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Ask for evidence in six areas before signing: where the training data came from, what rights cover it, whether it represents your tasks and workplace, how the complete robot was tested for safety, how the system is secured and maintained, and how you can audit, update or leave it. A dataset description or impressive model benchmark is not proof that a physical robot is suitable or safe in your facility.
Use the questions below in the request for proposal, technical review, safety review and contract. Require written answers, supporting records and acceptance tests tied to your operating conditions.
1. Make “third-party data” traceable
“Trained on third-party data” is not a meaningful assurance by itself. It could refer to licensed industrial recordings, public videos, synthetic data, data supplied by customers, or a mixture whose origins and quality differ substantially. Ask the supplier to describe the data chain from collection to the model or robot behavior.
Request a source and responsibility map
Require a table that identifies every source category used for training, validation and testing, the party responsible for it, the collection period and the relevant countries or jurisdictions. The answer should distinguish supplier-owned, customer-provided, open-source, synthetic and other third-party material. Ask which sources remain in the delivered system and which were used only during development.
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NIST’s AI Risk Management Framework links provenance and traceability with transparency and accountability. It specifically notes that maintaining training-data provenance and attributing decisions to subsets of training data can help an organization audit a system. See the NIST Govern Playbook and the framework’s trustworthiness characteristics.
Ask how data was collected, selected and changed
- Which sensors, locations, users, objects and task demonstrations contributed data?
- What inclusion, exclusion, deduplication and sampling rules were applied?
- Who labeled the data, what instructions did labelers receive, and how was label quality checked?
- What preprocessing, enrichment, filtering or augmentation occurred?
- What versions of datasets, labels and preprocessing code were used for each model release?
Request dataset documentation and a representative sample, or a controlled review method that protects legitimate confidential information. The NIST AI Procurement in a Box workbook asks suppliers whether they can provide access to input, training and processed data, including third-party and open-source data, or a representative sample. It also raises monitoring for irregular behavior and bias. The workbook is available at NIST AI Procurement in a Box.
Expose gaps and uncertainty
Have the supplier list known limitations, geographic and operational gaps, unusual conditions, excluded populations or objects, and any data that was unavailable. Ask whether the training set contains the kinds of lighting, floor surfaces, clutter, clothing, packaging, body sizes, languages, shifts and seasonal conditions present at your site. A broad statement such as “trained on millions of examples” does not answer a site-specific coverage question.
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Ask what logs are retained and whether a failure can be investigated against a model version, data subset, label change or software change. You may not receive proprietary weights or raw records, but you should receive enough lineage to determine what changed and to support an incident investigation. Define retention periods, export formats and access for your safety, legal and security teams.
2. Verify rights, privacy and contractual control
Data rights are procurement issues, not promises to resolve after deployment. The correct answer depends on the data, people, jurisdictions and use case, so treat NIST’s questions as a due-diligence framework rather than a universal legal opinion.
Inventory permissions for every use
Ask what license, contract, notice, consent or other permission covers collection, processing, labeling, training, validation, retention, onward sharing and future updates. Require the supplier to identify restrictions that could prevent your intended use, such as limits on commercial deployment, location, industry, duration or derivative models.
Where personal, sensitive or restricted information appears, ask how people were informed, how consent or opt-out works when applicable, and how withdrawal, correction and deletion requests are handled. Ask what happens to a model already trained on affected data: can the data be removed, must the model be retrained, and who pays for the work and revalidation?
Define your access and ownership rights
Put separate rights around raw data, processed data, labels, logs, prompts or task descriptions, model outputs and improvements learned during your deployment. Ask whether you can inspect, retain, use, transfer or delete each category, and in what format. Clarify whether the supplier may reuse your recordings or operational telemetry to improve products for other customers.
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NIST’s procurement workbook explicitly asks about buyer access and ownership, data-subject awareness and consent, safeguards, withdrawal and privacy. Include those questions in the contract’s data schedule rather than relying on a general privacy clause.
Allocate claims and audit duties
Ask who responds to third-party intellectual-property claims, data-protection inquiries, regulator requests and security incidents. Specify the supplier’s duty to preserve evidence, cooperate with your investigation and support an independent audit under confidentiality terms. Identify the records you can review, notice periods, remediation deadlines and the costs each party bears.
The NIST Govern Playbook includes third-party intellectual-property and other rights among the risks an organization should address. Your agreement should name accountable roles on both sides, not just a generic support mailbox.
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3. Prove fit for the actual task and workplace
A robot is a physical system, not a model score. Performance depends on the interaction between the AI algorithm, robot hardware and software, task design and environment. NIST describes this combined effect in its Physical AI and Data Generation for Robotics work.
Define the operating envelope
Write down the tasks the robot must perform, the objects it will handle, permitted speeds and forces, workspace geometry, lighting, noise, temperature, floor conditions, network availability, human proximity and shift pattern. Include the sensors, end effectors, fixtures and upstream or downstream equipment that will actually be used. Ask the supplier to state conditions outside the validated envelope.
