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Before sending business or personal information to an AI service, read the signed agreement, data-processing addendum (DPA), service terms and product settings as one data-flow contract. Confirm exactly what the vendor may do with prompts, files, outputs, feedback and metadata; how long each item is retained; which subprocessors and model providers receive it; what security and incident help you get; and which terms govern the model, region and configuration you actually use. A general “business data” label or a privacy notice is not a substitute for those permissions.
Start with the complete contract and data map
AI services rarely operate under one document. The controlling terms may be split among a master agreement, DPA, service-specific terms, acceptable-use rules, model terms, support terms and administrator settings. Save the versions in force on the date you approve the service, then map the proposed data flow against them.
Identify every data type
- Content: prompts, uploaded files, images, audio, source code and records sent by employees, customers or connected applications.
- Results: generated text, code, images, classifications, embeddings and other outputs.
- Operational data: account identifiers, usage data, telemetry, device information, logs and support tickets.
- Derived data: feedback, evaluations, safety labels, profiling signals, deidentified material and aggregates created from your content.
Define these terms in the agreement rather than assuming “Customer Data” covers them all. Require the definitions to apply to the named service, optional features, add-ons, model endpoints and connected applications. Note whose information is present, the jurisdictions involved, sensitivity (for example health, financial, employment or children’s data), and the expected volume and duration.
Assign roles and instructions
For each activity, record whether your organization and the provider act as controller, processor or another role, who decides the purposes and means, and which documented instructions apply. The Information Commissioner’s Office (ICO) recommends detailed processing information and instructions in the contract. The vendor should notify you if it cannot comply with an instruction or believes an instruction conflicts with data-protection law.
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Do not let a single role statement obscure separate activities. A provider might process customer content on your instructions to deliver an application while deciding its own purposes for billing, abuse prevention or service analytics. Each purpose needs its own legal and contractual treatment.
Make data reuse a separate, explicit permission
Ask a direct question for every data type: may the provider use it for training, fine-tuning, evaluation, benchmarking, human review, safety monitoring, analytics or general product improvement? Require a yes-or-no answer tied to the service and configuration, not a marketing phrase such as “we do not train on business data.” State whether the restriction applies to affiliates, subcontractors and upstream model providers.
Specify allowed and prohibited uses
A services-only clause should say that content is processed to provide, secure and support the contracted service, then list any additional purposes separately. If evaluation or safety review is necessary, identify the data involved, personnel access, retention period and safeguards. Prohibit sale, advertising, unrelated profiling and use to develop another customer’s product unless you have deliberately agreed to those activities.
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Treat deidentified and aggregated data carefully
Define the deidentification standard, method, testing and reidentification controls. Ask whether the vendor may link supposedly deidentified information with other datasets, and who bears the risk if it can be reidentified. State whether aggregated or deidentified material may be used for general model or service improvement, for how long and by whom.
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The February 2024 OpenAI DPA illustrates why this language deserves separate review: it permits continued improvement use of information derived from customer data when it has been deidentified, anonymized or aggregated so it is no longer personal data and does not identify the individual or customer. That is an example of one DPA, not a universal market rule; read the terms for your chosen service.
Set retention, deletion and exit terms by service
Obtain a retention schedule that distinguishes active content, logs, backups, abuse or safety records, support copies and legal holds. Ask when the clock starts, whether inactivity changes it, and whether data is stored in more than one region. Require deletion or return at contract end, a usable return format, written confirmation and flow-down obligations for subprocessors.
Check exceptions and backup expiry
Legal-retention exceptions should identify the records affected, the purpose, access limits and deletion date. Backups may persist after production deletion; the contract should state how they are isolated and when they expire. Ask what happens to prompts or outputs copied into security, quality or human-review systems.
The ICO guidance states: “Include clauses to instruct processors to delete or return all personal information at the end of the contract, unless the law requires its storage.” Treat this as a baseline for a UK GDPR arrangement, then adapt it to your jurisdiction and data categories.
Use product-specific figures, not assumed defaults
The February 2024 OpenAI DPA describes a maximum 30-day retention period for API Service Customer Data, subject to a legal-retention exception. The same DPA says ChatGPT Enterprise Service Customer Data is retained during the agreement term unless otherwise stated, and directs subprocessors to delete Customer Data within 30 days after DPA termination unless law prohibits deletion. These are terms in that particular DPA and service, not universal AI-vendor deadlines. Your schedule should name the exact product and edition.
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Map subprocessors and upstream model providers
Request the current subprocessor list, each party’s location and function, and the notice mechanism for changes. The contract should provide prior authorization or a documented general-authorization process, a meaningful objection period, a cure or replacement option, and a practical termination remedy if the objection cannot be resolved. Confirm that the vendor remains responsible for subcontractor performance within the agreement’s liability limits.
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Follow the data beyond the named vendor
- Identify which provider hosts the model, stores prompts, performs moderation or supplies support.
- Record processing locations and transfer mechanisms for each region in use.
- Check whether a feature silently routes content to a different provider or separate terms.
- Confirm deletion, confidentiality, security and incident duties are passed to every subprocessor.
- Specify your rights if a new provider creates an unacceptable legal, geographic or security risk.
Verify the exact model, routing and settings
Two models in one product can have different retention, provider roles and governing terms. Microsoft documentation gives an example in which some Anthropic model scenarios operate under Microsoft terms with the model provider acting as a subprocessor, while certain models with Data Retention require explicit enablement and Anthropic’s separate terms. The documentation states those models are not subject to Microsoft’s Customer Agreement and DPA. Confirm the model, region, tenant setting and provider terms in your deployment rather than generalizing from the product name.
