AI Text Analysis and Privacy: A Customer Feedback Checklist

Open survey responses and support tickets often explain why customers are satisfied or dissatisfied. They may also contain personal information. Before uploading these texts to an AI system, teams should establish which data they need, where it goes and who can view the results.

This guide is for research and CX teams analyzing feedback text. It describes an organizational review process; assess the specific processing activity with your responsible privacy professionals.

Start with a specific question, such as: “What problems do customers report with appointment scheduling?” Identify the necessary sources and describe the route from export to shared report.

• Source: Which free-text fields, identifiers and additional attributes are exported?
• Preparation: Which details can be removed beforehand?
• Analysis: Which systems and service providers process the text?
• Storage: Where are originals, processed text, results and logs kept?
• Sharing: Who receives results or original quotations?
• Completion: When are data deleted or access permissions removed?

Processing personal data requires an appropriate legal basis. Review purpose limitation, data minimization and information duties; consider Article 9 for special categories of data. A processing agreement is required when a provider acts as a processor. A data protection impact assessment is necessary where processing is likely to create a high risk. International transfers require assessment under Chapter V. An EU server location does not answer every question about recipients and access. See the GDPR, particularly Articles 5, 6, 9, 13/14, 28, 35 and 44 onwards.

Record open issues, owners and completion dates. A broad product label does not replace this assessment.

Names, email addresses and contract numbers are often not what a topic analysis is trying to uncover. Review the usefulness of every additional field. Check personal details inside the text too.

Automated masking can support preparation. Test whether remaining details still identify people and whether important meaning is lost. The guide to anonymizing text data provides a concrete example workflow.

• Which systems perform analysis and storage?
• Which subprocessors are involved, and what access is possible?
• Are inputs used for model training? Which contractual terms and technical settings apply?
• What information is retained in logs and backups?
• How do permissions, export and deletion work?
• What changes when the team shares a report with external recipients?

Ask for current documentation covering the actual package offered. On-premises deployment, particular certifications and specific deletion periods cannot be assumed for every AI product.

A report can contain personal information through original quotations or very small groups. Review recipients and the level of detail. An internal analysis team may need different information from the audience for a management presentation.

In a fictional example, a team summarizes a complaint instead of sharing a quotation containing a unique customer description. It labels the text as a summary. Whether that is sufficient depends on the specific content.

• The analysis purpose and required data fields are documented.
• Privacy questions and responsibilities are resolved.
• Provider documentation matches the actual data flow.
• A test set has been reviewed for identifying information and analysis quality.
• Permissions for data and reports are defined.
• Retention, deletion and project closure are organized.
• Changes to the dataset or procedure trigger another review.

Start with a clear question and synthetic test data or data approved for the intended purpose. The Cloud product page explains the analysis workflow; the security page is a starting point for assessing the service. For a new data collection project, explore surveys in the deepsight Cloud. In a demo, discuss the data and requirements of your particular use case.