Masking data, the process of anonymizing personal information with non-sensitive values, is key to ensuring data security and compliancy with privacy laws.
Why More and More Businesses are Masking Data
As the need for faster, more innovative, and more secure data integration rises, more businesses are masking data than ever before.
Today, virtually every aspect of business success depends on the effective use and governance of data. Data masking – or the process of anonymizing sensitive information with non-sensitive values – is integral to ensuring relevant teams can use data securely and compliantly, without creating bottlenecks, or stifling speed.
In this article, we’ll discuss the process of masking data, explain how it compares to other common types of data obfuscation, and cover the 6 most compelling reasons for masking data today.
The Process of Masking Data
The masking of data refers to a data anonymization process in which PII (Personally Identifiable Information) is replaced with obfuscated, yet statistically equivalent, data.
Although anonymized data can’t be identified, or reverse-engineered, it can still be used by authorized personnel in non-production environments, such as data science, software development, and test data management.
Data masking tools are commonly used to hide the data associated with:
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PII: Personally Identifiable Information, in order to comply with data privacy regulations, like GDPR, CCPA, HIPAA, SOX, APPI, DCIA, PDP, and more.
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PCI-DSS: Payment Card Industry Data Security Standard (payment card information)
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PHI: Protected Health Information, such as patient names, healthcare service dates, etc.
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IP: Intellectual Property, including copyrights, patents, trademarks, and trade secrets.
Masking Data vs Tokenizing Data vs Encrypting Data
Masking, tokenizing data, all enable enterprises to use datasets for operational and analytical workloads while keeping sensitive data safe. They differ in terms of how the data is protected, and what risks are involved. Here’s an overview of how they compare.
Tokenized Data
Tokenized data obscures the meaning of real data by replacing it with a meaningless token, such as a random string of characters, which have zero value in the event of a breach.
Data tokenization protects data at rest and in motion. If an application or authorized user needs the real data value, the token can be “detokenized” back to the real data. Usually, the original sensitive data is stored in a centralized token vault.
Encrypted Data
Encrypted data is sensitive data that has been mathematically changed via an encryption key, and therefore, protected. However, the original data pattern remains within the new code, which means it can be decrypted – potentially, by the wrong people.
How They Compare
When comparing data masking vs tokenization, or data masking vs encryption, it’s important to know that one isn’t inherently better than the other. Each has value and is used to protect sensitive information in different contexts and architectures, sometimes in combination.
However, it’s worth pointing out that masked data is the only type of obfuscated data that cannot be identified, or converted back to its original form. In comparison, a centralized token vault could be the target of a massive breach, and encrypted data can be reversed through tactics like social engineering and brute force computing.
Key Reasons for Masking Data
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Data security
If masked data is breached, the original sensitive data it replaced remains safe and protected. Although the process of masking data, makes the data look real, it cann't be used to identify a real individual, or to make a fraudulent transaction. Data masking best practices call for masked data that maintains security within internal systems, applications, and databases, as well as when it is in transit and in the cloud. -
Test data management
Software and application testing teams require realistic, complete, clean, and reliable data. Masking data is a safe and functional alternative to the use of real production data required by test data management tools. With masked data, relational integrity is retained, without ever compromising actual customer data. -
CI/CD
Today, being competitive requires a strong go-to-market strategy. Continuous Integration / Continuous Delivery (CI/CD) in DevOps is the key to innovation and competition. However, CI/CD also requires the ability to provision clean and usable data on demand. Manually cleaning and preparing data is a tedious and time-consuming process that slows down the software development lifecycle. Test data automation is the answer. And, dynamic data masking is the means by which DevOps teams can quickly provision, use, and test new software and apps, without creating bottlenecks or security risks. -
Compliance
The process of masking data allows enterprises to comply with data privacy laws. When data is properly masked, it’s impossible for unauthorized personnel to identify real people, events, transactions, credit cards, medical files, etc. As the number and severity of data privacy regulations rise, masking data and synthetic data generation are becoming more and more common in data-intensive enterprises. -
Customer 360
Customer 360, also referred to as a single customer view, provides a unified and holistic picture of the customer – one that integrates the interaction, transaction, and master data of every customer. With a 360-degree view of the customer, enterprises can improve the customer journey, provide more personalized experiences, increase engagement, prevent churn, and more. However, sharing real customer data with business users increases the risks of a data breach, or non-compliance. By masking data associated with Customer 360 use cases, business users can access realistic and insightful data to improve the customer experience, while keeping sensitive information secure. -
Third-party security
Enterprises today increasingly rely on third-party software and apps. Meanwhile, malicious attackers and hostile nation states view the supply chain as an attractive target to hack into enterprise databases. Indeed, it’s often difficult to accurately assess, let alone control, your vendors’ security posture. Masking data that integrates with, or is processed by, third-party vendors is critical to avoiding a breach in the supply chain.
Why Masking Data via Business Entities is Best
Masking data is essential for security and compliance. Although not all data masking vendors enable the breadth of features, capabilities, and speed that enterprises require, entity-based data anonymization tools do.
The entity-based data masking technology delivers all the data related to a specific business entity – such as a customer, payment, order, or loan – to authorized data consumers. At the same time, it automatically masks sensitive data – at rest, in use, and in transit – to support production, testing, and analytics environments.
Unlike many other data protection solutions, which centralize sensitive information, a business entity approach persists and manages every entity instance in its own individually encrypted Micro-Database™, eliminating the possibility of a massive breach. It can perform dynamic or static, structured or unstructured data masking, while maintaining relational integrity across all databases and systems.
For enterprises that want on-demand access to clean, complete, and operational masked data, a business entity approach is setting the data masking standard.