Put the configuration in an exhibit or other auditable record: model identifier and version, endpoint, region, training or retention switches, administrator roles, connected data sources and change-notification requirements. Require notice before a material routing or terms change, with a right to disable the feature or terminate the affected service.
Require measurable security and incident assistance
Security language should describe controls and evidence, not simply promise “industry standard” protection. Align the contract with your risk assessment and require at least:
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- Data minimization, pseudonymization where appropriate, tenant isolation and secure deletion.
- Vulnerability management, penetration testing or equivalent assessments, resilience, backups and restoration objectives.
- Logging, monitoring, personnel confidentiality and access-review procedures.
- Incident notification deadlines, a description of affected data, containment and remediation updates, and cooperation with your regulator and affected individuals.
Require assistance with data-subject requests, breach assessments, impact assessments and regulator inquiries. State who performs the technical search, the response time, cost allocation and the format of returned information. A vendor that merely forwards a request to a generic support queue may not meet your statutory deadline.
Define audit rights and acceptable evidence
Decide how you will verify compliance before signing. Options include direct audits, independent assessments, certifications, penetration-test summaries, control reports and responses to a tailored questionnaire. Specify frequency, notice, confidentiality, assessor qualifications, remediation plans and who pays. Require evidence for the service and region you use, not only a parent company’s general report.
Vendor agreements often limit on-site audits and substitute third-party reports. The OpenAI DPA example permits audit cooperation under stated conditions and allows third-party audit or certification summaries where law permits. Check what happens when a report is unavailable, a control exception is material or the vendor refuses reasonable evidence. Include escalation, remediation deadlines and a termination or suspension right for unresolved high-risk failures.
Compare contract positions on the same scenario
Use one written scenario for every vendor or plan: the same data categories, users, model, region, retention need and support level. Record the answer and the clause that supports it.
| Decision axis | Questions to answer | Evidence to retain |
|---|---|---|
| Reuse | Can content, outputs, feedback or derived data be used for training, evaluation, safety or general improvement, including after deidentification? | Clause numbers, product setting and effective date |
| Retention | How long are active data, logs, backups and safety records kept? What are the legal exceptions and deletion-confirmation process? | Service schedule and deletion certificate procedure |
| Provider chain | Who receives the data, where, for what function, with what notice and objection remedy? | Current subprocessor list and change notices |
| Assurance | Can you audit directly or obtain suitable reports, and what remediation follows a failed control? | Audit clause, report type, timing and escalation path |
| Model governance | Which provider’s terms govern each model, feature, region and retention setting? | Model inventory, routing diagram and governing terms |
| Responsibility | Who assists with requests and incidents, and what liability or service remedy applies to vendor and subprocessor failures? | DPA duties, SLA, indemnity and liability provisions |
Follow a practical review workflow
- Inventory the use case. List users, data subjects, fields, attachments, integrations, jurisdictions and the business purpose. Classify prohibited data before a pilot begins.
- Freeze the configuration. Record model, region, tenant, retention and reuse settings, administrator permissions and any human-review option.
- Collect operative documents. Obtain the master agreement, DPA, service and model terms, security exhibit, subprocessor list and current settings documentation.
- Mark unresolved permissions. For each data type, highlight training, evaluation, benchmarking, analytics, safety monitoring, feedback and derived-data language.
- Negotiate measurable clauses. Add purposes, retention periods, deletion evidence, notice periods, audit deliverables, incident timelines and remedies rather than relying on policy summaries.
- Test the exit plan. Ask for export format, deletion timing, backup expiry, legal holds and subprocessor confirmation. Run a small deletion or export exercise if the service supports it.
- Approve with owners. Assign legal, security, privacy and business owners to each control; schedule a review when the model, region or terms change.
Understand the limits of model contract templates
The European Union’s model contractual AI clauses were published as voluntary tools for public organizations procuring externally developed AI. The accompanying page says they should be customized, do not replace the GDPR or other applicable obligations, and do not constitute a complete contract. Use them to identify topics such as transparency, risk management and provider duties, then add a DPA, security terms, retention schedule and remedies appropriate to your transaction.
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The ICO material is UK guidance framed around UK GDPR expectations. Neither it nor a public-sector EU template determines the correct answer for a private buyer, another jurisdiction or a particular model. Your obligations depend on the data, role, transfer route, service and configuration actually used.
Red flags that require clarification
- A promise not to train that omits outputs, feedback, metadata, human review or derived data.
- “Delete on request” language without a period, backup rule, legal exception or confirmation.
- A subprocessor page that can change without notice, objection rights or a replacement remedy.
- Security claims with no control description, incident deadline or evidence format.
- Terms that apply to a product family while your selected model or region is governed separately.
- Deidentification language that lacks a standard, reidentification prohibition or limits on general improvement use.
- Audit rights limited so heavily that you cannot obtain evidence for your regulator or customers.
Pause deployment when a red flag affects sensitive or regulated data. Preserve the proposed data map and ask the vendor to identify the exact clause, setting and subprocessor that resolves it; do not infer an answer from a sales presentation.
When to obtain legal and specialist review
Have qualified privacy counsel review the actual agreement when data is regulated, children’s, health, financial, employment-related or subject to confidentiality duties; when processing crosses borders; when the vendor uses data for model improvement; or when liability, audit or deletion terms cannot be negotiated. Counsel should work from the recorded data flow and configuration, not a generic product brochure. Security, procurement and records-management teams may also need to approve controls, retention and exit evidence.
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A signed contract is only one control. Keep the model inventory, settings, subprocessor notices, audit reports and deletion records with the agreement, and reopen the review whenever the vendor adds a model, changes routing, introduces a retention feature or revises the applicable terms.
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