Data masking techniques have become a major topic of interest in recent years, given the push for better consumer privacy regulation in many territories around the world.
If you are interested in learning about masking production data within your own enterprise, then this article should be a great place to start.
- What is Data Masking?
- Why Use Data Masking?
- Data Masking Types and Techniques
- Data Masking Implementation Tips for Regulatory Compliance
- Frequently Asked Questions (FAQs)
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What is Data Masking?
Data masking refers to any process by which data is modified or replaced to keep its original values hidden. A few examples of these processes include:
- Redaction
- “Scrubbing"
- Pseudonymization
Why Use Data Masking?
Well-known large-scale legislation like the General Data Protection Regulation in Europe (GDPR) and the Children's Online Privacy Protection Act in the US (COPPA) encourage companies to use less consumer data whenever possible.
However, regulation is only one of many reasons to adopt data masking practices. Data breaches are often costly affairs for companies to handle, averaging as much as $4 million in liabilities across incidents.
Masking data can prove to be a much more cost-effective risk mitigation technique than many other encryption strategies, and it's certainly cheaper than an unexpected breach.
Data masking is all about replacing production data with structurally similar data. This being a one-way process makes retrieving the original data all but impossible in the event of a breach.
With their trust layer (that includes audit trails, toxicity detection, data masking, etc.) Salesforce is promising productivity and innovation without letting your secrets out of the bag. pic.twitter.com/7MRRHl0XGf
— gustavo (@GustavoChavezCS) June 24, 2023
We will dive into the details surrounding certain data masking processes below and discuss a few implementation tips worth considering.
Data Masking Types and Techniques
The following data masking types each serve separate needs by modifying sensitive data at different stages of its use:
Static Data Masking
This approach to data masking centers on preprocessing. Sensitive data is either removed entirely before it is transferred to testing environments or is replaced with structurally similar but ultimately illegitimate values.
What makes this type of data masking unique is that all the masking is handled before the modified data set is made available to third parties. The original source data is kept safe, and the modified copy is pushed out as needed.
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Dynamic and "On the Fly" Data Masking
Both on-the-fly and dynamic data masking modify data in transit. In the case of the former, data is masked as it moves from the secure production environment to an insecure development or testing environment.
Dynamic data masking skips storage of the modified data altogether as it simply streams masked data to secondary environments on an ad-hoc basis.
Here are eight actual techniques commonly used to mask sensitive data in production systems:
1. Shuffling Data
Shuffling allows for data from a specific row's columns in a given database table to be assigned to another row's matching columns randomly. Assuming this is done thoroughly so that no rows are left with their original data, it can be quite effective.
2. Substituting Data
Here, data is simply substituted using a set of boilerplate replacements. This is a fairly simple approach, yet it is also highly effective as the original data is completely masked and cannot be retrieved by any means without access to the source.
3. Scrambling Data Although scrambling may sound similar to shuffling, there is a key difference: individual characters in textual data are moved around as opposed to entire text values being assigned to different rows in a given database table.
For a bit more information about how this works, check out the following video:
Data Masking Implementation Tips for Regulatory Compliance
Here are a few things you should know if you want your data masking implementation to be compliant with certain industry guidelines and regulations, state data breach laws, and international laws.
Payment Card Industry Data Security Standards
Ensuring your data-sharing practices are in line with PCI DSS guidelines involves careful management of cardholder data across your entire network.
Requirement 3 of the PCI DSS points out that such information must be encrypted to keep unauthorized parties from accessing it.
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Health Insurance Portability and Accountability Act (HIPAA)
Health information is another major cause for concern among companies looking to keep their systems compliant with critical regulations.
Due to the nature of HIPAA and its requirements, you must ensure individuals are able to access their own unmasked data if they choose to. This means masking techniques that cannot be undone are not an option in production.
California Consumer Privacy Act (CCPA)
The CCPA governs the usage of personal information by companies with access to consumer data. Masking any data not needed for business purposes is a good first step towards complying with some of the more stringent portions of the Act.
Mitigating data breaches and complying with an ever-evolving set of international regulations can be challenging when your business depends on customer data to reach its performance objectives.
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Leverage data masking tools and techniques to tackle the growing body of issues concerning data usage around the world while preserving the real business value of the information you have on hand.
