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An AI services contract should define what the provider will deliver, what data each party may use and retain, how security will be verified, how performance will be measured, and what happens when the service fails or the relationship ends. Put those commitments in the agreement and its schedules—not just in sales presentations or informal assurances. The right terms depend on the service, data, deployment, jurisdiction, and sector; this checklist is a negotiation aid, not a universal contract form or legal advice.
Start by defining the service and its boundaries
“AI services” can describe anything from an advisory assistant to a system that processes customer records or makes recommendations used in consequential decisions. A useful contract replaces broad descriptions with a precise account of the work, responsibilities, and conditions under which the service will operate.
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Describe deliverables, exclusions, and dependencies
Identify the service, each deliverable, milestones, integrations, and any implementation or support work. State what is expressly excluded. List the inputs the customer must provide, access the provider needs, technical or operational dependencies, and the decisions each party must make. Name accountable contacts or decision-makers where doing so will prevent requests or approvals from getting lost.
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Clarify whether the service provides information for a person to review or makes, recommends, or routes decisions. If people are expected to check results, describe what review means in practice and which party is responsible for the resulting decisions. Do not let a generic description of “human oversight” obscure who actually does the work.
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Set acceptance and change-control rules
For each deliverable or milestone, specify how acceptance will be assessed, who performs the assessment, and what happens if the work does not pass. Define the test data and environment, operating assumptions, and any human review used in testing. Set a process for reporting defects, correcting them, retesting, and handling missed milestones. A change-control procedure should identify who can request a change, how its effects on scope, schedule, cost, data, or performance are assessed, and when written approval is required.
NIST’s AI Procurement in a Box: AI Government Procurement Guidelines is aimed at government buyers, not a mandatory clause set for private deals. Its recommendations are useful when negotiating access to data, vendor accountability, and evaluation, but the parties must tailor the project scope and acceptance terms to their own transaction.
Map data, permitted use, and ownership explicitly
Do not treat “customer data” as one undifferentiated category. A data schedule can make the contract workable by identifying the information involved, the permitted uses, who can access it, and what happens to it during and after the engagement. These decisions are related but distinct: ownership, access, license rights, confidentiality, retention, and model-training permissions should each be addressed rather than inferred from one another.
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List the categories that apply to the service. Depending on the arrangement, these may include customer-provided records; personal or sensitive data; prompts and other inputs; raw outputs; processed or enriched outputs; logs; provider data; and third-party data. For each, address:
- Which party supplies it and which party may access it.
- The specific service purposes for which it may be used, including any permitted operational or support use.
- Whether it may be retained after the engagement or reused for another customer or purpose.
- Whether it may be used to train, fine-tune, evaluate, or improve a model, and whether the parties can opt in or out.
- What access, export, or usage rights each party has in the data and resulting outputs.
- When and how data must be returned or deleted, and how the provider will confirm deletion.
Answer “Can the provider train on our data?” in direct language. If training or other reuse is allowed, define which data, for which purpose, under what conditions, and whether that permission continues after termination. If it is not allowed, say so expressly and address the handling of copies or derived information to the extent the parties intend to restrict them. The sources cited here do not establish a universal ownership or usage default; the agreement must state the rights the parties have negotiated.
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Secure access to inputs, outputs, and records
Specify what the customer can inspect and retrieve, including relevant raw inputs, processed or enriched outputs, and logs needed to monitor, assess, or correct the service. Identify export formats and any access arrangements that apply during the contract and at exit. If the provider restricts access to protect its intellectual property, state what is restricted and why, and negotiate an alternative such as representative, current samples or other evidence that still permits meaningful evaluation.
The NIST government procurement guide recommends access to data used and produced by AI models, clear articulation of data ownership, and written confirmation of deletion. Those recommendations concern government acquisition; they are not automatic rules for a private transaction. They nevertheless offer practical questions for any buyer assessing whether it can monitor the service and retrieve what it needs.
Check third-party data rights and provenance
Ask the provider to explain how external data used in the service was collected and governed, what integrity safeguards apply, and whether it has the rights needed to provide or commercialize that data for this engagement. A claim that information is “public” does not by itself establish unrestricted rights to use or redistribute it. The contract can require relevant disclosures and representations, while the appropriate allocation of legal risk should be determined for the transaction and applicable law.
Put security obligations and oversight in writing
Security commitments should reflect the information handled and the service’s risk. Avoid relying on a broad promise to maintain “industry-standard security” unless the parties also define how that promise will be understood and verified. The Federal Trade Commission’s business guidance recommends writing security expectations into provider contracts and overseeing provider performance. It gives encryption as an example, not as a complete universal control list.
Choose controls and evidence that fit the service
Depending on the risks, negotiate specific commitments concerning access controls, encryption, logging, vulnerability management, personnel safeguards, and security assurance. Decide what evidence the provider will supply, how often it will be supplied, and how the customer may follow up on material gaps. A useful security term tells the parties what is expected and what evidence can show whether the provider is meeting it; the control set should not be presented as mandatory in every deal.
