Data Masking and Obfuscation Questions and Answers for Viva

Answer-1: Replacing sensitive data consistently with the same masked value across datasets.

Answer-2: Replacing sensitive data with random masked values that vary each time.

Answer-3: Masking data while preserving the original data format and length.

Answer-4: It reduces the risk of data exposure when using cloud-based development and testing resources.

Answer-5: Substitution, shuffling, encryption, nulling out, and character scrambling.

Answer-6: To protect sensitive information within code or data structures from unauthorized access or reverse engineering.

Answer-7: It intercepts data queries and masks sensitive information before sending the response to users.

Answer-8: Ensuring data usability post-masking, maintaining data consistency, and performance overhead.

Answer-9: By validating that business processes and applications function correctly using the masked data.

Answer-10: Finance, healthcare, retail, and government sectors.

Answer-11: By anonymizing personal data to prevent unauthorized exposure.

Answer-12: Informatica, IBM InfoSphere Optim, Oracle Data Masking, and Microsoft SQL Server Data Masking.

Answer-13: Masking that allows original data to be restored, usually through tokenization.

Answer-14: It allows use of realistic data without exposing sensitive information.

Answer-15: A set of rules that define which data to mask and how to mask it.

Answer-16: Masking hides data while anonymization removes all identifying information making re-identification impossible.

Answer-17: Using consistent masking rules across related fields to maintain referential integrity.

Answer-18: It may cause some performance overhead depending on the masking technique and volume.

Answer-19: By using deterministic masking and preserving key constraints.

Answer-20: Protecting sensitive data when transferring between systems or environments.

Answer-21: Masking data at application layer or using specialized tools supporting NoSQL data formats.

Answer-22: A software component or tool that applies masking rules to data.

Answer-23: Yes, many tools support automation of masking during data refresh or migration.

Answer-24: Protects customer data accessed by multiple tenants or third-party services.

Answer-25: By implementing role-based access controls alongside masking rules.

Answer-26: Masking is primarily for non-production data protection; encryption is for securing data at rest or in transit.

Answer-27: The structure or pattern that masked data should follow, like preserving date or phone number formats.

Answer-28: Randomly rearranging values within a dataset to mask the original data.

Answer-29: Tokenization replaces sensitive data with tokens linked to original data; masking replaces it with fictitious values.

Answer-30: Yes, through techniques like pattern matching and substitution.

Answer-31: To provide safe test data while protecting sensitive information in continuous integration and deployment.

Answer-32: By hiding sensitive data even from authorized users who do not need access.

Answer-33: Data leaks, loss of data integrity, and compliance violations.

Answer-34: Through native masking features, triggers, or external masking tools.

Answer-35: It may reduce the accuracy of analysis if masking alters data patterns significantly.

Answer-36: By enabling safe use of data in disaster recovery environments without exposing sensitive info.

Answer-37: PCI DSS requires masking of credit card numbers when displayed.

Answer-38: By masking or redacting sensitive fields before logging.

Answer-39: Use format-preserving masking to replace all but the last four digits.

Answer-40: Regularly, especially after changes in data usage, compliance requirements, or application updates.

Answer-41: Data masking is the process of hiding original data with modified content to protect sensitive information.

Answer-42: Data masking obscures data for use in non-production environments, while encryption secures data for transmission or storage.

Answer-43: Static masking, dynamic masking, and on-the-fly masking.

Answer-44: Replacing sensitive data in a non-production copy of a database permanently before use.

Answer-45: Masking data in real-time as it is accessed without altering the original data.

Answer-46: Data obfuscation is making data unintelligible or confusing to protect it, often by scrambling or modifying data format.

Answer-47: It helps organizations comply with data privacy regulations by protecting sensitive data in test and development environments.

Answer-48: Properly implemented masking should be irreversible to prevent exposure of sensitive data.

Answer-49: Personally identifiable information (PII), financial data, health records, and authentication credentials.

Answer-50: Masking replaces data with fictitious values, while tokenization replaces data with tokens that map back to the original.