Hiring an AI provider does not automatically transfer your business’s legal responsibilities to that provider. Before signing, pin down what the system will do, what data it will handle, what rights each party has, and who bears which costs if it fails or causes harm. The right review depends on your jurisdiction, industry, data and use case; these are practical procurement questions, not legal advice for a particular deal.
Why can an AI vendor’s risks become your business’s risks?
Your business chooses how to deploy a tool, what information to give it, and whether people or customers will rely on its outputs. That means a provider’s involvement does not by itself settle questions about your own data practices, customer promises, or decisions. For example, a business that passes an inaccurate AI response to a customer may still face a dispute about the commitment it made, even if the system generated the response.
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The American Bar Association’s September 2023 analysis of AI licensing deals flags intended use, existing obligations, warranties, indemnities, liability limits and exit terms as issues to examine. The ABA’s January 2024 discussion of AI-enabled acquisitions also highlights vendor and training-data diligence, data protection, output rights and jurisdictional differences. These are useful issue-spotting sources, not a single legal rule for every country or transaction.
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A contract can establish rights and obligations between the parties and may give your business recourse if the provider breaches them. It cannot, just by saying so, make an otherwise unlawful use lawful or guarantee that a regulator, customer or court will treat the provider as solely responsible. Paul J. Malie and Anthony R. Petruzzi, partners at Tucker Ellis LLP, wrote in an August 2026 client alert that “The vendor contract is where enforceable rights and binding obligations are established.” Treat that as the authors’ contract-practice perspective, not a promise that any particular clause will be enforceable or sufficient.
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What should you establish before reviewing terms?
Describe the real workflow
Write down the business process, intended users, and the system’s role. Distinguish internal drafting or summarisation from customer-facing use or outputs that influence decisions about people. Note what happens when an output is wrong, misleading, biased or unavailable, and who can catch or correct the problem.
Inventory the information and people involved
List the information the system will receive or generate: personal data, confidential or regulated material, proprietary business information, and third-party content. Identify the relevant countries, industry requirements, and whether outputs affect individuals or create commitments to customers. These details determine which questions deserve priority and which legal obligations may apply.
Ask how the system works, within the limits of what the provider can explain
Request a description of intended use, known limitations, testing, transparency and the source-data approach. Ask whether training data is in-house, licensed, scraped or drawn from another source, where that information is available. The ABA’s 2023 analysis also advises looking beyond an order form to the full set of terms connected to the service.
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How should you assess data, privacy and security terms?
Do not treat “customer data” as a self-explanatory category. Have the provider specify how it treats prompts, uploads, outputs, logs and metadata, including access by personnel and any use for model training, fine-tuning, service improvement or human review.
- Use: Is each category used only to provide the service, or can it be used for other purposes? Can the business opt out of secondary uses?
- Retention and deletion: How long is each category kept, including after account closure? What deletion process applies at termination, and what confirmation can the provider give?
- Processing and transfers: Where is information processed? Which subprocessors can access it, what safeguards apply to them, and how will the provider notify you about changes? Establish any appropriate notice, objection or approval process.
- Security and incidents: What security controls and assurance materials are available? What cooperation and notice commitments apply if there is an incident involving your information? Consider whether audit or other assurance rights are appropriate to the data and applicable law.
- Confidentiality and third-party data: Confirm that permitted use, access and onward disclosure fit your own obligations to customers, employees, partners and other data owners.
For businesses subject to UK data-protection law, the UK Government AI Knowledge Hub states: “The UK data protection law applies irrespective of the type of technology used, so its basic principles of compliance will also apply to any AI system.” Its guidance highlights accountability, lawful basis, purpose limitation, transparency and individual rights, fairness, data minimisation, storage limitation, human oversight, accuracy and security. That is UK-specific guidance; it should not be presented as a complete statement of the law in other jurisdictions.
What rights should the contract cover for training data and outputs?
Check both directions of the rights flow. First, ask what rights the provider has to supply the model and service, and whether it can explain the provenance or licensing basis for relevant training material. Then confirm what your business may do with outputs: use them commercially, retain them, modify them, share them, and continue using them if the service ends.
