Use Cases For Data Masking
Data masking is a method of protecting confidential data by transforming sensitive data values into convincingly realistic values for the purpose of de-identification. Masked data cannot be reverse-engineered to reveal the original data values.
Organizations employ data masking for various purposes ranging from internal access control and maintaining customer privacy to compliance and managing security.
Data masking is a data security method used to anonymize an organization’s sensitive data. Should this data be leaked or breached, it cannot be used to identify an individual or in fraudulent transactions.
Teams often need functional data sets for testing purposes. Masked data retains the integrity needed for testing without compromising actual user data. Automated data masking allows teams to secure and release masked test data to non-production environments quickly for this purpose.
Data masking is used to anonymize sensitive data to ensure internal personnel who are not authorized to view certain information are prevented from doing so. If a member of your organization requires data for a legitimate reason, but is not authorized to view credit card details, for example, masking the last four digits of a card number prevents exposure to those sensitive elements.
Manually cleaning data is tedious, time-consuming, and slows down production. Automated data masking integrates with existing systems and removes any manual steps to allow teams to develop and test against real data quickly without disruption.
Once data has been masked, it remains masked in transit and in the cloud. Should that data be breached, it cannot be used to identify individuals or for fraudulent purposes.
Privacy is important to many people, which is why organizations need to take measures to ensure confidential client data and personally identifiable information (PII) is protected, particularly in scenarios where an organization shares data with third parties.
Data privacy regulations like the GDPR, HIPAA, GLBA, PIPEDA etc. are intended to ensure the safety, security and confidentially of PII. Implementing data masking as part of the development life cycle removes identifiers (including the 18 identifiers identified by the HIPAA Safe Harbor method) from data before it can be shared within an organization and meets the principle of Privacy by Design, as required by Article 25 of the GDPR.