Traditional Data Masking is Evolving to Synthetic Data Masking

To meet the challenge of continuous integration and delivery, enterprise software teams are adopting a new approach to data masking. Many developers and testers have replaced the traditional data masking process with a more automated approach – synthetic data masking.

Masked production data is the industry norm for producing privacy-protected test data. However, there are several problems associated with the process and its subsequent test data outputs.

1. Security risks
2. Slow speed
3. Limited variety
4. Limited volume
5. Limitations by format

(You can read the full article for an in-depth discussion on the limitations of traditional data masking.)

Five Benefits of Synthetic Data Masking

Synthetically masked test data offers many benefits when compared to the use of masked production data.

1. Security: Masked production data requires direct access to the original source database to identify and obscure sensitive information. GenRocket ensures that absolute security is maintained over the original source files because the original files are never accessed by GenRocket.
2. Speed: Once automated, synthetic data masking can produce 100% secure and accurate test data within seconds and generate millions of rows within minutes. This ensures that testing is never delayed by waiting for masked production data.
3. Variety: With traditional data masking, the variety of data is limited to what’s available in the production data subset. However, with the use of synthetic data, dev and test teams can specify any type of data needed for negative testing, boundary value testing, combinatorial testing, or functional testing of business rules in the application under test.
4. Volume: Synthetic data masking offers nearly limitless data volume. A fresh data subset in the required volume can be generated for each test run, ensuring that developers and testers can perform each test with certainty that test datasets were not corrupted by previous test operations.
5. No Limitations: Synthetic data masking removes the limitations of masked production data. It offers almost limitless combinations and permutations of data. It can handle data stubbing, negative and edge case data, 14 supported file types, multiple databases, and more. When testing new systems, platforms, or interconnectivity among systems, synthetic data masking offers far superior flexibility to masked production data.

Traditional data masking is a familiar process the industry has become accustomed to and comfortable with. And until now, there’s not been a better solution to take its place.

Technologies advance and processes evolve. The field of Test Data Management is evolving to become Test Data Automation – the ability to model, design, deploy and self-manage synthetic data for any testing purpose. Synthetic data masking is just one part of this evolution.

Read more about the challenges posed by traditional data masking and why the industry is moving towards synthetic data masking.