Buying or subscribing to an AI tool does not transfer your legal and operational responsibilities to its vendor. Your obligations depend on your role, the system’s intended purpose, the data you submit, the people affected, and the countries involved. In the European Union, a business using another company’s system can still be a deployer with duties under the EU AI Act. In the United States, NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance, not a law, but it provides a practical way to control supplier and operational risks.
Start with the use, not the product name
“We use an AI tool” is too vague for a compliance decision. Record the specific feature, model or service, workflow, and version being used. A drafting assistant that proposes internal meeting notes presents a different exposure from a system that ranks job applicants, evaluates creditworthiness, makes medical recommendations, or controls access to a service.
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Build a use inventory
For every third-party AI deployment, capture:
- The vendor, product, model or API, account owner, and business process.
- The intended purpose and whether the system is used internally, customer-facing, or embedded in a product you sell.
- The categories of people affected, including employees, applicants, customers, patients, students, or members of the public.
- Input data types: personal data, confidential business information, regulated records, copyrighted material, credentials, or sensitive categories.
- Whether an output informs or triggers a consequential decision, and who reviews it before action.
- The countries where your business operates and where affected people are located.
This inventory is the evidence needed to decide which laws and contractual controls matter. It also prevents a low-risk experiment from quietly becoming a production decision system.
Check territorial and sectoral connections
EU AI Act exposure is not determined solely by where the vendor is incorporated. The location of the provider, deployer, affected people, placing on the EU market, and use of outputs can all matter under the Act’s scope and definitions. Separate that analysis from obligations under privacy, employment, consumer-protection, financial, health, education, accessibility, copyright, or national security rules. The sources here do not settle the law for a particular country or sector, so obtain jurisdiction-specific advice when the stakes are high.
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How the EU AI Act treats a third-party user
The Act distinguishes actors such as providers and deployers. A provider develops or places an AI system on the market under its name; a deployer uses a system under its authority. Supplying the software does not automatically make the customer the provider, but it also does not erase the customer’s deployer duties. Article 26 sets out deployer obligations, while Article 25 addresses responsibilities along the value chain and circumstances in which an actor’s role can change: Article 26 (deployer obligations) and Article 25 (value-chain responsibilities).
What deployer responsibilities can involve
For a high-risk system, Article 26 includes duties concerning use in accordance with instructions, competent human oversight, monitoring, input data under the deployer’s control, record keeping, and actions when risks or serious incidents arise. The exact duty depends on the system, the deployer’s role, and the conditions in the Act. A contract saying “the vendor is compliant” is not a substitute for configuring the tool correctly, training operators, or acting on observed failures.
For example, if a business uses a supplied recruitment system to rank applicants, it must understand the intended purpose, ensure staff can interpret and challenge outputs, monitor for errors, and retain the records required by applicable rules. If the same tool is used only to format an internal document, the risk classification and control set may be different. Do not infer the classification from marketing language; assess the actual function and context.
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When a customer can take on provider responsibilities
Article 25 describes role changes when an actor puts its name or trademark on a system, substantially modifies it, or changes its intended purpose so that it becomes a high-risk system. Ordinary configuration or adding a business-specific prompt does not automatically make a customer a provider. The question is whether the change meets the Act’s conditions and affects the system’s legal classification or responsibility in the value chain. Document what was changed, by whom, and why, and obtain specialist advice if a modification is substantial or a new purpose is high impact.
Transparency obligations are a separate workstream
Transparency is not limited to publishing a general AI policy. The European Commission’s guidance says the Article 50 transparency obligations apply from 2 August 2026. The guidance covers provider measures and deployer notices for specified situations, including certain interactions with AI, generated or manipulated images, audio or video such as deepfakes, text published to inform the public on matters of public interest, and biometric categorisation or emotion-recognition contexts. Read the current guidance before designing notices: European Commission guidance on Article 50 transparency obligations.
Map each user-facing interaction to the required notice, its timing, and the party responsible for delivering it. A vendor may provide a disclosure control in its interface, while your business may still need to tell people that they are interacting with an AI system or that content has been generated or manipulated. Keep evidence of the configuration and test the notice after interface or model updates.
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Due diligence questions for an AI supplier
Supplier review should be proportionate to the use case. A low-risk brainstorming service does not require the same evidence as a system connected to payroll, medical records, or a customer eligibility decision. Ask for written answers and retain the versions that applied when you approved the use.
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- What inputs and metadata are collected, where are they processed, and how long are they retained?
- Are prompts, uploaded files, feedback, or outputs used to train or improve a shared model? Is there an opt-out, and does it apply to all processing paths and subcontractors?
- How can your organization retrieve, correct, export, or delete data, including backups and logs?
- What confidentiality commitments apply to your content, and can the vendor use it for product development or human review?
- Which subprocessors and hosting regions are involved, and how are they changed or disclosed?
Security, access, and resilience
- Which authentication, role-based access, encryption, tenant-isolation, and administrative controls are available?
- How are vulnerabilities, abuse, model extraction, prompt injection, and account compromise handled?
- What incident-notification deadline, service levels, support channels, backup arrangements, and exit assistance are offered?
- Can you obtain independent audit or attestation evidence appropriate to the service and your risk?
Model behavior and compliance evidence
- What is the documented intended purpose, known limitation, prohibited use, evaluation scope, and update policy?
- What testing covers accuracy, robustness, bias, safety, privacy, and security for the version you will use?
- How are material model, data, policy, pricing, or interface changes announced, and can you delay or reject a change?
- What documentation supports your own impact assessment, transparency notices, human-oversight procedures, and incident reports?