Demand representative and held-out tests
Request results for your intended tasks across relevant objects, users, sensors and operating conditions. Tests should include representative cases that were not used to tune the system. Ask how the supplier prevented leakage between training, validation and test data, and whether your site can supply a controlled evaluation set.
Require task-level measures that matter operationally: successful completion, mis-grasps, collisions or near misses, recovery time, human interventions, cycle-time distribution and performance degradation when a sensor or network is impaired. A single average accuracy number can hide failures that are unacceptable in a physical process.
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Set acceptance thresholds before the pilot
For each task, define the minimum acceptable performance, maximum intervention rate, allowed downtime, environmental limits and conditions that trigger a safe stop. Identify who performs the test, how many repetitions are required, what constitutes a failure and whether results must be independently witnessed. Make production access conditional on passing these criteria.
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Ask what happens in unfamiliar conditions
- How does the robot detect uncertainty or an out-of-distribution object?
- What does it do when perception and control disagree?
- How does it respond to a failed, obstructed or miscalibrated sensor?
- What happens when connectivity, a cloud service or a third-party component is unavailable?
- Can an operator pause, override, reset and safely recover the robot without supplier intervention?
Require demonstrations of these cases in a representative environment, not only a scripted happy-path video.
4. Obtain a safety case for the deployed system
Safety evidence must cover the robot, its software, tooling, workcell, people and procedures as deployed. A model card or benchmark cannot substitute for a system-level safety case.
Request the hazard and risk analysis
Ask for the hazards considered, assumptions, risk controls, residual risks and acceptance criteria. The analysis should cover foreseeable misuse, loss of localization, unexpected object properties, degraded sensors, communication loss, software faults, human entry into the workspace and interactions with other machines. Require the supplier to identify which controls are technical, procedural or dependent on your staff.
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Review simulation and in-domain evidence
Ask which scenarios were simulated, what was validated against physical testing and where simulation is known to be inaccurate. Require in-domain testing in a workcell configured like yours. NIST’s AI RMF identifies simulation, in-domain testing, real-time monitoring and human intervention as practical approaches for managing AI risks; its Manage Playbook describes lifecycle risk controls and monitoring.
Specify monitoring and intervention
Document the signals operators and supervisors can see: confidence or uncertainty indicators, force and speed limits, workspace violations, sensor health, policy changes and repeated near misses. Define who is authorized to stop the system, how a safe stop is performed, how the robot is restarted and what evidence is captured. Test intervention under realistic time pressure.
Set incident and change procedures
Contract for immediate notification of serious incidents, vulnerabilities and material behavior changes. Require a shared process for triage, evidence preservation, corrective action, customer communication and return to service. State when a software, model, data or hardware change requires renewed risk assessment and acceptance testing. Do not allow silent updates to a safety-critical behavior.
5. Treat security and support as continuing obligations
Third-party training and service dependencies create risks after purchase. NIST identifies data poisoning and data exfiltration among AI security concerns and recommends documenting, testing and monitoring third-party resources.
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- Which people, services and devices can access training data, telemetry, model artifacts and robot controls?
- Are privileges separated for operators, developers, support staff and administrators?
- How are credentials, keys, remote sessions and logs protected?
- Can the robot operate safely if cloud access is interrupted, and can remote access be disabled by the buyer?
- How are uploaded data and outputs protected from unauthorized extraction or reuse?
Ask for a dependency inventory covering operating-system components, libraries, models, cloud services, connectivity and supplier-provided hardware. Require vulnerability disclosure channels, severity targets, patch-testing procedures and evidence that updates do not bypass your acceptance controls.
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Agree on update and retraining governance
Define what counts as a material change: a new model, training-data source, sensor, control policy, dependency, safety limit or interface. Require advance notice, release notes, test results and rollback instructions. Specify whether you can pin a validated version, defer an update for a defined period or approve changes individually.
Plan for outages and supplier failure
Ask how the robot behaves when a data provider, model host, connectivity service or critical component becomes unavailable. Require contingency modes, recovery objectives, backup and restore procedures, spare-part commitments and support escalation. Identify a safe state for prolonged outage rather than assuming the system will simply continue operating.
Write an exit and decommissioning plan
Specify how you retrieve your data, logs, configurations and permitted model artifacts; how the supplier deletes retained copies; and how deletion is verified. Address portability to another integrator, removal of remote access, disabling credentials, safe disposal or return of hardware and preservation of records needed for legal or safety purposes. Include fees, timelines and assistance obligations before you sign.
