |
| Model cards on Hugging Face Hub (opens an external website) | Hugging Face | Toolkit | Tier 3 | Practical templates and YAML front-matter conventions. |
| alphagov on GitHub (opens an external website) | GDS | Toolkit | Tier 1 | Reference implementations, frontend, and publishing platform source. |
| x-govuk community tools (opens an external website) | Cross-government community | Toolkit | Tier 2 | Community-built plugins, design tooling and prototypes. |
| Open Government Licence v3.0 (opens an external website) | The National Archives | Law | Tier 0 | Default licence for publishing public sector content and guidance. |
| Greening Government ICT and Digital Services Strategy (opens an external website) | Defra / GGC | Policy | Tier 1 | Carbon, circular-economy and reporting expectations for digital estates. |
| Web Sustainability Guidelines (WSG) (opens an external website) | W3C Community Group | Guidance | Tier 3 | Practical efficiency criteria for pages, media, hosting and AI workloads. |
Links were verified on 2026-08-08. Always confirm you are reading the current version at the source — guidance changes without notice.
Trust and transparency
How to check, challenge and contribute
A guidance utility that cannot be questioned is not trustworthy. Here is how this one is governed, and how you hold it to account.
Provenance of this content
Every substantive claim links to a tiered source. Where this portal expresses an opinion or a house rule rather than a legal requirement, it says so.
Ownership and review
Owner: Public AI Utility Toolkit working group
Version: 2.0.0
Last reviewed: 2026-08-08
Next review due: 2026-11-08
Licence and reuse
Open Government Licence v3.0 (content) / MIT (code). Fork it, adapt it for your organisation, and tell us what you changed so others benefit.
Data and privacy
This site is static and client-side. It sets no tracking cookies, runs no analytics that identify you, and sends nothing you type in the tools to any server. Preferences are stored locally in your browser and you can clear them at any time.
Known limitations
This is a concept portal, not a statutory instrument. It does not replace your organisation's policies, your DPO, your security team or your legal advice. Some linked standards sit behind paywalls.
Report a problem
Tell us about a broken link, an out-of-date claim, an accessibility barrier or a gap. Accessibility issues get a response within five working days.
Changelog
| Version | Date | Change |
|---|---|---|
| 2.0.0 | 2026-08-08 | Added task-first entry points, six interactive tools, a tiered resource library, H0–H5 review table, RACI, transparency and redress commitments, MoSCoW roadmap, glossary and FAQ. Added dark, high-contrast and large-text modes, site search, on-page navigation and a print stylesheet. |
| 1.0.0 | Initial | Concept portal: mission, knowledge corpus, toolkit, assurance workflow, catalogue, UCD and code of practice. |
Gaps and roadmap
Must have Must have Must have Must have Must have Must have Must have Should have Should have Should have Should have Should have Should have Should have Could have Could have Could have Could have Could have Won't have (this time) Won't have (this time) Won't have (this time) Won't have (this time)
What is missing, prioritised with MoSCoW
An honest backlog is a trust signal. These are the gaps identified in this portal and in most public sector AI guidance, ordered by whether they are essential, valuable, desirable or deliberately out of scope.
Named accountable owner and published contact route
Nothing on this page is credible without a person who owns it and answers questions.
Accessibility statement, testing evidence and feedback route
Legally required for UK public sector sites; also the fastest trust signal.
Version, last-reviewed and next-review dates on every artefact
Freshness is the main failure mode of guidance portals.
Machine-readable metadata schema published openly
Without a schema, registries cannot federate and evidence cannot be automated.
Risk-based human review model with no self-approval
The core control that keeps accountability with people.
Source citation and provenance for every claim
Prevents hallucination becoming policy.
DEIA screening built into the default workflow
Equality duty is a due-regard duty — it must happen before decisions, not after.
Search across the whole corpus with filters and permalinks
Findability is the top usability complaint for guidance sites.
Role-based entry points and task-first navigation
Users arrive with a task, not an interest in your information architecture.
Downloadable, offline and printable formats
Supports assisted digital, low bandwidth and audit contexts.
Worked examples and anti-patterns for each control
Abstract principles do not change behaviour; examples do.
Change log and subscribe-to-updates
Lets teams re-check work when guidance changes.
Multilingual and alternative-format provision (Welsh, Easy Read, BSL)
Statutory in some contexts and essential for inclusion in all.
Automated link-health and freshness checking
Broken authority links silently destroy trust.
API and JSON export of the registry and corpus
Enables reuse by other departments and tooling.
Maturity self-assessment with benchmarking
Helps organisations see where they are without a consultant.
Community contribution workflow with moderation
Scales the corpus beyond a single team.
Interactive decision tree for tool selection
Reduces the "which tool may I use?" support burden.
Carbon and cost telemetry per model call
Makes proportionality measurable rather than rhetorical.
Automated approval of any high-risk output
Out of scope by design — approval stays human.
Storing personal or official-sensitive data in this portal
The portal is public and stateless; sensitive records belong in assured systems.
Vendor-specific lock-in to a single model provider
Conflicts with interoperability and exit obligations.
Replacing professional assurance roles
The toolkit supports specialists; it does not substitute for them.
Reference
Glossary and frequently asked questions
Glossary
- Agent
- Software that plans and acts across multiple steps using tools, within explicit permissions and bounds.
