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Conduct an AI impact assessment (AIA) around the real business process in which a vendor tool will operate—not around the vendor’s generic model description. Before approval, document the intended purpose and decision role; identify affected people and possible harms; trace data, processing roles, vendors, and subprocessors; test the tool with evidence from your context; rate residual risks; put human oversight, complaints, security, and contractual controls in place; and define monitoring and reassessment triggers.
There is no single assessment that every organization must complete. The NIST AI Risk Management Framework (AI RMF) is voluntary guidance. EU AI Act Article 27 creates a specific fundamental-rights assessment duty only for defined deployers and covered high-risk systems. Data-protection obligations and regulator expectations can apply separately. Your assessment should therefore record both the facts of the deployment and the legal regimes that actually apply.
What an AI impact assessment should produce
An AIA is a decision record for one proposed deployment. It should let an approver answer four questions:
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- Who could be affected, including people who never use the tool directly?
- What evidence shows that the tool is accurate, fair, secure, explainable enough, and fit for this use?
- Which safeguards, owners, contracts, and monitoring arrangements make the remaining risk acceptable?
The finished record normally includes the use-case description, data and supply-chain map, affected-group analysis, vendor evidence, local test results, risk register, chosen controls, approvals, contract requirements, and a review schedule. Keep assumptions and unresolved questions visible rather than treating a purchase decision as proof that risks are acceptable.
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An AIA is not a substitute for a privacy or data-protection impact assessment, security review, equality analysis, safety case, or sector-specific approval. Where assessments overlap, cross-reference material instead of copying inconsistent versions.
Step 1: Define the proposed use before evaluating the model
Start with the process, not the product name. Write a plain-language description that another team could use to reproduce the proposed deployment.
Record the process and purpose
- The business or public-service process in which the tool will appear.
- The intended purpose and the problem it is meant to solve.
- The point in the workflow where the tool receives information and returns an output.
- Whether it generates, classifies, predicts, ranks, recommends, summarizes, or makes a decision.
- How often it will run, for how long, and whether it will be used in a pilot, production, or both.
- Which people can see, edit, approve, or act on an output.
- Every decision that may rely on the output, including decisions made indirectly by downstream systems.
Describe human involvement precisely
“Human in the loop” is not a sufficient control description. State what the reviewer sees, what information is available besides the model output, whether the reviewer can reject it, how much time they have, and whether policy or workload makes rejection realistic. Distinguish a meaningful review from a person who merely clicks to confirm an automated recommendation.
Set the boundaries of use
List prohibited uses, approved input types, retention limits, user permissions, and escalation points. A tool approved to draft internal summaries is not automatically approved to rank applicants, determine eligibility, or communicate a final decision. Record the intended period and frequency of use; these details are expressly relevant to covered assessments under EU AI Act Article 27.
Example scope statement
“From 1 March 2027, the service team will use the vendor’s classifier to route incoming support tickets into four queues. It will not deny service, set priority for safety incidents, or send customer-facing decisions. Trained agents will review the category before assignment. The system will process new tickets continuously and retain inputs for 30 days unless a legal hold applies.”
This level of specificity makes later testing and monitoring measurable. It also prevents a vendor’s broad claims about a model from being mistaken for evidence about your particular use.
Step 2: Identify affected people and context-specific harms
Make two lists: people directly subject to an output and people indirectly affected by decisions, resource allocation, communications, or records created by the tool. Include staff who operate or review the system, because workload, deskilling, or pressure to accept outputs can change the risk.
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Build an affected-groups map
- Customers, applicants, patients, students, employees, residents, or other people whose records enter the system.
- People represented in reference data but not directly contacted.
- Groups likely to encounter language, disability, accessibility, connectivity, or documentation barriers.
- People who may have less ability to challenge an error or obtain a human review.
- Third parties whose information appears in submitted documents or messages.
For each group, ask what could go wrong in this process. Potential harms include denial or delay of a service, unequal error rates, exposure of confidential information, reputational damage, unsafe advice, loss of opportunity, excessive surveillance, or inability to understand and contest an outcome. Assess the concrete workflow rather than relying on a statement that the underlying model is “safe” or “unbiased.”
