When it comes to data, you want to make sure that this is kept safe, secure, and out of irresponsible hands. This sensitive information is crucial not only to better business processes but also to make sure that identities are protected. That’s where the method of data masking is helping businesses across all industries better protect the sensitive data of their customers and of their inner mechanisms alike.
Data Masking Methods
You might be asking yourself, what is data masking? It’s the data security technique that scrambles data to create an inauthentic copy for various non-production purposes. This retains the characteristics and integrity of the original production data while helping organizations minimize security issues. This masked data is then used for analytics, training, or even testing. Data masking is commonly seen across five different methods: substitution, shuffling, averaging, redaction and nulling, and format-preserving encryption.
In the substitution method, the original data value is replaced with an inauthentic value, making sure however that the format of the inauthentic data is the same as the original. In the shuffling method, values are vertically shuffled in a column of a database, randomize so that the aggregate value stays the same, but isn’t dispelling authentic data. Averaging replaces all numerical values in a table column with an average value, making it impossible to find individual datasets. Redaction & nulling is straightforward, replacing information with a variable like X to mask the data. Format-preserving encryption turns data into an array of incomprehensible symbols.
Data Masking Workflow Options
There are a few options for addressing workflow while maintaining data integrity and confidentiality in these masking methods. In static data masking workflow, for example, a copy of the original data is made and masking is applied to the copy. Through extract-transform-load (ETL) masking, information is extracted from a production database. This creates an exact copy extracted for select queries. Through in-place masking, data is masked within the original database, eliminating the need to extract and load information from original content.
Dynamic data masking is applied to a copy of the data whenever the system receives a user request. View-based data masking is based on the access rights of an end-user. In this technique, when a user requests data, they may get a masked view of the original data. Proxy-based data masking is the newest method that has emerged to address workflow. In this model, all the data requests go through a proxy system, which runs the data masking as a service. This is to protect the data in the event of hacking or any unauthorized access.
Rules of Masking
Organizations need to adhere to certain standards and privacy regulations when it comes to data masking. For one, once a data masking technique is used to transform authentic data, it should be impossible to retrieve the original data from the masked sources. If the data can be reversed, this is a significant security issue. The masking technique should not alter the nature of the data either. Transformations should be used in such a way that geographic distribution, readability, and other distributions of the original data are preserved.
Data masking should not affect the integrity of the database, especially when protecting personal information like credit card numbers or social security numbers. Masking does not necessarily cover every field of a data record. Sensitive information may be covered up, but other information may still be on display that isn’t as impactful. Masking methods should be automated, as this is not a one-time process with production data constantly changing. Data masking is as much about security and safety as it is effectiveness and efficiency.