A software development team needs to test a new application feature using realistic data that reflects production scenarios. The production database contains sensitive Personally Identifiable Information (PII) and is subject to strict data privacy regulations. Which of the following is the MOST appropriate method for creating a compliant test dataset?
Create a sanitized copy of the production database using data masking techniques to anonymize PII.
Temporarily grant the QA team read-only access to the live production database for testing.
Use a full, unmasked copy of the production database in an isolated test network.
Generate a small set of manually created, synthetic data records.
Using live production data containing PII in a non-production environment violates data privacy regulations like GDPR and HIPAA. The most appropriate method is to create a sanitized copy of the data using techniques like data masking, anonymization, or pseudonymization. This approach preserves data realism and referential integrity for testing while protecting sensitive information. Using an unmasked copy, even in an isolated network, is non-compliant and risky, as test environments often have weaker security controls. Granting direct read-only access to the production database for testing purposes is a highly insecure practice that violates the principle of environmental separation. Manually creating a small set of synthetic data is a compliant option but often fails to accurately represent the complexity and scale of production data, making it less effective for achieving realistic test outcomes.
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What is data masking, and how does it protect PII?
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What is the difference between anonymization and pseudonymization?
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Why is using synthetic data less effective for testing?