Involve people who can reveal hidden impacts
Speak with process owners, frontline reviewers, security and privacy staff, accessibility specialists, legal counsel, and representatives of affected groups. The EU AI Act’s recital on Article 27 identifies affected-group representatives, independent experts, and civil-society organizations as possible sources of relevant information. Participation should change the risk analysis or controls, not serve as a ceremonial sign-off.
Document contestability
For every consequential output, state how a person can ask for an explanation, correction, or human reconsideration; who receives the request; the response time; and what evidence is retained. If a group cannot realistically use the route—for example, because notices are inaccessible or the process requires information it does not possess—record that as a risk requiring a design change.
Step 3: Map data, roles, and the entire supply chain
Create a data-flow diagram from collection to deletion. Include prompts, uploaded files, metadata, logs, outputs, human annotations, analytics, backups, and support access. Mark where each item is stored and which country or region is involved when that matters to your obligations.
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Assign processing roles for each operation
Determine who decides the purpose and essential means of each processing activity. Depending on the operation, your organization or the vendor may be a controller, processor, or joint controller; a vendor can have different roles for service delivery, fraud prevention, product improvement, and support. Do not assign one label to the whole relationship without examining the actual decisions.
Trace subcontractors and onward transfers
Obtain the vendor’s current list of subprocessors and service dependencies. Ask whether prompts or outputs are used to train or improve a shared model, whether support staff can access content, how data is segregated, and how the vendor handles onward transfers. Identify who can change a subprocessor or model without your approval and how you will be notified.
Put the map into the contract
Written terms should describe processing details, documented instructions, confidentiality, security measures, subprocessors, assistance with rights requests, incident notification, audit or assurance access, and deletion or return of personal information. Specify retention periods and backup deletion, not just “reasonable retention.” Require notice and a decision right for material changes to processing, hosting, model behavior, or subcontractors. The UK Information Commissioner’s Office (ICO) recommends checking periodically that the parties’ actual conduct still matches their agreed responsibilities; build that check into contract management.
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Minimum data questions
- What fields are required, optional, or unnecessary?
- Can identifiers, sensitive attributes, or free text be removed, masked, or kept out of prompts?
- What is the lawful and fair basis for each purpose?
- How are access, export, correction, deletion, and retention requests handled?
- What logs are needed for accountability, and who can read them?
Step 4: Obtain vendor evidence and test fit for purpose
Set an acceptable level of performance before reviewing the vendor’s claims. A single overall accuracy figure rarely answers whether the tool is suitable for your workflow. Define the errors that matter, the groups for which results must be checked, and the point at which use must stop or require extra review.
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Ask for documentation covering:
- Training and evaluation data sources, time periods, geographic coverage, labeling methods, and known gaps.
- Model version, intended operating conditions, constraints, and unsupported uses.
- Evaluation design, sample sizes, baselines, confidence intervals where available, and results by relevant subgroup.
- Known failure modes, hallucination or calibration behavior, and safeguards against unsafe or inappropriate outputs.
- Explainability features, available reason codes, traceability, and the information a reviewer can inspect.
- Bias, fairness, accessibility, and robustness testing, including remediation decisions.
- Security controls, incident history relevant to the service, change-management practices, and notification commitments.
- Customer testing guidance, service-level commitments, and the process for reporting harmful behavior.
The ICO recommends due diligence on accuracy, bias, design trade-offs, and independent evaluation where appropriate. If material bias or discrimination cannot be mitigated for your use, its guidance says not to procure the service or dataset.
Run a local test, not just a document review
- Assemble a lawful, representative test set that reflects your actual language, formats, edge cases, and affected groups. Keep it separate from production data and minimize personal information.
- Define reference answers or decisions using a documented process. If experts disagree, record the adjudication rule rather than hiding disagreement.
- Run the candidate tool and compare results with the reference process and any existing system.
- Report overall and subgroup performance, false positives and false negatives, abstentions, latency, and the severity of each error.
- Have domain experts review harmful, ambiguous, and high-impact examples. Record whether a human could detect and correct the error before it matters.
- Repeat testing after configuration, model, prompt, data, or workflow changes.
Evaluate trade-offs explicitly
Improving one metric can worsen another. A threshold that reduces false negatives may create more false positives; a refusal rule may improve safety while increasing manual workload. Decide whether the trade-off is acceptable for this use, who approved it, and what additional review is required. Do not present a vendor benchmark measured on a different population as your organization’s performance result.
