How do you answer AI governance requirements with evidence?

Use-case identity

Anchor the buyer question to one defined AI use case

Retain the exact RFP language, definitions, referenced law or policy, question identifier, lot, required response type, requested evidence and relevant date. Words such as explainable, fair, safe or human-supervised have no single test outside a context. The buyer may be asking for a current product fact, a supplier governance practice, a contractual duty, a deployment responsibility or a legal conclusion. Preserve those as separate propositions.

Define the use case as a bounded job. Record the user, affected people, intended task, decision or action influenced, material consequence, operating environment, frequency and excluded uses. “AI for customer service” is too broad. Classifying a message topic, flagging an urgent safety phrase, retrieving an approved policy paragraph, drafting a reply and approving compensation are different functions with different evidence.

Fix the offered configuration. Name the bidder and provider entities, product edition, enabled functions, model and release policy, instruction set, retrieval collection, connected tools, locations where relevant, logging mode, human review path and fallback. If a value will be selected after award, state the selection rule and evidence still due. Do not manufacture one best-case configuration from incompatible options.

System boundary

Map the complete system, not only the model

Trace an input to its practical effect. Include collection and preprocessing, deterministic checks, prompts or instructions, one or more models, retrieval sources, post-processing, policy rules, connected tools, queues, displays, notifications, human actions, logs and fallback routes. The same foundation model can produce materially different behavior inside two applications. The RFP answer concerns the offered system in its operating context.

Create a component record with legal entity, service function, version or change policy, supplied information, output, customer configuration, evaluation responsibility, monitoring signal and failure path. Distinguish the organization that develops a model, the supplier that integrates it, the buyer that deploys the service and the person who operates it. Record the facts first. Counsel determines how applicable rules classify those parties.

Separate model knowledge from buyer content and retrieved authority. A generated answer may depend on provider training, the supplier instruction set, a buyer knowledge base and the current conversation. Record which source can influence which output, how updates enter, and what happens when sources conflict or cannot be reached. This makes a transparency or accuracy statement testable without disclosing protected implementation detail.

Applicable duties

Record classifications as reviewed decisions with a date

Do not infer legal status from a product label. Under the EU AI Act, classification depends on the actual system, purpose, role and context, and implementation material continues to evolve. The Commission provides guidance on the AI-system definition, transparency duties, general-purpose models and high-risk use cases. Some guidance is non-binding or draft. Record the controlling legal text, guidance status, system facts, reviewer, decision date, assumptions and the event that requires reconsideration.

Keep legal obligations distinct from voluntary governance. NIST AI RMF 1.0 is a voluntary, use-case-agnostic risk framework and is being revised. ISO/IEC 42001:2023 specifies an organizational AI management system. ISO/IEC 23894:2023 provides AI risk-management guidance. These can organize evidence and support assurance within their scope. They do not classify an RFP use case, decide local law or prove a particular system result.

Use a responsibilities table even when the final legal analysis is open. Name who maintains the use-case record, validates data, approves evaluation criteria, operates review, handles complaints, investigates incidents, authorizes model changes and can restrict or suspend use. A supplier answer can state those current facts while the authorized reviewer resolves provider, deployer, importer, distributor, controller, processor or sector-specific duties.

Control design

Convert each governance principle into a verifiable result

Split compound questions. Transparency to an operator, notice to an affected person, explanation of an individual result, documentation for the buyer and traceability for investigation are different controls. Fairness may require a defined harm, affected groups, comparison method, data limitations and response to disparity. Accuracy needs a task, reference answer, tolerance and cost of error. Human oversight needs a real intervention point. Map each proposition separately.

Write a control as an observable relationship: under a defined condition, a named mechanism should produce a stated result for a population, and a named owner should respond if it does not. Record whether support is complete, partial, dependent on buyer configuration, dependent on a future contract, under legal review or absent. A broad control family can contribute to several propositions without proving any of them automatically.

Make the failure action part of the control. A low confidence score may send a case to manual handling; an unavailable approved source may prevent drafting; a prohibited input may be rejected; an urgent phrase may bypass normal prioritization; a breached disparity threshold may restrict a function; a severe harmful output may suspend a release. Without the response path, a monitoring measure is only an observation.

Evaluation evidence

Evaluate the offered system against the work it will actually do

Build evaluation cases from the use case and its consequences. Cover ordinary work, rare but material situations, ambiguous inputs, missing context, conflicting authority, unsupported requests, known model limitations, foreseeable misuse and failure of an external component. Include the languages, document types, channels and user groups actually in scope. Public benchmarks can inform a method, but they cannot replace this system-level set.

