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Buying access to an AI service does not transfer all compliance responsibility to its vendor. Your business still needs to identify its role, intended use, data flows and affected people, then determine which laws and controls apply. A company deploying a supplier’s model can have duties even when it neither built nor trained the system.
The European Union AI Act supplies binding obligations for covered actors and uses. In the United States, NIST’s AI Risk Management Framework (AI RMF) and its Generative AI Profile provide voluntary risk-management guidance. Neither framework is a universal answer for every country, sector or use case, so high-impact deployments should receive jurisdiction-specific legal and privacy review.
The short answer: a vendor contract is not a compliance hand-off
Responsibility follows what your business does with an AI system, not simply who hosts it. Start by documenting the tool, its purpose, the people affected, the information sent to it and the countries involved. Then classify your role and the risk created by the particular use.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsUnder the EU AI Act, a business may be a deployer when it uses an AI system under its authority. A supplier can remain the provider, but deployer duties still apply in relevant cases. Article 26 sets out duties for deployers of high-risk systems, including following the provider’s instructions, assigning human oversight, monitoring operation, using relevant and sufficiently representative input data where the deployer controls it, keeping logs when required, and taking action when serious risks or incidents arise. Read the full provision at Article 26 of the AI Act.
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Other laws may apply at the same time: privacy and data-protection rules, employment and discrimination law, consumer-protection requirements, professional secrecy, cybersecurity obligations and sector-specific regulation. A general-purpose writing assistant used for low-risk drafting is not assessed the same way as a system that ranks job applicants, evaluates creditworthiness or supports a safety-critical decision.
First determine your role and the system’s intended use
Deployer, provider or another actor
The AI Act’s allocation of duties can change along the value chain. Article 25 explains circumstances in which an actor that rebrands a system, places it on the market under its own name, substantially modifies it, or changes its intended purpose in a way that makes it high-risk can assume provider responsibilities. This does not mean every configuration change or ordinary customization automatically turns a customer into a provider. The legal test depends on what changed and how the system is placed into service. See Article 25 for the role-change rules and cooperation duties.
Use case matters more than the product label
Record the specific business function rather than writing “we use an AI chatbot.” Identify whether outputs are merely suggestions, are published to customers, or directly influence a consequential decision. Note who can be affected: employees, applicants, patients, students, borrowers, customers or members of the public. The same underlying model may present different regulatory exposure when used for marketing copy, internal search or automated eligibility decisions.
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Geography and extraterritorial effects
Map where your business is established, where the provider operates and where affected individuals are located. EU exposure can arise from placing an AI system on the EU market, putting it into service there, or producing outputs used in the EU, depending on the Act’s scope and facts. A business outside the EU should not assume that location alone removes every obligation. Conversely, using an AI tool somewhere in the world does not automatically make every business subject to the AI Act.
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Which EU AI Act obligations can affect a customer?
High-risk deployments
For a high-risk system, Article 26 requires operational controls by the deployer in addition to the provider’s duties. Your implementation should follow the provider’s instructions for use, designate people who can exercise effective human oversight, monitor the system, preserve required logs, and act on risks or incidents. Input data under your control must meet the Act’s applicable quality requirements. The exact duties and exceptions depend on the system category and the conditions in the legislation, so treat the article as a legal requirement to analyse rather than a generic checklist.
Transparency obligations
The European Commission’s transparency guidance states that Article 50 obligations apply from 2 August 2026. The measures distinguish provider responsibilities from deployer notices and address situations such as informing people when they interact with certain AI systems, marking or disclosing deepfakes, identifying certain AI-generated or manipulated public-interest text, and transparency in specified biometric-identification or emotion-recognition contexts. The practical notice, timing and format depend on the system and interaction. Consult the Commission’s guidelines on transparency obligations before deployment.