- Autonomy level
- How far an agent may act without a human decision, from suggest-only to bounded execution.
- Assisted digital
- Support for people who cannot use a digital service independently.
- ATRS
- Algorithmic Transparency Recording Standard — the UK format for publishing algorithmic tool use.
- Conformance pack
- A bundle of automated checks that tests an artefact against a named standard.
- Crypto-agility
- The ability to change cryptographic algorithms without redesigning the system.
- DEIA
- Diversity, equity, inclusion and accessibility.
- DPIA
- Data protection impact assessment, required for high-risk processing.
- Drift
- Divergence between a cited source, a model version or a control and what is actually in use.
- EQIA
- Equality impact assessment evidencing the public sector equality duty.
- Evidence pack
- The bundle of records proving how an AI-assisted output was produced, checked and approved.
- Harvest now, decrypt later
- Capturing encrypted data today to decrypt once quantum computers are capable.
- Human in the loop
- A person who must act before an output takes effect.
- Human on the loop
- A person who monitors and can intervene in an otherwise automated process.
- Least privilege
- Granting only the minimum permissions needed for the shortest necessary time.
- Material AI assistance
- AI involvement that a reasonable reader would want disclosed because it shaped the output.
- Prompt injection
- Untrusted content that manipulates a model into unintended actions.
- Provenance
- The verifiable record of where content, data or code came from and how it changed.
- PQC
- Post-quantum cryptography — algorithms resistant to quantum attack.
- Skill
- A packaged, versioned capability an assistant or agent can invoke, with defined inputs and limits.
- Source tiering
- Ranking sources by authority so conflicts resolve predictably.
- 4-eyes / 6-eyes
- Two or three independent people involved in producing and approving an output.
Frequently asked questions
Do I have to declare every use of AI?
No. Declare material assistance — where AI shaped the substance, structure, analysis or code of an output. Trivial uses such as spell-checking do not need a declaration, but your local policy may set a lower threshold.
Can AI approve my work?
No. AI can suggest, draft, check and explain. Approval is a human act by someone with the authority to make that decision. AI never grants authority a user does not already hold.
What if the AI cites a source that does not exist?
Treat it as an incident. Do not publish. Verify every citation against the original, log the failure in the drift and incident register, and check whether the same prompt pattern has been used elsewhere.
Is this a substitute for a DPIA, EQIA or security assurance?
No. This toolkit helps you prepare and evidence those assessments; it does not replace them or the professionals accountable for them.
Can I paste official-sensitive information into a general assistant?
Not unless the tool is explicitly approved for that classification and the data-handling terms have been assured. When in doubt, do not paste — ask your information asset owner.
How do I handle AI-generated code?
Treat it as untrusted third-party code: review it, test it, scan dependencies, record provenance and never merge without a human reviewer who understands it.
What accessibility duties apply to AI-generated content?
The same ones that apply to everything else. WCAG 2.2 AA, plain language, alternative formats and the accessibility statement duty all apply regardless of who or what drafted the content.
Who do I contact if something goes wrong?
Your service owner and the named AI assurance contact for your organisation. Report near-misses too — they are the cheapest form of learning.
Contact, feedback and support
Bug
Accessibility
Feature
Correction
Security
Discussion
Tell us what is wrong, missing or unclear
Every route below is public and auditable except the direct message form. Choose the one that fits — if you are not sure, raise an issue and we will redirect it.
Something is broken
Broken links, tools that do not work, layout failures, incorrect calculations or console errors.
An accessibility barrier
Anything that stops you using this site with a screen reader, keyboard, magnification, voice control or any other assistive technology. These are treated as priority defects.
Target first response: 5 working days.
A feature or resource request
A tool you need, a section that is missing, or an authoritative source that should be in the library.
Content is wrong or out of date
Superseded guidance, a withdrawn standard, a mis-stated legal duty or a dead authority link. Include the source that proves it.
A security vulnerability
Do not open a public issue. Use private disclosure so we can fix it before it is known. See our security policy for scope and timescales.
A question or an idea
Not sure it is a defect? Want to talk about approach, reuse this in your organisation, or contribute? Start there.
Message the team directly
Use this if your message should not be public — for example it concerns a specific service, a supplier, or information you would not want indexed. It is not a route for personal data or anything above OFFICIAL.
Choose Prepare and send message to compose this in your own email client, or Copy message instead if you would rather paste it somewhere. Nothing is transmitted from this page.
What happens to your message
How it is handled
- • Nothing is transmitted from this page. The form builds a message and hands it to your own email client, or to GitHub, so you stay in control of what is sent.
- • GitHub issues are public. Do not put personal or sensitive information in them.
- • Direct messages go to the team mailbox and are handled under the host organisation's retention schedule.
- • Accessibility reports are logged as defects with a target first response of five working days.
- • Security reports follow coordinated disclosure and are never opened as public issues.
If we cannot resolve it
- • Ask for the decision to be reviewed by the accountable owner named in the transparency section.
- • For accessibility complaints in the UK, you can escalate to the Equality Advisory and Support Service.
- • For data protection concerns, contact the host organisation's data protection officer, then the Information Commissioner's Office.
- • All correspondence about this repository is capable of being disclosed under freedom of information law.
Make guidance usable, testable and accountable.
Start with the corpus, encode the metadata, publish the skills, bound the agents, enforce the review — and keep improving it in the open.