NIST’s Generative AI Profile identifies intellectual-property, data-privacy, and information-security risks in third-party integrations. It suggests adapting existing procurement controls and considering software bills of materials (SBOMs), service-level agreements (SLAs), and attestation reports: NIST AI 600-1, Generative AI Profile. These are risk-management options, not a universal legal checklist or guarantee that a supplier is compliant.
Controls to operate after purchase
Set an internal approval boundary
Define which teams may use which tools, for what purposes, and with which data classes. Block regulated or confidential data from consumer-grade accounts unless the contract and technical settings support that use. Require a named owner who can suspend the integration and a security or privacy review before connecting new data sources.
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Keep people accountable for consequential outputs
Where an output can affect a person’s rights, access, employment, money, health, or safety, assign a qualified human reviewer with authority to reject the result. Give reviewers enough context to understand limitations and require them to record the evidence used for the final decision. Automation of a workflow does not make an error someone else’s responsibility.
Monitor, log, and respond
Track model and prompt versions, material configuration changes, access, high-impact outputs, overrides, complaints, and incidents. Define thresholds for investigation, rollback, vendor escalation, and notification to regulators or affected people where required. Test representative inputs periodically rather than relying only on the supplier’s pre-release evaluation. Retain only the logs you need and apply your organization’s retention and access rules.
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Train users on failure modes
Training should cover fabricated facts, unsafe recommendations, hidden personal data in prompts, copyright and confidentiality concerns, prompt injection, automation bias, and the process for reporting a suspected incident. Explain that a fluent answer is not evidence of accuracy and that users must not bypass review by copying an output directly into a consequential record.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where NIST fits in a U.S. risk program
NIST’s AI Risk Management Framework is voluntary. It is a structured way to organize governance rather than a federal licensing requirement or a replacement for privacy, consumer-protection, employment, sectoral, or state law. NIST describes four functions:
- Govern: assign accountability, policies, risk tolerance, inventory ownership, supplier oversight, and escalation.
- Map: document context, affected people, intended and foreseeable uses, data flows, benefits, harms, and legal or organizational requirements.
- Measure: test performance, security, privacy, bias, reliability, and human factors; record methods, limits, and results.
- Manage: prioritize risks, apply controls, monitor residual risk, respond to incidents, and decide whether to continue, restrict, or stop a use.
Use the framework alongside procurement and security processes. NIST’s overview and profile are useful for translating a vendor questionnaire into evidence and actions, but neither document determines whether a particular deployment is lawful in a particular jurisdiction: NIST AI Risk Management Framework.
A practical six-step implementation workflow
- Inventory the use. Record the tool, vendor, purpose, data, affected people, decision impact, owners, and countries.
- Classify the role and risk. For EU exposure, assess whether the service is an AI system, its intended purpose, any high-risk category, transparency trigger, and whether you are a deployer, provider, or another actor. Escalate uncertain or high-stakes classifications.
- Review data and contract terms. Confirm collection, training use, retention, deletion, confidentiality, security, subprocessors, incident notice, and intellectual-property treatment for inputs and outputs.
- Request evidence. Obtain system documentation, limitations, testing summaries, change notices, service levels, and suitable audit or attestation material. Consider an SBOM for software components where it improves supply-chain visibility.
- Implement internal controls. Approve data classes and use cases, train staff, configure access, require human review for consequential outputs, maintain logs, and publish notices required for the interaction.
- Reassess on change. Repeat the review when the vendor changes the model, terms, data practices, subprocessors, or capabilities, or when your organization connects the tool to a new process or uses it for a new purpose.
Important EU dates and why they need checking
The dates below describe the official material available for this article. They are not a single start date for every AI Act obligation.
| Item | Date or status | What a business should do |
|---|---|---|
| Article 50 transparency obligations | Applies from 2 August 2026, according to Commission guidance | Review affected interactions, notices, marking or disclosure controls, and evidence of implementation. |
| Commission high-risk guidance | Page updated 6 July 2026; it describes changed dates following political agreement on the AI Omnibus | Use the current guidance and consolidated legislation rather than an old compliance calendar. |
| Certain Annex III high-risk rules | Commission page reports 2 December 2027 for specified rules | Confirm whether your system category and transition provisions fall within that date. |
| Certain product-integrated high-risk rules | Commission page reports 2 August 2028 for specified rules | Check product-specific obligations and conformity or transition requirements. |
| Regulation (EU) 2026/1744 | Contains amendments to AI Act timelines | Verify the operative text at EUR-Lex before relying on a date. |
Read the Commission’s high-risk guidance for the categories and transition details: Guidelines for AI systems considered high-risk. Commencement and transition provisions can change and may differ by system category.
Common mistakes and their fixes
- “The vendor is compliant, so we are covered.” Fix: document your deployer role, configuration, oversight, monitoring, and incident process.
- “A small customization cannot change our role.” Fix: assess whether a modification is substantial or changes the intended purpose into a high-risk use under Article 25.
- “NIST certification makes the deployment legal.” Fix: treat NIST as voluntary risk guidance and separately check applicable law.
- “A privacy policy answers the AI question.” Fix: examine model training use, retention, confidentiality, outputs, security, and user notices as distinct issues.
- “The launch review is enough.” Fix: reassess after model, contract, data, supplier, or business-process changes and monitor real-world performance.
- “One EU date applies to every obligation.” Fix: track the specific article, system category, transition rule, and current consolidated text.
When to obtain specialist advice
Get jurisdiction-specific legal, privacy, employment, sectoral, or technical advice when the system makes or materially influences high-impact decisions; processes sensitive or regulated data; is deployed across several countries; is integrated into a product you provide; is substantially modified; or has produced a serious incident. Bring your use inventory, data-flow diagram, contract, model documentation, test results, human-oversight plan, and proposed notices. A specialist can then assess the facts rather than offering a generic answer based only on the tool’s brand or marketing category.
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