Secure & Compliant Test Data Management
Data privacy transforms test data management
Data privacy laws have changed the landscape of test data generation and management. Companies using production data with sensitive structured data in test/dev environments are NOT compliant with the GDPR, CCPA, KVKK, and other global data privacy regulations.
Test Data Management and Data Privacy now share the same issues and processes.
Voltage Data Privacy Manager automates data privacy and protection of sensitive, regulated production data for use in test development pipelines for functional app testing, training, QA, and related use cases.
Discover private data in databases
To help ensure data privacy, businesses must first understand their data and map their sensitive data sources and data flows. Then protection policies for testing, QA and development can be applied. Data Privacy Manager finds sensitive structured data in active and inactive systems across the enterprise.
Enable data privacy in non-production environments
Generating non-meaningful test data for performance testing is not a difficult exercise but generating meaningful data that looks and behaves like real production data for functional testing is the challenge. Meaningful data contains all the characteristics of production data, such as format, context, and referential integrity, but is anonymized for data privacy compliance. Building a test database with meaningful, protected test data allows the application owner to see and assess how the application will perform once it released. Without meaningful test data in the test environment, it is impossible to predict the way the application will behave after the release.
Anonymize the data for secure use
Test Data Management is the ability to create non-production data that is a realistic simulation of the actual production data. Reproducing production data with realistic but anonymous data to protect customer information. Anonymize the Data for Secure Use.
Ensure that the real sensitive data is secure
There are numerous approaches to keeping sensitive data protected from exposure. Transforming data at use so the end-user never sees real data is appealing but is not secure because any access outside of the application will see real data. Recent data breach cases have clearly shown that the best method is to persistently protect data by default—at rest, in motion, and in use. This means protecting the data as soon as possible—preferably at entry—and then leaving it protected except for the few cases where the real data is truly required.
The loss of customer trust remains the biggest concern
Failing to protect customers' data has many negative consequences for organizations. Respondents to an Osterman Research survey, Privacy Compliance in the United-States: Status and Progress in 2022, indicate that protecting personal data is essential for a range of reasons, with avoiding loss of customer trust the highest-ranked (76%), followed by avoiding loss of corporate reputation (74%).
Avoiding loss of customers trust
Avoiding loss of corporate reputation
Avoiding loss of the value of our company
Avoiding regulatory fines for non-compliance
Avoiding loss of corporate brand value