Step 5: Rate risks and select controls
Use a risk register that links each harm to a control and an accountable owner. “Low risk” should be a reasoned conclusion, not a default classification.
| Risk-register field | What to record |
|---|---|
| Risk and affected group | The specific harmful outcome and who may experience it. |
| Cause and evidence | Workflow step, data condition, test result, vendor limitation, or stakeholder report supporting the concern. |
| Likelihood and severity | Your ratings for this deployment, with definitions and assumptions. |
| Existing protections | Controls already operating and their known effectiveness. |
| Additional mitigation | Design, data, access, review, notice, security, or contractual change required. |
| Owner and deadline | The person or function accountable for implementation and verification. |
| Residual-risk decision | Accepted, accepted with conditions, delayed pending evidence, or rejected; include approver and date. |
Controls that commonly fit vendor tools
- Narrow the purpose, eligible users, input fields, or decision scope.
- Minimize data and prevent sensitive or irrelevant content from entering prompts.
- Use role-based access, logging, environment separation, and strong authentication.
- Require pre-deployment and periodic tests, with a release gate for consequential use.
- Keep a trained human reviewer with authority, time, and information to override the output.
- Provide clear notice, an appeal or complaint route, correction procedures, and accessible alternatives.
- Define rate limits, fallback procedures, and an incident-response playbook.
- Block automated action when confidence, data quality, or rule checks fall below a threshold.
For each control, state how it will be tested and what evidence proves it remains in operation. NIST’s AI RMF organizes this work into Govern, Map, Measure, and Manage. It is a useful structure, not a legal safe harbor.
Step 6: Record the decision, contracts, and accountability
Assemble one approval pack rather than scattering evidence across procurement, privacy, security, and product tickets. Include the final use statement, affected-groups analysis, data-flow and role map, vendor evidence, local test report, risk register, control owners, training plan, incident and complaint process, contract deviations, and unresolved issues.
Use a clear decision gate
- Approve: evidence meets the predefined standard, controls are operating, and residual risk is accepted by the authorized owner.
- Approve with conditions: a limited pilot or restricted purpose is allowed while named actions and deadlines remain open.
- Delay: evidence, testing, legal analysis, or contract protections are insufficient.
- Reject: the use is not justified, or material bias, discrimination, security exposure, or other harm cannot be mitigated.
Record who made the decision, their authority, the date, the version of the tool and configuration assessed, and the facts that would invalidate the decision. Procurement approval alone is not accountability for an operational decision.
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Contract provisions to verify
- Purpose, documented instructions, permitted data, retention, deletion, and return.
- Security and confidentiality obligations, including incident timelines and cooperation.
- Subprocessor approval or notice, location changes, and flow-down duties.
- Assistance with access, correction, deletion, objection, and other rights requests.
- Evidence delivery, audit or independent-assurance rights, and remediation commitments.
- Notice of material model, training-data, hosting, or service changes.
- Exit support, export of records, and deletion verification.
Step 7: Monitor performance and reassess
Approval is the start of governance, not the end. Assign a service owner and define what will be measured, how often, and what happens when a threshold is crossed.
Monitor the deployment
- Accuracy and error severity against the approved reference process.
- Performance and error patterns for relevant groups and languages.
- Abstention, override, appeal, complaint, and incident rates.
- Input-data drift, changes in volume or user behavior, and reviewer workload.
- Access violations, unusual exports, security alerts, and vendor notices.
- Whether human reviewers actually perform the required checks.
Define reassessment triggers before go-live
Start a new assessment or update when the vendor changes the model, training practice, hosting, subprocessors, or material functionality; when the organization introduces a new purpose, decision, affected group, or geography; when data changes; when performance drifts; when an incident or credible complaint occurs; when human oversight changes; or when a new legal requirement applies. Under Article 27, a covered deployer must update assessment information when it considers an assessed element changed or no longer current.
