Scenario: A financial institution wants to use customer data for internal analysis without exposing sensitive information. How can data masking and anonymization help achieve this goal?

Data masking involves hiding or obscuring sensitive information in a dataset, allowing internal analysis without revealing the actual data. Anonymization goes further by replacing identifiable information with pseudonyms or codes, ensuring privacy even during detailed analysis. Encryption secures data during transmission or storage but may not allow internal analysis without decryption. Deleting sensitive data would defeat the purpose of using it for analysis.