Technology, IP & DataPractice area
AI Model Procurement, Training-Data & Deployment Agreements
Organisations adopting, building or commercialising AI need contracts that connect the model, the data, the intended use and the operating reality. A globally minded agreement strategy can make decision rights, intellectual-property allocation, data controls, assurance and change management clearer before a system moves into production.
The starting point
Make the next decision with the commercial context in view.
An AI procurement or deployment is rarely a conventional software purchase. A buyer may be acquiring access to a general-purpose model, commissioning a customised system, licensing a dataset, supplying enterprise data for fine-tuning, or embedding a model within a customer-facing or operational workflow. Each route can change who controls inputs and outputs, who may use data for improvement, what assurance is available, how dependencies are managed and what happens when a provider changes the service. A useful starting point is to define the commercial objective and the risk boundary together: what must the model do, what data may it receive, which people or systems may rely on it, what decisions remain human, and what evidence is needed to operate and exit responsibly. International reference points underscore the value of lifecycle risk management, accountability, transparency, robustness and privacy; they do not remove the need to assess the arrangement and applicable rules in context.[^nist][^oecd]
How we help
The work around the decision.
01
Procurement architecture and scope.
Mapping the proposed service—model access, APIs, hosted tools, managed services, custom development, fine-tuning or integration—against business use cases, dependencies, acceptance criteria and delivery responsibilities.
02
Training-data and input-data arrangements.
Clarifying provenance, permissions, permitted uses, quality expectations, restrictions, retention, security, deletion and any agreed rules for using customer data, prompts or feedback to train, test or improve a model.
03
Intellectual-property and output allocation.
Addressing ownership or licence rights in pre-existing materials, datasets, prompts, customisations, model outputs, evaluation assets and documentation, with appropriate treatment of third-party components and usage restrictions.
04
Assurance, testing and documentation.
Structuring proportionate requirements for technical documentation, evaluations, limitations, performance information, security, incident processes, audit or information rights and change notifications. The detail should reflect the use case and the role of each party rather than assume that all AI services carry the same risk.
05
Deployment and operating controls.
Defining authorised users, permitted purposes, human oversight, user-facing disclosures where relevant, monitoring, escalation, content or output controls, and responsibilities across the customer, provider, integrator and other suppliers.
06
Data protection, confidentiality and security.
Aligning contractual data handling, cross-border arrangements where applicable, confidentiality protections, access controls, subcontracting, breach notification and cooperation obligations with the parties’ operational design and legal assessment.
07
Risk allocation and supplier accountability.
Considering representations, remedies, service levels, limitations of liability, indemnity structure, insurance, suspension rights and incident response in light of the procurement model, available assurance and the consequences of failure.
08
Change, continuity and exit.
Planning for model updates, altered terms or usage policies, deprecation, provider or subprocessor changes, portability, transition assistance, return or deletion of data, retained records and orderly migration to another solution.
Context for the decision
The facts and records that shape the route.
1. Is this simply a software licence?
Not necessarily. The answer may turn on whether the arrangement provides a hosted application, access to a model through an interface, bespoke development, data services, operational support or a combination of these. The contract should describe the actual allocation of control and responsibility, rather than rely on a label. This is general information; the appropriate structure requires review of the proposed service and governing context.
2. Can the provider use our inputs or data to improve its models?
That is a contractual, technical and data-governance question, not a default assumption. The parties may need to identify the relevant data categories, purposes, permissions, safeguards, opt-in or opt-out mechanisms, retention and deletion position, and any restrictions applicable to personal, confidential or regulated information. A clear written position can help align the service configuration with the intended data boundary.
3. Who owns AI-generated outputs?
There is no universal answer. Outcomes may depend on the contract, the nature of the inputs and outputs, third-party terms, applicable intellectual-property rules and the way the output is used. A practical agreement should distinguish between ownership, licences, allocation of risk, confidentiality and restrictions on reuse; it should not assume that every output attracts the same rights or treatment.
Questions, not prescriptions
What may matter.
1. Is this simply a software licence?
Not necessarily. The answer may turn on whether the arrangement provides a hosted application, access to a model through an interface, bespoke development, data services, operational support or a combination of these. The contract should describe the actual allocation of control and responsibility, rather than rely on a label. This is general information; the appropriate structure requires review of the proposed service and governing context.
2. Can the provider use our inputs or data to improve its models?
That is a contractual, technical and data-governance question, not a default assumption. The parties may need to identify the relevant data categories, purposes, permissions, safeguards, opt-in or opt-out mechanisms, retention and deletion position, and any restrictions applicable to personal, confidential or regulated information. A clear written position can help align the service configuration with the intended data boundary.
3. Who owns AI-generated outputs?
There is no universal answer. Outcomes may depend on the contract, the nature of the inputs and outputs, third-party terms, applicable intellectual-property rules and the way the output is used. A practical agreement should distinguish between ownership, licences, allocation of risk, confidentiality and restrictions on reuse; it should not assume that every output attracts the same rights or treatment.
4. What assurance should we request before deployment?
The level of assurance should be proportionate to the use case, affected people, autonomy of the system, data involved and consequences of error or misuse. Relevant requests may include material limitations, testing or evaluation information, security practices, documentation, monitoring and incident routes. Authorities’ AI risk resources emphasise the value of managing risk across design, use and evaluation, while the necessary measures remain context-dependent.[^nist]
5. How should a contract handle model changes?
Model and service changes can affect performance, security, data handling, cost, compatibility and the controls built around a workflow. Agreements can therefore address notice, testing, change-management cooperation, suspension or rollback where feasible, records, and termination or transition options. Whether a particular mechanism is appropriate depends on bargaining position, service design and applicable requirements.
Begin with context
Put the decision into focus.
For an initial conversation, share only high-level, non-confidential context about the relevant business decision, markets, parties, documents and timetable. Do not send confidential, privileged, personal or time-sensitive material through a website enquiry.Legal information only. This page provides general legal information, not legal advice. It does not address the facts of any individual matter and does not create a lawyer-client relationship. The appropriate route, framework, timing and outcome depend on the applicable law, documents and circumstances.