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Using a third-party AI tool does not automatically transfer your business’s legal responsibilities to its provider. Your company may still have to answer for the data it submits, the claims it makes about AI-generated answers, and decisions it allows the system to influence. A vendor contract can define protections and remedies between the parties, but it cannot make an unlawful use lawful.
Before signing, define the intended use, trace what information and outputs move through the service, and check the full set of contract terms against the risks your business would actually face. The legal result depends on the jurisdiction, industry, data, and use case; this is a procurement checklist, not legal advice for a particular deal.
Start by defining what the AI will do
Legal and commercial review depends on the actual deployment, not just the product label. An internal drafting assistant using non-sensitive material presents different questions from an AI service answering customers, screening job applicants, or shaping lending decisions.
- Name the business process, intended users, and whether use is internal, customer-facing, or decision-influencing.
- List the information involved: personal data, confidential or regulated records, proprietary material, and content belonging to third parties.
- Identify the jurisdictions and industries involved, and whether an output could affect an individual or create a commitment to a customer.
- Describe what could happen if an output is inaccurate, biased, insecure, unavailable, or presented as authoritative.
- Ask the provider to explain the system’s intended uses, limitations, testing, transparency, and the origins of relevant source data, including whether it is in-house or collected from elsewhere.
The American Bar Association’s September 2023 guidance on AI licensing deals recommends evaluating intended use and reviewing all linked terms, not only the order form. Make a written use-case record and use the same scenario when comparing suppliers; otherwise, differences in their answers may reflect different assumptions rather than different protections.
Find out exactly what happens to your data
Do not treat “customer data” as a single category. Ask how the service handles prompts, uploads, outputs, logs, and metadata, and whether the provider or its subprocessors may use any of them for model training, fine-tuning, service improvement, or human review. Get the answer in binding terms that apply to the specific product and plan, rather than relying only on a sales explanation or a general privacy statement.
Check the data lifecycle
- Use: Which data uses are allowed, and are any secondary uses prohibited unless you separately agree?
- Access: Can provider staff or contractors review inputs or outputs, and under what circumstances?
- Location and transfers: Where is information processed and stored? What cross-border transfer terms and safeguards apply to your situation?
- Subprocessors: Which third parties can handle the data? How will you be notified of changes, and what safeguards must those parties follow?
- Retention and deletion: How long are prompts, outputs, logs, and metadata retained? What is deleted during service and after termination, and how can deletion be verified?
- Security and incidents: What controls, breach cooperation, notice commitments, and audit or assurance rights are specified?
Match these commitments to your own confidentiality, privacy, and security obligations. In the UK, data-protection law applies regardless of whether processing uses AI. The UK Government AI Knowledge Hub summarizes the principle this way: “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. Those are UK-specific legal principles, not a universal statement of every jurisdiction’s requirements.
Check rights to the model, inputs, and outputs
There are two separate rights questions: whether the provider has the rights and permissions needed to supply the service, and what rights your business receives in the material it submits and the outputs it uses. Ask about training-data provenance and licences where relevant, as well as your rights to retain, modify, use, and commercially deploy outputs. Clarify whether those rights continue after the service ends.
Do not assume a contract clause saying that you “own” an output guarantees copyright protection. The ABA’s January 2024 analysis notes that copyright and patent laws in the majority of jurisdictions it discusses do not currently protect works or inventions created solely by AI; human contribution and governing law matter. The agreement can allocate contractual rights between provider and customer, but it cannot settle every question about rights against third parties.
Ask whether the provider will defend or indemnify your business against third-party intellectual-property claims, what conditions and exclusions apply, and whether that protection is meaningful under the liability limits. Also consider the reverse risk: if your team uploads material it does not have permission to use, the vendor’s terms may leave that exposure with your business.
Read protections and liability limits together
Review warranties, disclaimers, indemnities, liability caps, exclusions, insurance, governing law, dispute forum, suspension, termination, and transition assistance as one package. A broad indemnity may offer little practical protection if it is heavily qualified or sits under a low cap; a higher cap may also have carve-outs that change what is covered. Negotiability and enforceability depend on the agreement and governing law.
Focus on losses most relevant to the defined use: intellectual-property claims, confidentiality or security failures, loss of customer data, vendor misconduct, and service interruption. Compare those protections with the promises your company makes to customers. The ABA has warned that a malfunction or hallucination in a customer-facing deployment may expose the business using the system to claims or to allegations that it breached its own commitments.
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Keep oversight for outputs that affect people
For hiring, lending, customer service, or other consequential uses, do not rely on a vendor’s general accuracy or fairness assurance alone. Ask what testing is performed, what evidence or transparency is available, which limitations are known, and how errors or complaints are escalated. Define when a person must review an output, correct it, or make the final decision, and keep records appropriate to the use and applicable law.
Set boundaries for what users may submit and how they should verify results. If the system’s answer could be mistaken for a promise, professional judgment, or final decision, specify who approves it before it is communicated or acted on. The business deploying the tool needs governance that fits its own process, not just the provider’s controls.
Read every document that forms the deal
Do not stop at the order form or headline service agreement. Assemble the master or service agreement, terms of use, privacy policy, data-processing addendum, acceptable-use rules, product-specific terms, and documents incorporated by link. Check which document takes precedence if provisions conflict, how terms can be amended, and whether the provider can change the service or terms with notice.
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Compare customer and vendor indemnities, warranties and disclaimers, caps and carve-outs, insurance, dispute procedures, suspension rights, termination rights, and exit support. A clause that looks favourable in one document may be narrowed elsewhere. The ABA’s September 2023 guidance specifically urges buyers to review the full set of linked terms.
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Plan for changes, outages, and exit
Before deployment, decide what happens if the service is materially changed, unavailable, discontinued, or no longer acceptable for the use. Confirm how you can retrieve business data and outputs, how deletion works, whether transition assistance is available, and what happens to integrations or workflows when the relationship ends. If continuity matters, identify a fallback process rather than assuming the provider will remain available on unchanged terms.
Assign an internal owner to coordinate legal, privacy, security, procurement, and business reviews. Set a review point when the use, data, provider terms, or relevant rules change. UK Government guidance recommends involving legal expertise early in AI procurement; businesses elsewhere should involve counsel familiar with their own jurisdiction and sector, especially where personal data, regulated activity, or consequential decisions are involved.
Use a consistent procurement checklist
- Document the use: Define users, workflow, affected people, jurisdictions, data categories, and likely harms from a wrong or unavailable output.
- Get product-specific answers: Ask about system limitations, testing, source-data approach, data uses, human review, subprocessors, locations, retention, and deletion.
- Map rights and accountability: Confirm permissions for service delivery and customer inputs, rights to use outputs, IP-claim handling, and who must check consequential outputs.
- Read the complete contract set: Reconcile linked documents and examine warranties, disclaimers, indemnities, limits, exclusions, security commitments, changes, and remedies as a whole.
- Test the downside: Ask whether the negotiated protections and any applicable insurance offer meaningful recourse for the risks identified in the use-case record.
- Approve operations and exit: Name the internal owner, define escalation and human review, and agree on outage, termination, deletion, and transition procedures.
- Review before renewal or material change: Reassess if the provider changes terms or system capabilities, or if your business changes the data, users, geography, or purpose.
The Law Society of Ireland’s vendor-evaluation tool is a useful example of a focused assessment for legal practices, with attention to confidentiality, GDPR, privilege, security, service levels, contract terms, and exit. Its scope is legal-practice review; it is not a universal checklist for every business or industry.
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