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The FTC’s provider-security guidance emphasizes written expectations and oversight. Its broader Start with Security guide is business guidance, not a contract template. NIST’s Cybersecurity Framework supplier-contract references also point to topics such as supplier evidence, security requirements, verification, disclosure, and defined responsibilities. These sources support discussion points; they do not make every possible control a universal contractual requirement.
Name subprocessors and manage incidents
Identify material subprocessors that handle relevant data or deliver important parts of the service. Set rules for notice, approval, or other agreed controls when a provider adds or changes them, and require relevant obligations to flow down the chain. Decide what information the primary provider must disclose about the subcontracted work and how it remains accountable under the agreement.
For a security incident, define the provider’s notice and cooperation duties, the information it must share, and how the parties will coordinate investigation, communications, and remediation. Choose a contractual timeline that fits the service and the parties’ needs, but do not assume one number applies to every situation: applicable legal notification duties and timing depend on the jurisdiction and facts. The cited sources do not establish a universal number of hours or days.
Make performance measurable under stated conditions
A contract should translate desired outcomes into measures the parties can observe. Broad promises that a service will be “accurate,” “efficient,” or “high quality” are difficult to assess unless the parties agree what those terms mean for the task and how results will be tested. Tie each measure to the conditions under which it applies.
Build an evaluation plan
For each material outcome, agree on a baseline, test method, production conditions, reporting cadence, and escalation threshold. Choose accuracy or quality measures suitable for the task rather than assuming one score captures every important failure. State which test data will be used, how human review factors into evaluation, and what the parties will do when results fall below an agreed threshold. If the service operates in materially different conditions from the test environment, address how that difference affects interpretation of results.
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Consider whether the contract needs reporting on errors, exceptions, or other task-relevant outcomes, in addition to a headline measure. Specify who receives the reports, who investigates an issue, and how remediation or retesting works. These provisions make evaluation an operating process rather than a one-time demonstration.
Manage changes to the model or operating conditions
Address how the provider will notify the customer of changes that could materially affect the service—for example, changes to the model, underlying service, data sources, or operating conditions. Define which changes require advance notice, review, or approval, and how the parties will re-evaluate performance after a change. The contract should also clarify what happens if the changed service no longer meets the agreed requirements.
NIST’s AI Risk Management Framework (AI RMF 1.0), published January 26, 2023, is a voluntary resource for organizations designing, developing, deploying, or using AI systems. Its functions—Govern, Map, Measure, and Manage—can help organize questions about responsibility, context, evaluation, and response. The NIST AI RMF Playbook suggests actions aligned to those functions and says it is not a checklist to apply in full. The framework is not a certification, legal mandate, contractual remedy, or warranty that a model will achieve a particular result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Agree on failure remedies, exit, and transition
Performance measures are more useful when the agreement connects them to a process for addressing problems. Decide what constitutes a material service failure, what remediation the provider must undertake, how quickly the parties will review and retest a fix, and whether the customer may use negotiated service credits or other remedies. The appropriate remedy depends on the deal; the cited guidance does not prescribe one.
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Liability limits, indemnities, intellectual-property allocation, and regulatory responsibilities require careful allocation in light of the service and governing law. The guidance described here does not decide those terms for a particular contract. Have qualified counsel assess them alongside applicable privacy, AI, consumer-protection, procurement, security, and IP obligations.
Compare proposals using the same questions
When evaluating providers or negotiating competing drafts, use a consistent set of questions. A detailed answer is more useful than a broad assurance that a provider is “flexible” or “secure.”
| Area | Questions to compare |
|---|---|
| Scope and evaluation | Are deliverables, exclusions, dependencies, acceptance tests, and change procedures specific enough to administer? |
| Data access and rights | Can the customer access and export relevant inputs, outputs, logs, and derived data? Are ownership, licenses, and permitted uses explicit? |
| Retention and reuse | Are retention, deletion, confirmation, and model-training permissions stated separately for relevant data categories? |
| Security | Are controls, evidence, provider oversight, incident cooperation, and subprocessor rules concrete and verifiable? |
| Third-party data | Does the provider disclose relevant provenance, governance, integrity safeguards, and rights to supply or commercialize the data? |
| Outcomes and change | Are metrics tied to stated test and production conditions, with reporting, escalation, and change evaluation? |
| Exit and remedies | Can the customer obtain usable data and outputs, transition the service, and invoke a defined response to failure? |
These comparison axes synthesize procurement, security, and AI risk guidance; they are not a scoring standard. Which items matter most will depend on what the service does, the data it handles, and the consequences of an error.
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The source materials have different audiences and authority. NIST’s procurement guide addresses government acquisition; the AI RMF is intended for voluntary use; the FTC materials are business guidance; and NIST’s cybersecurity supplier references are guidance rather than ready-made contractual language. None supplies a complete private-sector contract or resolves the applicable law for a specific transaction.
Before signing, have the business, technical, security, privacy, and legal stakeholders review the same operating description and data schedule. Ask them to identify what the service needs, what risks the contract must address, and what evidence will show that the provider is meeting its commitments. Qualified counsel should adapt the final terms to the relevant jurisdiction, sector, deployment, and data.
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