Overview
The main objective of this project is to Implement the Redgate Data Masker tool for Oracle/MS SQL Database to protect the sensitive data within the organization also simultaneously maintaining its usability for the development and testing team. In the process, data masking replaces original data with functional fictitious data so that it can be used safely in situations where original data is not needed.
This process helped us to protect the sensitive information of a Corporate Banking institution for their many business & Personal critical information, such as (and many more)
- Personally identifiable information (PII).
- Loan Account information.
- Social Security Number (SSN).
- Intellectual property data(IP).
- Mobile/Land/Address/Zip details.
Using the data masking concept, we have changed the data value while keeping the constant formatting of the Original data. For example, we can understand as below The Loan account ID is in 6-digit format, Let’s say: 123456, and masking data changes the numbers, but maintains the same 6-digit format. Using the example above, the masked Loan account ID could become: 456123(using shuffling) 1239999(using prefix), or 999456 (using postfix) based on the masking rule set.
Here we note that data masking uses several methods to alter Original sensitive data, including character or number substitution, character shuffling, or the use of algorithms to generate random data that has the same properties as the original data.
While chose Redgate data masker, which we have chosen as a masking tool that has the masking capability to mask Oracle as well as Microsoft SQL server databases.
Masking Workflow
Masking Methodology
This is where a masking concept has been implemented to protect the originality of data and result as final execution to transform the Original data into masked data.
Data Masking & Process Automation Architecture
In a new process of masking and automation, We were able to achieve the protection of the Originality of data and same time have a more efficient overall process of automation using shell command.
Business Recognition
By implementing the data masking we have made sure the copies of production data for non-production use are more sure and noninformative w.r.t the original data. These data has been widely used for below purposes
- Personnel training
- Application development and testing
- Development & testing dummy Reporting
- Business analytics modeling
Same time it has helped to protect against insider threats and comply with General Data Protection Regulation to strengthen and unify personal data protection & compliance.