Every year sensitive information is exposed due to data breaches. These data breaches cost companies an estimated $4.35 million on average. As a result, many companies now place a premium on ensuring the security of their data sources.
Data masking offers an effective solution to protect stored data from exposure even in the event of a data breach.
This article will define Data Masking standards for data security. More specifically, this article will cover:
- What is Data Masking?
- Importance of Data Masking
- 5 Types of Data Masking
- Data Masking Best Practices
- Conclusion
- This part of Satori’s Data Masking Guide
What is Data Masking?
Data Masking, also called Data Obfuscation, is a method of concealing sensitive information by replacing it with distorted text or numbers. These distorted texts or numbers still maintain the same overall data points to allow for analysis.
The primary goal of Data Masking techniques is to protect classified data by generating a new version of the data — masked data — that cannot be easily deciphered and therefore can be shared and used throughout the organization. Even if you use data from different databases, the information will be consistent and easy to use.
There are many types of data that you can protect using data masking standards, but the most common type of data for data masking database management includes:
- PII: Personally Identifiable Information
- PHI: Protected Health Information
- PCI-DSS: Payment Card Industry Data Security Standard
- ITAR: Intellectual Property
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Importance of Data Masking
Masking sensitive data offers value to companies in several ways including:
- Promotes GDPR data masking standard conformity by removing potential vulnerabilities that could expose private information. This means that many businesses can gain a competitive edge through data masking.
- Renders sensitive information useless to hackers without affecting its usability or consistency.
- Safeguards sensitive information during data migrations to the cloud or while using integrated third-party apps.
- Protects against the uncertainties that come with outsourcing a project. Since most companies rely solely on trust when allowing outsourced workers to deal with their data, masking it safeguards sensitive information from being improperly accessed.
5 Types of Data Masking
Dealing with various data types also paves the way for different data masking types. With that, here are five methods of masking data:
Scrambling
Scrambling is a simple masking technique that rearranges the characters and numbers, so they no longer form meaningful strings. Simple to construct, this method does not provide as much security for sensitive data as one may hope, and you cannot use it on all data types.
Encryption
Most sensitive information can only be protected by encryption while still maintaining data compliance. The information remains hidden behind an encrypted message, and you can only read it once the correct key — the encryption key — is entered.
You should use encryption for production data that you must restore to its original form. However, as long as only approved parties possess the key, the information will remain secure. Consequently, it is essential to handle the encryption key properly.
Nulling Out
The sensitive information is hidden from illegal access by assigning a null value to a data column. Although its a fairly straightforward method, it offers less reliable data which can have an adverse effect on productivity. It also complicates the use of information in testing and development.
Substitution
Substitution is masking data by replacing it with a different value. This option is one of the most effective data masking approaches in terms of fidelity to the original look and feel of the data. A wide variety of information can benefit from the substitution method. Although this data security method is effective, it is challenging to implement.
Shuffling
While substitution employs new columns of masking data, shuffling keeps the same column of masking data but randomly rearranges the rows. However, if the shuffling technique is known, the scrambled data can be easily reverse-engineered.
Data Masking Best Practices
To ensure your data masking techniques offer the protection you’re looking for, follow these data masking best practices:
Identify and Locate the Sensitive Information
Identify and compile a list of the following details regarding the sensitive information you store before masking data:
- Authorized individuals who have access to private information
- Locations of sensitive information
- Their usage and application
Masking is not required for all the data elements that make up an organization. Instead, conduct a comprehensive search for sensitive information in production and non-production locations. This preparation and input may take a considerable amount of time depending on the intricacy of the data and the organizational structure.
Determine the Breadth of the Project
Organizations should identify:
- the types of data they need to mask
- the audiences for those masks
- the applications that will be using the masked data
- the locations of the data in both production and non-production environments
On paper, this may not seem like much of a challenge. Yet, in practice, with all the moving parts and many revenue streams, it could take a lot of time and effort to accomplish. Therefore, it needs to be accounted for in the project’s overall timeline.
Maintain Referential Integrity
Referential integrity requires that each kind of information originating from a business application is masked using the same data masking algorithms.
When dealing with the same data, it is important to ensure that the organization’s various data masking technologies and processes are in sync. In doing so, you can avoid problems in the future should you need to share data between departments.
Safeguard your Data Masking Techniques
It is just as important to mask sensitive data as it is to keep it secret. Therefore, businesses should set up the proper protocols to ensure that only qualified individuals can access the masking algorithms.
Make Data Masking Methods Repeatable
Data can evolve due to shifts in an organization, project, or product context. You do not have to reinvent the wheel every time; instead, the masking process should be one that is quickly and automatically executed, whenever there are changes to sensitive data.
Conclusion
Companies that employ data masking to protect confidential data need a comprehensive security system. Even if data has already been masked, you must safeguard infrastructure and data sources such as databases against increasingly complex attacks.
Satori enables quick and secure identification and classification of sensitive data. Satori’s process automatically discovers the location of sensitive data, even across multiple databases, stores and warehouses.
To learn more about how Satori can help you quickly and easily mask data and secure access to sensitive data:
