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Answer: Utilize dynamic data masking by creating views that conditionally mask sensitive data based on user roles and permissions, allowing authorized users to see unmasked data for analytics.
Option B is the most appropriate because it leverages dynamic data masking to conditionally hide sensitive information based on user roles, ensuring compliance with GDPR and data privacy requirements without sacrificing the ability to perform analytics. This approach is scalable, cost-effective, and minimally disruptive to existing workflows, as it does not require data duplication or exclusion of columns, nor does it introduce the performance overhead associated with full dataset encryption.
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In a scenario where you are tasked with implementing data masking for a dataset containing sensitive customer information, such as social security numbers, to comply with GDPR and ensure data privacy, while still enabling authorized users to perform analytics. The solution must be scalable, cost-effective, and not disrupt existing analytics workflows. Which of the following techniques would be the MOST appropriate to achieve these objectives? (Choose one option)
A
Implement static data masking by creating a copy of the dataset with sensitive data permanently altered, making it available for analytics without the original sensitive information.
B
Utilize dynamic data masking by creating views that conditionally mask sensitive data based on user roles and permissions, allowing authorized users to see unmasked data for analytics.
C
Exclude sensitive columns entirely from all datasets and views, ensuring no access to sensitive data but potentially limiting analytics capabilities.
D
Apply full dataset encryption, decrypting data only for users with the highest access levels, which may introduce performance overhead and complexity in analytics workflows.