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Protect customer privacy by collecting only the data needed for a clearly explained personalization purpose, limiting who can access it and how long it is kept, and checking whether your AI provider retains or reuses it. Make sure your customer-facing explanations match the system’s actual behavior. Also assess whether the feature only recommends products or affects consequential outcomes such as eligibility, price, or access; those uses can raise different legal requirements.
Start by defining what the personalization is for
Before connecting customer data to an AI feature, state the specific purpose in plain language—for example, recommending products based on a customer’s browsing and purchase history. Then decide which inputs are necessary for that purpose. Treat collecting less data as a design choice, not something to address only in a privacy notice.
Map the data the system uses
List each input and record where it comes from, why the feature needs it, and whether it is sensitive or inferred. Common inputs include:
- Account and transaction data
- Browsing events
- Customer preferences
- Inferred interests
- Third-party data
For each item, ask whether the recommendation can work without it or with a less revealing alternative. Do not assume that data is appropriate to use simply because the business already holds it.
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Keep separate purposes separate
Product recommendations, advertising, analytics, model training, and other forms of personalization are distinct uses to assess. Do not quietly expand a recommendation feature into those other purposes. Assess them separately and explain them transparently where they are used.
Check what the AI provider does with customer data
A privacy promise to customers is only accurate if it matches the vendor’s data handling and the settings actually enabled. Ask the provider how it handles:
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- Retention periods and deletion, including what happens to data after a customer or retailer requests deletion
- Use of customer data for model training or other reuse
- Subprocessors and who can access the information
- Security safeguards and incident handling
Review the provider’s terms, contract, and product settings together. If the provider may retain or reuse information, account for that in the customer explanation and in your decision about whether the service is suitable. The US Federal Trade Commission warns AI providers and businesses to honor privacy and confidentiality commitments; it cautions that using or retaining consumer information for other purposes without clear and conspicuous notice and affirmative express consent can create legal risk. That is FTC guidance, not a complete account of every US federal or state privacy requirement.
Set protective defaults for retention, access, and security
Privacy protection involves operational controls as well as disclosures. The European Commission’s GDPR guidance describes data protection by default as processing only what is needed for the purpose, keeping it for the shortest possible time, and limiting access to people who need it. It also identifies pseudonymization and encryption as examples of technical and organizational measures to consider early in system design.
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Keep data only as long as it is useful for the stated purpose
Choose the shortest workable retention period for each input, and make sure deletion processes cover relevant systems and provider-held data where applicable. The exact period depends on the purpose and applicable requirements; a privacy policy alone does not implement a retention limit.
Limit access and protect identifiers
Use least-privilege access so employees and service providers can reach only the data their work requires. Pseudonymization or encryption can reduce exposure, but they are safeguards rather than guarantees that information cannot be linked back to someone. Treat data as personal if it remains reasonably linkable to a person.
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Consider what the system can reveal
Review whether recommendations or their underlying data could expose sensitive characteristics or recreate information thought to be anonymous. The UK Government Data and AI Ethics Framework warns that data linkage can re-identify people and that AI systems may memorize personal information. Do not describe data as anonymous or a safeguard as effective unless that claim reflects the system and its risks.
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Tell customers what information is collected, why it is needed, how AI uses it, whether it is shared with a provider, how long it is retained, and what choices or controls are available. The explanation should describe the actual feature, not an idealized version of it. If a provider’s training or retention practices change, revisit both the settings and the explanation.
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The FTC also advises businesses to collect only what they need, keep it safe, and dispose of it securely. These are useful general practices, but they do not replace checking the laws that apply to your business and customers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Distinguish recommendations from consequential decisions
Personalized product suggestions and consequential automated decisions are not interchangeable. A recommendation may involve profiling: the European Commission describes profiling as evaluating personal aspects to make predictions, even when no decision is made. But the Commission’s explanation of solely automated decision-making focuses on decisions with legal or similarly significant effects, with safeguards that include human intervention and the ability to express a view or contest a decision in specified cases. This is not a blanket rule that every product recommendation is a consequential decision.
Check what the system’s output actually changes. If it affects a customer’s eligibility, price, access, or another significant circumstance, assess the relevant profiling and automated-decision rules for the jurisdictions involved rather than treating the feature as ordinary merchandising. Requirements depend on both geography and the specific processing.
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For EU deployments, the European Commission published AI Act Article 50 transparency guidelines on 20 July 2026 and states that the covered transparency obligations apply from 2 August 2026. Whether a particular retailer feature is covered depends on the system and its role; do not infer applicability from the fact that it uses AI alone.
Use this implementation sequence
- Define the purpose. Describe the recommendation feature and the customer benefit it is intended to provide.
- Map and assess inputs. Record each data source, its purpose, sensitivity, and necessity. Remove inputs that are not needed.
- Separate uses. Assess recommendations independently from advertising, analytics, model training, and unrelated personalization.
- Review the provider. Check retention, deletion, training and reuse, subprocessors, access, security, and incident handling. Align the contract and product settings with what customers are told.
- Set defaults. Apply the shortest workable retention, least-privilege access, and appropriate pseudonymization or encryption. Consider whether outputs can expose sensitive traits or reveal information thought to be anonymous.
- Explain and provide controls. Describe collection, purpose, AI use, sharing, retention, and available customer choices in plain language.
- Test the impact. Determine whether the output affects only product discovery or also eligibility, price, access, or another significant circumstance.
- Reassess changes. Review the controls when the model, provider terms, data sources, jurisdictions, or use case changes.
Keep legal and operational checks aligned
Official guidance offers a practical baseline, but it does not establish a single privacy checklist that applies identically everywhere. The European Commission’s GDPR guidance, its explanation of profiling and automated decisions, the FTC’s guidance, and the UK Government Data and AI Ethics Framework address different legal and policy contexts. Confirm the requirements for the places where the business operates and the people it serves. Avoid claiming a legal basis, level of anonymity, or human review unless the relevant process genuinely exists.
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