In today’s data-driven world, organizations collect and store sensitive information in massive volumes: PII, social security numbers, credit card details, health records, etc. That’s why stringent governance policies are put in place and ensured by authorities like the GDPR and CCPA to protect such data from being breached or leaked. Still, the risk always remains. In light of this, data masking is rapidly being recognized as a means for organizations to share realistic, nonsensitive data for development, testing, and training without compromising security.
This article will guide you step-by-step on how to carry out data masking within your organization, along with best practices, the different masking techniques, and available tools to perform this critical security step.
What is Data Masking and Why Use It?
Data masking is a process in which sensitive data is transformed but the original data structure and functions remain. Therefore, the original data values are hidden either fully or partially. This masked data could be used for other purposes, such as software development and testing, data analysis and training, and sharing with third parties.
This would empower developers and testers to use actual data sets without getting hold of correct customer details. Data scientists and analysts can build models and practice training without compromising privacy. Likewise, software companies can securely share anonymized data with partners while adhering to compliance regulations.
We can generically classify the data masking techniques into the different types which has their own respective strengths and weaknesses. For instance, in tokenization original information is substituted with unique tokens, while its format remains the same. In shuffling, it mixes up or rearranges characters of a field in the data in a random manner. In the encryption technique, information is enciphered with a certain cryptographic key so that it becomes difficult to understand the information without the aid of the correct decryption key.
Benefits of Implementing Data Masking
Effective data masking offers a multitude of benefits for organizations:
- Enhanced Data Security- It helps you conceal certain data from plain sight in case you are faced with a data breach situation.
- Improved Compliance- Data masking can be used to address the legal regulation requirements such as the GDPR and CCPA by hiding the sensitive information.
- Facilitates Development and Testing- Masked data enables developers and testers to use actual data for testing and developing within their applications or programs without compromising on the value and security of the data.
- Enables Secure Data Sharing- Companies can evaluate and share anonymous data with other organizations or perform statistical analysis on them without any concerns about data breaches.
Implementing Data Masking- A Hands-On Guide
Data masking is a complex method that needs to be implemented properly. The first phase involves data identification and categorization. The organizations have to evaluate all private data that is stored in their systems and categorize it according to its sensitivity level to high, moderate or low. This is helpful in identifying which masking approach should be used on each of the data types.
The second step is defining the masking policies. There should be clear policies that indicate what is to be masked, how it is to be masked, and the purpose of doing so. Such policies, then, have to match with the regulations of data governance and compliance with which the Data Privacy Act can be applied.
Of course, the selection of the correct data masking tool is a prerequisite for successfully implementing data masking. Scalability, ease of use, data type support, and integration with the current infrastructure are some of the considerable variables during such selection.
Once the tool is selected, data masking processes can be initiated. This is associated with how data masking integrates into development and testing workflows. Automation is recommended whenever possible to ensure consistency and efficiency.
Data quality and testing is the next step that is considered to be of utmost significance. Masked data requires the confirmation that while the data remains masked and continues to operate as it did in the beginning, it still serves the intended purpose. There should also be periodic testing to ensure that the algorithms for masking are unbreakable. In this way, it ensures that the applied masking algorithms are sufficiently protected. It is natural that if the base algorithms applying the masking functions are well protected with effective access control mechanisms, then it is possible to limit the constantly increasing threats of reverse engineering.
Data masking is one of the most important methods for data protection and security. All these best practices and proper selection of tools can go a long way in helping organizations to create a strong data masking framework within the fast growing world of big data. This can help to offer good security, facilitate development and testing, and also help in building trust. It must be remembered that data masking is a continuous process; one has to learn and unlearn, watch and adapt to new strategies and methods to stay ahead of evolving security threats.