Data masking defined

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data masking is a data security process that transforms information to hide its original content, making it unreadable to unauthorized users. This process is essential for protecting sensitive information, such as personally identifiable information (pii) and financial data, ensuring that it remains secure during activities like development, testing, and analytics.

With data masking, organizations can safely work with real data without risking exposure to those who shouldn't have access to it.

What is static data masking?
Static data masking (sdm) involves masking data at rest, typically within databases. This method permanently replaces the original data with masked values in a copy of the production database, such as a sandbox, creating a non-production environment that can be used safely for testing on an application development platform.

What is dynamic data masking?
Dynamic data masking (ddm) allows for real-time data obfuscation while america phone number list users access the information, ensuring that sensitive data is never exposed in its original form to anyone without the proper permissions.

This approach allows developers and other users who may not have the necessary access to work with data for essential business operations, such as analytics, all while keeping customer information secure. With ddm, organizations can balance the need for data usability with strong protection against unauthorized access.

Data masking vs. Data sanitization
while data masking transforms data to hide its original content, data sanitization involves completely removing or redacting sensitive information from the dataset. For instance, masking might replace real names with fake ones, whereas sanitization would delete or obscure the names entirely.

You would typically use data masking when you need to retain the data's usability for tasks like testing and development, while data sanitization is ideal when sensitive information must be completely eliminated for security reasons.