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Set response actions
Specify who can pause the tool, switch to a fallback process, notify affected people, preserve evidence, investigate, and communicate with the vendor. A complaint mechanism should route to a person with authority to correct the outcome, not merely to technical support. After an incident, document root cause, affected records or decisions, remediation, and the conditions for resuming use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which framework or legal duty applies?
| Instrument | Legal force and scope | When it matters | What it contributes |
|---|---|---|---|
| NIST AI RMF 1.0 | Voluntary, rights-preserving, non-sector-specific, and use-case agnostic guidance. Published January 26, 2023. | Any organization seeking a structured risk process, unless another rule or contract makes a control mandatory. | Govern, Map, Measure, and Manage lifecycle activities, with flexible terminology and practices. |
| NIST AI RMF Playbook | Voluntary implementation support; the complete version was announced March 30, 2023. | When teams need examples and questions to operationalize the RMF. | Practical prompts for governance, mapping, measurement, and management. The Playbook states: “The AI RMF and the Playbook are intended for voluntary use.” |
| EU AI Act Article 27 | A statutory fundamental-rights assessment duty for specified public bodies, private entities providing public services, and certain deployers of Annex III high-risk systems. Applicability depends on the system and deployment facts. | Before first use of a covered high-risk system and when assessed elements change or become stale. | Requires documenting the process, intended purpose, period and frequency, affected groups, specific risks, human oversight, and measures if risks materialize. Existing data-protection assessment material may be cross-referenced where it covers overlapping duties. |
| UK ICO third-party and contracts guidance | Regulator guidance focused here on data protection, supplier diligence, roles, contracts, and review. The relevant page is under review following changes made by the Data (Use and Access) Act. | When UK data-protection responsibilities or supplier relationships are in scope. | Practical expectations for supply-chain mapping, written terms, due diligence, independent evaluation, and ongoing checks. Confirm the current guidance and applicable law before relying on it as definitive. |
These instruments can be used together, but one does not automatically satisfy another. Identify the organization’s location, sector, role, system classification, data, and affected people before deciding which duties apply.
A pre-deployment checklist
- Approve a precise purpose, workflow, output type, frequency, duration, and prohibited-use list.
- Name affected groups and document specific harms, barriers, and contestability routes.
- Draw the end-to-end data and supply-chain map, including subprocessors and support access.
- Assign controller, processor, or joint-controller roles for each relevant operation.
- Set accuracy, fairness, safety, explainability, and human-review acceptance criteria.
- Obtain vendor evidence and run a representative local test with subgroup and edge-case analysis.
- Record risks, mitigations, owners, deadlines, residual-risk decisions, and escalation rules.
- Complete privacy, security, accessibility, equality, and sector reviews that apply.
- Make contracts reflect processing, security, subprocessors, rights assistance, incidents, changes, audits, and deletion.
- Train users and reviewers; verify that oversight is meaningful in the real workload.
- Approve, condition, delay, or reject the deployment with a named accountable decision-maker.
- Set metrics, review dates, incident actions, and reassessment triggers before production use.
Common failure modes and how to correct them
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Why it fails: Generic model claims do not describe your data, users, decision thresholds, or harms.
Correction: Freeze the workflow and decision role first, then test the tool against local reference cases.
Calling a nominal approver “human oversight”
Why it fails: Reviewers may lack time, context, authority, or a way to challenge the output.
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Correction: Give reviewers independent evidence, override authority, training, workload capacity, and a documented escalation path.
Ignoring indirect and excluded groups
Why it fails: People affected by downstream allocation or unable to appeal can bear the greatest harm.
Correction: Map direct and indirect groups, test accessibility and language barriers, and provide an effective human route.
Signing a generic data-processing addendum
Why it fails: It may omit model-training uses, subprocessors, support access, retention, or change notification.
Correction: Match terms to the data-flow map and verify actual conduct periodically.
Treating a one-time assessment as permanent
Why it fails: Models, data, users, vendors, and laws change.
Correction: Assign an owner, monitor defined indicators, and pause or reassess at each trigger.
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Deploy only when the intended use is specific, affected people and harms are understood, evidence supports fit for purpose, material bias or discrimination is mitigated, processing and contracts are accurate, human oversight and redress work in practice, and an accountable owner accepts the documented residual risk. If any of those conditions is unresolved, narrow the use, run a controlled pilot, obtain better evidence, or do not procure the tool. The quality of the decision depends less on the length of the form than on whether it faithfully describes the real deployment and remains current after launch.
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