Changing dates for high-risk rules
Do not reduce the AI Act to one start date. A Commission high-risk guidance page last updated 6 July 2026 describes a changed enforcement timeline following political agreement on the AI Omnibus: certain Annex III high-risk provisions are described as applying from 2 December 2027, while certain rules for AI integrated into regulated products are described as applying from 2 August 2028. The categories, transition provisions and final legal effect can differ. Check the current consolidated legislation and Commission material, including the high-risk guidance and Regulation (EU) 2026/1744, before relying on a date in a contract or compliance plan.
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NIST’s AI RMF is a voluntary U.S. risk-management framework, not a statute and not a substitute for privacy, consumer-protection, employment, sectoral or AI law. NIST says the framework is intended to be voluntary, and its overview notes that AI RMF 1.0 is being revised. Its four functions provide a useful operating model:
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- Govern: assign accountability, policies, roles, documentation and escalation paths.
- Map: define context, intended purpose, affected groups, risks and dependencies.
- Measure: test performance, security, privacy, reliability, bias and other relevant characteristics.
- Manage: prioritize risks, apply controls, monitor results and improve or discontinue use.
NIST’s Generative AI Profile, published 26 July 2024, calls out third-party integration risks involving intellectual property, data privacy and information security. It recommends adapting existing procurement and risk processes rather than treating an external model as a black box that needs no review. The profile is available as a PDF at NIST AI 600-1, and the framework overview is at NIST’s AI RMF page.
A practical workflow before employees use a third-party AI tool
- Inventory the proposed use. Record the vendor and product, model or feature, business owner, purpose, workflow, affected people, countries, data categories, expected volume and whether outputs influence a consequential decision or are shown externally.
- Classify the role and risk. For EU exposure, determine whether the product is an AI system in scope, what its intended purpose is, whether a high-risk category or Article 50 transparency duty may apply, and whether your organization is acting as deployer, provider or another actor. Escalate uncertain or high-impact classifications to qualified counsel.
- Map data before sending it. List personal data, confidential information, trade secrets, regulated records, copyrighted material and credentials that could enter prompts, uploads, logs or support channels. Decide which data classes are prohibited, restricted or permitted, and whether de-identification is effective for the use.
- Review the supplier’s terms and data practices. Confirm the legal roles for processing, retention periods, deletion and access mechanisms, confidentiality, use of customer content for model improvement, subprocessors, international transfers, security controls, incident-notification times, and ownership or licensing terms for inputs and outputs. These are procurement questions to resolve in context, not a universal legal safe harbor.
- Request evidence proportionate to risk. Ask for system documentation, known limitations, evaluation or test information, security and privacy materials, change-management notices, service levels, support contacts and audit or attestation reports. NIST specifically identifies software bills of materials (SBOMs), service-level agreements (SLAs) and SSAE reports as examples to consider for third-party transparency and risk management.
- Set internal controls. Approve named use cases, train users, restrict access, require human review for consequential outputs, provide required notices, retain decision records where appropriate, and define who can pause the system. Make reviewers responsible for checking accuracy, fairness, relevance and policy compliance rather than accepting fluent output as proof.
- Monitor and reassess. Track errors, complaints, security events, drift, usage outside the approved purpose and changes to the supplier’s model, terms, data practices or capabilities. Reopen the assessment when your organization embeds the tool in a product, connects it to new data or changes the decision it supports.
Questions to ask an AI vendor
Use the following questions to compare tools on regulatory fit, not just price or model quality. A vendor certificate or assurance report can support due diligence but cannot answer every question about your own deployment.
