The Tool Desk
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1. Define the system’s purpose, use and accountable parties
Start with the public task the AI is intended to support and the real-world decisions or services it may affect. A system that helps staff search documents presents different risks from one whose output influences eligibility, enforcement, inspections, benefit prioritisation, education, health, housing or another consequential service.
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Write down the intended use and who may be affected
- Describe the task, users, setting and decisions that may rely on the output.
- Identify the people and groups who may be affected, including those who could face an adverse outcome.
- State whether the output is advisory, can shape a human decision, or can directly trigger an action.
- List foreseeable uses outside the intended scope and specify when staff must not rely on the system.
Assign roles before signing a contract
Name the agency owner, system provider, deployer, vendors and any relevant data controller, processor or subprocessor. Do not assume the agency is only a deployer: the European Commission’s Article 50 transparency FAQ notes that a public authority may itself be a provider when it develops a system, has one developed, and places it on the market or puts it into service under its own name. The role can affect which obligations apply.
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Because duties vary by place and use, have counsel or the responsible privacy and governance teams check the rules that apply to the agency and the intended deployment before procurement and again before launch.
2. Map the data and decide how it will be protected
Document the information the system collects, accesses, infers, generates, receives from staff or other systems, shares with vendors, retains and deletes. Include data used in prompts, retrieval, training, fine-tuning, testing and evaluation—not just information in the final output.
Record the data lifecycle
- Source and purpose: where each data category comes from and why it is needed for the public task.
- Legal basis and sensitivity: the applicable authority for processing and whether the information is sensitive or otherwise subject to additional safeguards.
- Flow and access: who can see the data, including vendor staff and subprocessors, and whether it is transferred across borders.
- Quality and retention: how data accuracy will be checked, how long it is kept, and how deletion works in agency and vendor systems.
- Security and incidents: technical and organisational controls, access permissions, logging, and the process for responding to a breach or other incident.
- People’s rights: how applicable requests to access, correct, object to, delete or otherwise exercise rights over personal data will be received and handled.
Require the vendor to explain its data use and controls in terms the agency can verify. Contract terms should address data handling, retention, deletion, security, subprocessors, incident support, change notification and the agency’s ability to investigate or end the service.
3. Decide whether a data protection impact assessment is required
For processing governed by the GDPR, the controller must complete a data protection impact assessment (DPIA) before processing that is likely to result in a high risk to people’s rights and freedoms. The European Data Protection Board’s DPIA guidance explains that if the assessment indicates high residual risk despite planned safeguards, the controller must consult the competent supervisory authority before proceeding.
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Make the decision for the actual processing
Assess the purpose, data, people affected, scale, likely consequences and safeguards of the proposed deployment. Check the relevant supervisory authority’s lists of processing that is likely to require or not require a DPIA, where available. Record why a DPIA is or is not required; do not treat buying a vendor product or completing a general AI review as a substitute for that decision.
A DPIA is a GDPR-specific duty. Agencies outside GDPR’s scope should check their own privacy law and assessment requirements rather than assuming the same trigger applies.
4. Check whether a fundamental-rights impact assessment is required
A covered high-risk AI deployment may trigger a separate fundamental-rights impact assessment (FRIA) under the EU AI Act. The requirement applies to specified deployments by public bodies and certain providers of public services; it does not mean every government use of AI automatically requires a FRIA. Determine whether the system is high-risk, whether the agency or service falls within the relevant coverage, and what duties attach to the agency’s role.
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Assess affected people, risks and safeguards
The European Commission’s AI Act Recital 96 describes the FRIA as a prior assessment of risks to affected individuals or groups and measures to take if those risks materialise. Representatives of affected people, independent experts or civil-society organisations may be involved to help identify impacts. The assessment should connect identified risks to concrete mitigations, responsible owners and response actions.
Coordinate a FRIA with a DPIA without treating them as interchangeable
Where both assessments apply, the Commission’s “Navigating the AI Act” guidance says they should be conducted in conjunction so relevant analysis can be reused and substantive overlap reduced. Cross-reference shared evidence, but verify that each assessment’s required topics are addressed: completing a DPIA alone does not automatically fulfil every FRIA obligation.
5. Decide what people must be told
Transparency involves both legal notices and an explanation people can use. Identify who needs to be told, at what point, what the AI does in the service, whether its output informs a decision, and how to ask questions or challenge an outcome. Also check national or local rules on public records, administrative procedure, accessibility, notices and automated decisions; the EU material does not resolve those requirements for every jurisdiction.
Apply the EU AI Act’s Article 50 rules where relevant
As of 2 August 2026, Article 50 transparency obligations apply, according to the European Commission’s guidance published on 20 July 2026. The rules include disclosures for specified direct interactions with AI and specified exposures to emotion-recognition or biometric-categorisation systems. They also cover marking deepfakes and certain AI-generated text on matters of public interest when there has been no human review or editorial control. The exact trigger, responsible party and any exception depend on the system and circumstances, so check the applicable legal text and current guidance for the particular use.
Do not treat a generic “AI is used” notice as a complete answer to every disclosure duty. Work out which specific interaction, system or content is covered and make the notice accessible to the people who need it.
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6. Test performance, limitations and human oversight
Before release, the agency should have evidence that the system is suitable for its intended context and a plan for what happens when it is wrong, uncertain or used outside its limits. The European Commission’s public-sector guidance highlights bias, testing and validation, staff skills, transparency and trust as important concerns when integrating AI.
Validate for the agency’s real cases
- Document test methods, representative test cases and the populations or circumstances represented.
- Measure relevant error types and examine data-quality problems, bias and possible unequal effects.
- Record known limitations, failure modes and circumstances in which outputs should not be relied upon.
- Set acceptance criteria and identify who can approve, restrict or stop use if results fall short.
Make review and challenge operational
Specify when a qualified person must review an output, what that person can see, and whether they have authority to override it. Give staff an escalation route for suspected harm or unreliable results. Define how affected people can seek correction, review or appeal where applicable, and who handles those requests. Establish criteria for pausing the system, investigating a problem and restarting only after it is addressed.
7. Secure procurement terms and an evidence trail
Procurement should let the agency examine and govern the system throughout its use, not just accept a vendor’s assurances at purchase. Compare options by the data they require, retention and deletion controls, vendor and subprocessor access, documented performance and limitations, logging and auditability, human intervention features, accessibility, security, incident support, change controls and practical exit options. These are assessment criteria, not a ranking of particular products.
Keep an internal record of the purpose and scope, accountable owner, provider and vendors, data flows, assessments, validation results, known limitations, oversight plan, complaints or incidents, and review dates. A public-facing explanation of the system’s purpose, data use, role in decisions, safeguards and routes for questions can make transparency practical, while respecting information that cannot legally be disclosed. The cited EU sources do not establish one universal public AI register that every agency must publish.
8. Monitor and revisit the decision to use AI
Assign a named owner and a monitoring schedule before launch. Reassess when a model or vendor changes, new data is introduced, the deployment context shifts, a new affected group emerges, performance degrades, or material legal guidance changes. The AI Act Recital 96 states that a relevant FRIA should be updated when factors pertinent to the assessment change.
These checks provide a practical starting point, not a substitute for a jurisdiction-specific legal determination. An agency should not proceed until it has established which duties apply to its system and put the required safeguards, notices and accountability arrangements into operation.
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