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Do not equate a contract term saying that the customer owns or may use an output with a guarantee that copyright protection exists in every jurisdiction. In its January 2024 analysis, the ABA says copyright and patent laws in the majority of jurisdictions it names do not currently protect works or inventions created solely by AI; human contribution and governing law matter. The practical treatment of a particular output therefore depends on the facts and applicable law, as well as the contract.
Ask whether the provider will defend or reimburse your business for third-party intellectual-property claims, and read the conditions and exclusions. The ABA’s 2023 analysis notes that malfunctioning or hallucinated outputs in customer-facing use may expose the deploying business to claims or to allegations that it broke its own commitments. A vendor indemnity may help allocate that risk between contracting parties, but it does not prevent a third party from making a claim against your business.
How do you assess accuracy, bias and consequential use?
For hiring, lending, customer service or other consequential workflows, ask what the provider tests, what results or limitations it can explain, and how errors or suspected bias are escalated. Decide which outputs require review by a person before they are acted on or shared, who has authority to override them, and how corrections are recorded.
Match oversight to the actual stakes. An internal aid that an employee checks before use is not the same workflow as an automated output delivered to customers or used to influence a decision about an individual. A provider’s general assurance does not replace the deploying business’s own governance. Set a named operational owner and involve legal, privacy, security, procurement and business stakeholders as appropriate. UK government guidance recommends early legal engagement; businesses elsewhere should seek advice suited to their own jurisdiction and use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which contract clauses should be read together?
Review the whole agreement set, not just the order form or a headline promise. The ABA’s 2023 analysis specifically cautions that linked documents can shape the deal. Collect and compare the order form, master or service agreement, terms of use, privacy policy, data-processing addendum, acceptable-use rules, product-specific terms and any documents incorporated by reference. Check how amendments and policy changes are made and notified.
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- Warranties and disclaimers: What does the provider actually promise about the service, security, rights or performance, and what does it disclaim?
- Indemnities and liability limits: Who covers which claims? Do exclusions or caps leave meaningful recourse for the risks that matter here, such as IP claims, confidentiality or security breaches, loss of customer data, or provider misconduct? Check whether the indemnity is limited by the same cap and what conditions apply.
- Insurance and dispute terms: What insurance is relevant and evidenced? Which law, forum and dispute process apply? Their practical effect depends on the transaction and governing law.
- Suspension, termination and transition: When may either party suspend or end service? What notice, assistance and data-return or deletion steps follow? Can the business move to another supplier without losing necessary access to information or outputs?
There is no universal liability-cap figure or clause set that fits every deployment. Compare the provider’s commitments with the promises your own business makes to customers, and ask counsel to assess material gaps, enforceability and negotiation options for the relevant deal.
How can you compare providers on the same basis?
Use one representative workflow and data scenario for every provider under consideration. Record the evidence each provider gives you and distinguish a contractual commitment from a product description or an assurance that is not included in the agreement. Compare these dimensions:
- Training and other secondary-use rights for prompts, uploads, outputs, logs and metadata.
- Retention, deletion, processing locations, subprocessors and change-notice arrangements.
- Security commitments, incident cooperation and available assurance.
- Training-data provenance, rights to provide the service, output-use rights and IP claim protection.
- Testing and transparency on accuracy or bias, human review, escalation and correction.
- Warranties, indemnities, liability caps and carve-outs, insurance and dispute terms.
- Service changes, suspension, termination, data handling at exit and transition assistance.
These are diligence dimensions, not a ranking of providers. The sources available here do not establish that one vendor is universally safer or better than another.
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Assign an owner for periodic review and set a process to revisit the system when its use, provider terms, data flows, subprocessors, or relevant rules change. Keep track of outages and errors, route material incidents to the right internal teams, and confirm that the agreed deletion and transition steps can be carried out when service ends.
The Law Society of Ireland’s vendor-evaluation tool is a useful focused example for legal practices: it highlights confidentiality, GDPR, privilege risk, security, service levels, contract terms and exit. It is not a general checklist of legal requirements for every business. Whatever the sector, make the review proportionate to the data, workflow and consequences involved.
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