Masking sensitive test data has improved compliance and now enables safe, realistic debuggingThe best features DataMasque offers that stand out to me are the flexibility to create rule sets and mask UUIDs, which have been really beneficial. Additionally, their customer service and technical support have been very helpful in getting this set up. The flexibility in creating rule sets has helped my team specifically because we have lots of dependencies on different databases and different database types. We have an RDS instance and an OpenSearch instance. Having those rule sets really allowed us to connect data between the two because the same data can be across multiple datasets. Creating these rules enabled us to not think too much about which ones need to be unique or which ones need to be the same across different tables. Whenever we run into issues or questions, we are able to talk to the DataMasque team, and they give us a reasonable technical response within a good timeframe that allows us to solve our issues pretty quickly. We have not had huge delays or blockers because of the communication between us and DataMasque. I appreciate the consistency to continue to improve the application, and there are new features coming out this year that will be tools we use. DataMasque is doing a great job of adding new features, and we will definitely be using them in the future. DataMasque has positively impacted our organization by already showing visible benefits in security and reduced risks for compliance, even though we are still in the process of setting up production and using it to the full extent. It will allow our developers to troubleshoot issues easily without the risk of viewing PHI or anything similar. We are seeing the benefits already of that, but we still need to complete the full integration and incorporate it into our workflows to see the full benefits, which we are currently in the process of. So far, it has been really good. By just masking production data, we can go into this masked database set to understand edge cases and issues as they come up from support without the developers having to look at any production data. They can go into this masked dataset and see the problem firsthand, something we would not have caught with our dev dataset because it is such a smaller amount of data, whereas production has a vast amount of data that we can look through.