| Area | Questions for the supplier | Evidence to retain |
|---|---|---|
| Intended use and limits | What purposes, users, jurisdictions and prohibited uses are documented? What known failure modes or performance limits exist? | Product documentation, acceptable-use terms, model or system cards, limitations and release notes. |
| Data collection and training | What prompts, files, metadata and feedback are collected? Are customer inputs used to improve models, and can that use be disabled? | Data-flow description, privacy terms, configuration records and written answers to procurement questions. |
| Retention and deletion | How long are inputs, outputs, logs and backups retained? How do you request deletion, retrieval or correction? | Retention schedule, deletion procedure, contract clauses and completion records. |
| Security and access | Which authentication, encryption, tenant isolation, administrator controls and subprocessor safeguards are provided? | Security architecture, independent attestation where available, subprocessor list and access-control settings. |
| Change management | How are model updates, feature changes, new subprocessors and altered terms announced? Is advance notice available? | Change-notice commitments, version history, service communications and your reassessment log. |
| Auditability and incidents | What logs, export functions, incident reports and cooperation will be available if regulators, customers or auditors ask questions? | Logging specification, incident-notification SLA, audit rights and escalation contacts. |
| Availability and support | What uptime, support response, service credits and continuity arrangements apply, and what happens if the service is withdrawn? | SLA, business-continuity information, exit plan and data-export test. |
| Outputs and intellectual property | Who may use inputs and outputs, what restrictions apply, and how are claims of infringement or unsafe content handled? | License terms, indemnity language where offered, usage policy and internal review rules. |
Controls that prevent common failures
Employees paste sensitive information into an unapproved service
Publish an approved-tools list, block unsanctioned integrations where feasible, classify data in plain language and provide a safe alternative for restricted material. Technical controls such as single sign-on, access groups, data-loss prevention rules and logging are more reliable than a policy that users never see.
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A fluent answer is treated as a verified decision
Require a named reviewer to validate facts, sources, calculations and eligibility criteria. For employment, lending, healthcare, safety or other consequential contexts, document the review and give affected people any rights or explanations required by applicable law. Do not let a model’s confidence or polished style substitute for evidence.
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A silent vendor update changes the risk profile
Keep the approved model or feature version where the service allows it, subscribe to change notices, test critical workflows after updates and define a rollback or suspension process. Reassess if the provider adds browsing, autonomous actions, biometric functions, new training use or connections to production systems.
An incident is discovered but nobody owns the response
Assign an internal owner, provider contact and legal or security escalation path before launch. Define triggers for disabling the integration, preserving logs, notifying the provider, investigating affected people and reporting to authorities when a law requires it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose between third-party AI tools
Compare candidates against the use case and risk, not a generic “best model” ranking. Weight the following factors according to the harm a failure could cause:
- Regulatory fit for the countries and sectors in which the system will operate.
- Data handling, training-use controls, retention, deletion and confidentiality.
- Security architecture, identity controls, tenant isolation and subprocessor governance.
- Documentation, testing, logging, exportability and audit support.
- Human-oversight features, output filters, explainability aids and administrator controls.
- Incident response, change notification, uptime, support and exit options.
- Ability to provide evidence your organization can use in risk assessments, customer responses or regulatory inquiries.
No single certification, contractual promise or assurance report eliminates your obligations. The buyer still has to configure the service correctly, supervise its use and comply with laws that apply to the resulting process.
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When to obtain specialist advice
Seek jurisdiction-specific legal, privacy or regulatory advice when the system affects employment, credit, insurance, healthcare, education, essential services, biometric data, children, public-sector decisions, safety or legally protected rights; when you cannot determine whether the use is high-risk; when you substantially modify or rebrand a system; or when data transfers and confidentiality obligations are unclear. The sources above support an operational framework, not a universal legal conclusion for a particular business.
A defensible operating record
Keep a concise file for each approved use: the business purpose and owner; role and risk classification; data-flow and supplier review; contract and configuration; user training; human-oversight design; testing and monitoring results; notices provided; incidents and corrective actions; and dates for reassessment. This record helps management decide whether to continue, restrict or stop a deployment and gives auditors or regulators a coherent account of how the decision was made.
The practical rule is straightforward: treat a third-party AI service as a component in your business process, not as a compliance shield. Identify what the system does in your hands, control the data and decisions around it, and revisit the assessment whenever the law, the supplier or your use changes.
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