Use only the customer data needed for a clearly defined personalization purpose, restrict how long it is kept and who can access it, and make sure your customer-facing promises match what your AI providers actually do. Then check whether the system only recommends products or influences decisions with significant effects on customers.
Start by defining what the personalization is for
“Personalization” can describe several different activities: recommending products, tailoring a page, targeting advertising, analyzing store performance, or training a model. Do not treat them as one blanket purpose. Decide what the feature is meant to do, then assess each use of customer data against that purpose.
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For a product recommender, for example, you might decide that recent browsing and purchase history are relevant inputs. That does not automatically make every account detail, third-party data point, or inferred characteristic necessary. A smaller, purpose-matched data set is a privacy design choice, not just a notice-writing exercise.
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Record each input, where it comes from, why it is used, how sensitive it may be, and whether the feature needs it. Include information passed to an AI provider as well as information retained in your own systems.
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- Account and transaction data: identify which account attributes and purchase details support the stated recommendation purpose.
- Browsing events and preferences: document what activity is captured and how explicit customer preferences affect recommendations.
- Inferred interests: note what the system predicts about a person, not only the raw data supplied to it.
- Third-party data: record its source and purpose, and assess it separately rather than assuming it belongs in the recommender.
For each input, ask whether the feature can work without it or with a less revealing alternative. Keep recommendation data separate from advertising, unrelated analytics, model training, or other personalization unless those uses have been separately assessed and explained.
Check what AI providers retain or reuse
A retailer’s privacy promises need to match the provider’s actual data practices. Before sending customer information to a provider, establish how the provider handles retention, deletion, model training or other reuse, subprocessors, access, security, and incident response. Check both contractual terms and product settings; a setting does not replace a contract, and a contract does not prove a setting is configured as promised.
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Make the answers specific to the data and service you will use. For example, establish what happens to prompts, recommendation inputs, outputs, logs, and other records the service creates. Confirm how deletion requests are carried out and whether any data is retained for a stated operational or security purpose. If the provider’s practices do not support the commitments you plan to make to customers, change the configuration, the contract, the data flow, or the customer-facing explanation before launch.
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Privacy controls should shape day-to-day operation, not sit only in a privacy notice. Set the shortest workable retention period for each data type, and limit access to people who need it for their role. Review whether data is also copied into logs, exports, backups, or other systems with different retention or access rules.
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Pseudonymization and encryption can be useful safeguards, but they do not by themselves make personal data anonymous. The UK Government’s Data and AI Ethics Framework warns that data linkage can re-identify people and that AI systems may memorize personal information. Consider what a model’s outputs could reveal, including sensitive characteristics, and whether information thought to be anonymized could be reconstructed or linked back to someone.
The European Commission describes data protection by default as processing only data needed for a purpose, keeping it for the shortest possible time, and restricting access to people with a need to know. It identifies pseudonymization and encryption as examples of measures to consider early in system design.
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Explain the practice accurately to customers
Use plain language to describe what information is collected, why it is needed, how AI uses it, whether it is shared with a provider, how long it is kept, and what choices or controls customers have. Explain in terms that match the system actually in operation; do not claim that data is anonymous, never retained, or never reused unless those statements are accurate for the complete data flow.
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Determine whether the system is only recommending products
A recommendation is not automatically the same as a consequential automated decision. The European Commission describes profiling as automated evaluation of personal aspects to make predictions, whether or not a decision is taken. Its explanation of solely automated decision-making focuses on decisions with legal or similarly significant effects, and describes safeguards—including human intervention and the ability to express a view or contest a decision—in specified cases. These distinctions matter: do not assume every product recommendation is covered by the same rules, or that calling a feature a recommendation settles the question.
Review what the output changes in practice. If it affects eligibility, price, access, or another significant circumstance, assess the relevant profiling and automated-decision rules for the jurisdictions and customers involved. Consider the actual effect, not just the feature’s label or the fact that a person may be involved somewhere in the process.
Account for the rules that apply in your markets
Privacy requirements depend on where customers are located, what data is processed, and what the system does. The European Commission’s GDPR guidance supports building technical and organizational measures into design and using privacy-protective defaults. The FTC materials describe US guidance on commitments and data security, but do not resolve every state or federal requirement. The UK Government’s Data and AI Ethics Framework offers privacy-by-design recommendations, including transparent explanations of collection, purpose, use, and retention; it is an ethics framework, not a substitute for checking applicable law.
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Quick Recap
Use a launch and change-review checklist
- Define the feature and purpose. Write down what it personalizes and which separate uses—such as advertising, analytics, or model training—are outside that purpose.
- Inventory the inputs. Record each data source, sensitivity, necessity, and use, including inferred interests and information sent to providers.
- Verify provider practices. Confirm retention, deletion, training or reuse, subprocessors, access, security, and incident handling against the service configuration and contract.
- Configure safeguards. Set workable retention limits and least-privilege access; apply pseudonymization or encryption where appropriate; consider what outputs may reveal.
- Prepare an accurate explanation. Describe collection, purpose, AI use, sharing, retention, and available choices without promising controls the system does not implement.
- Assess impact and applicable rules. Check whether outputs influence eligibility, price, access, or another significant circumstance, then assess requirements for the relevant jurisdictions.
- Reassess material changes. Repeat the review when the model, provider terms, data sources, markets, or use case changes.
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