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When alternatives are under consideration, score each against identical, use-case-specific requirements. Do not let one supplier answer with a benchmark while another provides site tests. Mark an item “not stated” until evidence is supplied.
| Comparison axis | Evidence to request |
|---|---|
| Task and environment fit | Defined operating envelope, scenarios, performance results and failure behavior for the intended tasks |
| Data provenance and rights | Source categories, collection and selection process, quality records, permissions, restrictions and known gaps |
| Evaluation strength | Representative validation, held-out conditions, acceptance thresholds, test expertise and independent-review options |
| Physical safety | Risk assessment, system-level test results, monitoring, human intervention, safe stop and incident process |
| Security and resilience | Access controls, dependency inventory, update process, vulnerability response, continuity and recovery plans |
| Governance and auditability | Logs, documentation, contractual access, change notices, audit rights and named accountable roles |
| Lifecycle and exit | Support, retraining and update terms, portability, data deletion and retention, and safe decommissioning |
Weight the axes according to the consequences of failure in your facility. Keep the scoring record with the procurement file so a later change in model, supplier or use case can be compared against the original decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Turn the questions into a staged procurement
Stage 1: Issue a fact-based request for information
Send every supplier the same data, rights, task, safety, security and lifecycle questions. Ask for document titles and version numbers, not links to a marketing page. Require suppliers to label claims as measured results, design targets, estimates or unsupported areas.
Stage 2: Conduct a controlled technical review
Have engineering, operations, safety, security, privacy and legal reviewers examine the evidence together. Reconcile the source map with contract language and the dependency inventory with the security design. Record unresolved assumptions and convert each into a test or contract condition.
Stage 3: Run a representative pilot
Use your sensors, tooling, network controls, objects, operators and workcell layout. Keep a held-out set of tasks for final acceptance. Exercise uncertainty, sensor faults, network loss, human intervention, emergency stop and recovery. Capture model, software, data and hardware versions for every run.
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Stage 4: Gate production on written acceptance
Sign off only when the robot meets the agreed thresholds and the supplier has delivered the safety case, operating limits, incident contacts, update process, logs and exit terms. If a requirement is deferred, document the owner, deadline, interim control and explicit authorization to operate.
Stage 5: Monitor after deployment
Review interventions, near misses, drift, task mix, sensor health, security events and supplier changes at a defined cadence. Reassess the system when the workplace, objects, users, model, data source or dependency changes. A successful pilot is evidence for that configuration, not a permanent guarantee.
8. Red flags that require a stronger answer
- “Our model is industry-leading.” Ask for task-specific results, held-out conditions and failure data.
- “The data is proprietary.” Ask for source categories, rights evidence, a protected sample or an independent review path.
- “The robot is safe by design.” Request the hazard analysis, test conditions, monitoring and intervention evidence.
- “Updates are automatic.” Require change notices, test results, rollback and customer approval rules.
- “The cloud is optional.” Verify what functions fail offline, how remote access is controlled and what safe state applies during an outage.
- “We have never seen that failure.” Ask how failures were sought, logged and reproduced, and what happens when one is reported.
None of these answers proves misconduct. They show that the evidence is too general for a purchase decision involving a physical system.
9. Understand the status of ISO/CD 26264-1
ISO/CD 26264-1 is an under-development committee draft listed by ISO as a 2026 project. Its stated scope covers humanoid-robot dataset lifecycle stages from planning through decommissioning, including quality, security and privacy requirements. It is not a final published standard, so do not represent compliance with it as certification or a settled requirement. The project page is ISO/CD 26264-1.
You may use the draft’s lifecycle emphasis as a discussion aid, but your binding requirements should come from the documented risks, acceptance tests and contracts for your deployment. Legal, privacy and safety duties remain dependent on the facts and jurisdiction.
Questions to put directly to the supplier
Use these prompts verbatim in a questionnaire, then request evidence for each answer:
- What third-party, open-source or customer data contributed to training, validation and testing, and who is responsible for each source?
- How was each data source collected, selected, labeled, processed and quality-checked? What are its exclusions and geographic or operational gaps?
- What evidence shows that the data and tests represent our tasks, environment, sensors, users and foreseeable conditions?
- Can we review dataset documentation, a representative sample or an independent assessment under confidentiality terms?
- How can a robot outcome be traced to relevant data, model and software changes, and what logs are retained?
- What licenses, permissions, notices or consent cover collection, processing, training, retention, sharing and updates?
- How are correction, withdrawal and deletion requests handled, including their effect on trained models?
- What rights do we receive to access, inspect, retain, use, transfer and delete raw, processed and training-related data?
- Which tasks and operating conditions were evaluated, what were the thresholds and results, and who performed the tests?
- What happens during uncertainty, unfamiliar objects, sensor failure, connectivity loss or third-party service failure?
- What monitoring, human override, safe stop and incident-reporting mechanisms exist, and who acts when a risk is reported?
- How are model, data and software changes approved, communicated and tested before deployment?
- What controls protect training data, model access and robot interfaces from unauthorized use, exfiltration or poisoning?
- What is the contingency, support and decommissioning plan if the supplier, data provider or critical service becomes unavailable?
These questions reflect the procurement concerns in NIST’s workbook and the lifecycle practices in the NIST Govern and Manage Playbooks. A supplier that can answer them with specific, reviewable evidence gives you a basis for a reasoned decision; a supplier that offers only a dataset label or benchmark does not.
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