For decades, businesses have been protecting data privacy to build customer trust. Fast forward to today, it is no more a choice but a regulatory mandate that can attract stringent action in case of no compliance. With regulations like GDPR and CCPA, enterprises must take extra measures to protect data privacy in their test environments.
As more companies rely on technology to power their businesses, the importance of test data management has become increasingly crucial.
In this post, I share best practices and critical techniques to ensure privacy in test data management, including best practices for data masking, data subsetting, and data scrambling. By implementing these techniques, companies can minimize the risk of data breaches and maintain customer trust.
What is Test Data Management?
Test Data Management (TDM) manages the data used for testing software applications. This includes identifying, creating, and managing test data sets that are representative of the production environment.
However, as test data management involves working with often sensitive or confidential data, ensuring privacy in TDM is critical to protect the information. This has to be achieved while still providing developers with the necessary data to test their applications.
The changing TDM landscape due to privacy laws
To ensure relevant application testing covering various use cases, many companies use real production data, which typically includes sensitive personal information.
Data privacy laws such as GDPR, CCPA, and KVKK are there to protect citizen data, even when used internally, and prohibit its misuse. However, outsourcing testing, Quality Assurance, and training processes to third parties in different countries may expose citizen data to unauthorized users, violating data privacy laws.
Businesses must identify, classify, and protect every data element according to user population and policies to enable data privacy. All environments must be considered when handling data.
A data breach can affect a customer’s data in any environment, whether a production database, an archive, or a test database. Such incidents can result in non-compliance fines and penalties for the business.
It’s essential to have the ability to demonstrate that data is non-reversible and to respond to privacy auditors’ inquiries by verifying that test and analytics data values are genuinely anonymized and irreversible. These are crucial data privacy management capabilities.
Using a Test Data Repository to Shield Real Data Sources
To ensure secure testing and minimize the workload on data sources during testing, organizations can use a Test Data Repository containing realistic but fake data to protect actual production data from unauthorized access. Data Privacy Manager provides this repository to shield authentic data sources and ensure data privacy.
The Test Data Repository stores the following types of meaningful generated data:
- Generated randomly from file-based or SQL-based dictionaries.
- Shuffled randomly from the SQL-based dataset.
- Select randomly with preserved context, generate a feminine name if the sex is female and a masculine name otherwise. Create a meaningful address based on the ZIP code, and so on.
- Generated algorithmically using random anonymization or user-defined transformation.
- Ensure consistent data by generating the same random value for a given input, enabling consistency across different data stores.
Remove or replace personal or sensitive information. One approach uses synthetic or pseudonymization to substitute identifiable information with fictitious or random data.
Anonymization safeguards individual privacy and compliance with privacy regulations like GDPR or HIPAA by protecting personal data used for testing.
Provision of test data by business entities
Data management solutions, be it fabrics, mesh or others, have been trying to utilize micro-databases for optimal data provisioning. Off-late, K2View’s patented solution enables storing business entity data in an exclusive micro-database. Their test data management tool collects data from production sources and arranges it based on the main business entities, such as customers, orders, products, etc.
The data for each entity undergoes masking, compression, and encryption while being processed and is then stored in a dedicated micro-database. This feature helps DevOps teams to achieve faster time-to-market.
The micro-databases are kept in a test data warehouse that DevOps can access through an API and by testers through a self-service, web-based application.
Use a secure test environment.
A secure test environment limits access to authorized personnel by using dedicated, separate environments. Security controls like firewalls and access controls can be configured to prevent unauthorized access and protect sensitive test data.
Implement access controls
Implement access controls to limit data access using role-based, IP or user account restrictions. Regularly review and audit these controls to maintain data access by authorized personnel only.
Secure data transmission
Enterprises should securely transmit test data to the test environment using encryption or secure file transfer protocols like SFTP or FTPS to prevent unauthorized access or interception. Encryption safeguards data in transit and prevents unauthorized reading.
Monitor data usage
To maintain privacy in TDM, organizations should monitor test data usage and access effectively, which can be done by implementing audit trails and logging mechanisms. Audit trails document all test data actions while logging records, data access and timing.
Monitoring data usage enables the timely detection of any unauthorized test data access or usage, which can help prevent data breaches.
Dispose of data securely
Securely dispose of test data after testing by either deleting the data or using a data destruction tool. Deletion makes data inaccessible, while data destruction tools erase data permanently and render it irrecoverable.
Ensuring privacy in Test Data Management is critical to protect sensitive information while providing developers with the necessary data to test their applications. By following these best practices, organizations can mitigate the risks associated with working with sensitive data and ensure that privacy is maintained throughout the testing process.
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