Predefine acceptance rules. A classification measure may need per-class recall because missing a safety category costs more than an unnecessary escalation. Drafting quality may require factual support, correct policy selection, absence of invented commitments and a separate severe-error ceiling. Fairness analysis may require group-specific results and uncertainty intervals where data permit. Record why the measure represents the buyer outcome and who approved the threshold.

Preserve reproducibility at the useful level: application version, model identifier or release policy, instructions, retrieval snapshot, settings, test-set version, method, scorer, sample size, results, severe cases, exclusions and date. Keep independent assurance within its tested scope. A clean assessment of one release does not prove future provider models or the buyer configuration. A failed case remains visible after remediation and retest.

Worked example

Evidence a transport complaint assistant without widening its role

A fictional regional transport operator is buying an assistant for passenger correspondence. It classifies the topic, proposes a service team, retrieves approved policy text and drafts a reply. A staff member must review and send every response. The assistant may not decide compensation, determine fault, close a complaint, alter a passenger account or handle an immediate safety report through the ordinary queue.

The system map separates deterministic safety phrases, language detection, topic classification, policy retrieval, text generation, the staff review screen and the outbound mail service. An urgent safety match bypasses generation and creates a priority manual case. A missing or conflicting policy source blocks a proposed answer. The review screen shows the passenger message, source passages, generated draft and warnings, and the operator can edit, reject or reroute it.

The evaluation set covers English and Welsh messages, spelling variation, multiple topics, complaints about accessibility, requests for compensation, reports of smoke or injury, abusive text, missing journey details and conflicting policy dates. Topic results are reported by class. Safety recall has a separate threshold and every miss receives a severity review. Drafts are checked for source support, invented promises, tone, personal-data leakage and correct escalation. Workload testing confirms that reviewers have enough time to inspect the displayed sources.

The answer states what was tested and what remains the operator’s responsibility. It does not call the assistant an automated complaints decision-maker. It records the current model release policy, evaluation date, review permissions, safety bypass test, monitoring owner and suspension route. Counsel separately confirms applicable classifications and notices. The evidence supports a bounded drafting and routing claim, not a claim that all passenger decisions are automated fairly.

Response wording

Write the buyer answer from scoped evidence, not adjectives

Answer each proposition with the relevant use case, control result, system version, evidence date, responsible party and material limitation. Replace “responsible AI,” “fully explainable” and “bias-free” with behavior the buyer can inspect. If an RFP requires a yes or no, keep the supporting qualification in the permitted explanation or attachment and follow the procurement’s response rules.

Separate current facts, future commitments and legal conclusions. “Every draft requires staff approval in the offered configuration” is a present control statement. “The supplier will provide quarterly evaluation results” is a future contractual duty. “The buyer is the deployer” is a legal classification. Each needs its own source and authority even when the final response places them together.

Use proportionate disclosure. Buyers may need a use-case record, responsibility table, model and provider summary, evaluation report, limitations, oversight workflow, monitoring schedule, incident process, change policy or certificate scope. They rarely need raw personal test data, secret instructions, exploit strings, unrestricted logs, licensed datasets or unresolved internal findings. Obtain owner approval for the exact artifact and audience.

• Name the use case, system version and decision influence.
• Describe the control through an observable result.
• Identify who operates the control and who owns failure action.
• Reference evidence with matching population, method and date.
• State buyer configuration, human action and provider dependencies.
• Preserve open legal decisions and known evaluation limits.
• Disclose only the approved representation of protected evidence.

Operating governance

Maintain the answer through operation, incidents and change

Define monitoring from the use-case risks. Observe input and output shifts, abstentions, escalations, overrides, corrections, complaints, severe failures, unavailable sources, provider changes and control bypasses where they matter. Set an owner, review interval, investigation threshold, response time and available action. Availability and latency do not show whether the service remains accurate, fair or appropriately supervised.

Define an AI incident broadly enough for the use case. It may include a harmful or misleading output, unauthorized action, repeated discrimination, failure of oversight, undisclosed automated interaction, loss of traceability, model or data change outside approval, or inability to recover a material decision. Link triage, containment, preservation, notification review, correction, root-cause work, retest and closure. Legal and security teams retain their own notification decisions.

Reopen affected propositions after a change to purpose, user, affected population, decision role, model, provider, instruction, data, retrieval collection, tool, rule, threshold, interface, oversight staffing, operating environment, law, guidance or buyer configuration. Retest by impact rather than repeating every check blindly. Keep the superseded evidence and answer so reviewers can see what changed and why the new claim is supportable.