The adoption of technology is a must to keep data safe throughout each stage of its lifecycle. Organizations can choose from data protection methods such as encryption, masking, tokenization, etc, but they often face difficulty in deciding on the right approach.

A common misconception within the data community is that encryption is considered a form of data masking. In this article, we will provide an overview of encryption and data masking, and show how they differ from each other.

Definitions:

Encryption works by encoding the original data, or plaintext, with the help of sophisticated algorithms that convert it to unreadable text or ciphertext. A decryption key would be needed to revert the ciphertext to a readable format. Encryption is used to protect sensitive data, such as payment card information (PCI), personally identifiable information (PII), financial account numbers, and more.

Data masking, also called data obfuscation, is a data security technique to hide original data using modified content.  The main reason for applying masking to a data field is to protect data that is classified as PII, sensitive personal data, or commercially sensitive data. However, the data must remain usable for the purposes of undertaking valid test cycles. Data masking meets the requirements of most privacy laws including GLBA, HIPAAGDPR, PCI DSS, PIPEDA, CCPA, etc.

There are a few different types of masking. Below is a look at the three main types of data masking:

Static Data Masking

Static data masking refers to the process in which important data is masked in the original database environment. The content is duplicated into a test environment, and can then be shared with third-party vendors or other necessary parties.

Dynamic Data Masking

In dynamic data masking, automation and rules allow IT departments to secure data in real-time. That means it never leaves the production database, and as such is less susceptible to threats.

On-the-fly Data masking

Like dynamic data masking, on-the-fly data masking occurs on demand. In this type of data masking, and Extract Transform Load (ETL) process occurs where data is masked within the memory of a given database application. This is particularly useful for agile companies focused on continuous delivery.

How does data masking works?

Every single business has sensitive data, whether they are trade secrets or employees’ social security numbers, thus all sensitive data must be protected. Data masking obscures sensitive information and replaces it with proxy data.

Data masking works by shielding confidential data, such as credit card information, social security numbers, names, addresses, and phone numbers, from unintended exposure to reduce the risk of data breaches. It minimizes the risk of data breaches by masking test and development environments created from production data, regardless of the database, platform, or location.

Data masking technology can integrate with existing authentication solutions, including Active Directory, LDAP, and Identity Access Management software, and it complements other data protection technologies such as encryption, database activity monitoring (DAM), and security information and event management (SIEM), collectively providing comprehensive data privacy protection.

Data Encryption Vs. Data Masking

One of the most valuable tools of data masking is that once the information is masked, it is irreversible. Using the employees’ example above, you would not want to make a client’s credit card or banking information available to people working at your call centers. This would expose your clients to identity theft and your business to potential litigation. Your employees will still be able to read some of the information but will not be able to unmask what you have obfuscated.

With encryption, information is completely scrambled and illegible to anyone who sees it. However, the intended recipient would be able to unscramble the information once it is received.

How do you pick the best data masking and data encryption for your Business?

Depending on what type of protection you need and the amount of information that needs to be concealed, there is a myriad of options available for you. If you are at a loss about how to move forward with data masking, Encryption Consulting can help. Contact us and let us talk about what we can do for you.

To learn more about Encryption, check out our article https://www.encryptionconsulting.com/education-center/encryption-vs-tokenization

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Data Masking is the process of replacing original production data with structurally similar, inauthentic data. The format of the data remains the same, but the values are altered. The alteration may take place through encryption, character shuffling, or substitution. Data Masking is a one-way process that retrieves the original data or reverse engineering to obtain the original data impossible.

Data privacy legislation such as GDPR in the EU promotes Data Masking, and businesses use private data as little as possible. The average cost of a data breach is $4 Million, which gives companies a strong motivation to invest in information security solutions such as Data Masking, which can be relatively cheaper to implement than some other encryption solution.

Types of Data Masking

  • Static Data Masking (SDM): In Static Data Masking, data is first masked in the database and then is copied to a test environment so organizations can move the test data into untrusted environments or third-party vendors.

  • Dynamic Data Masking (DDM): In DDM, second data storage is not needed. Data remains unmasked in the database, and upon request, data is masked and sent over. Contents are shuffled in real-time on-demand to make the data masked. Unmasked data is never exposed to unauthorized users. A reverse proxy is needed to achieve DDM. Other dynamic data masking methods are generally called on-the-fly data masking.

Benefits of Data Masking:

  • Data Masking is essential in many regulations and compliance, such as HIPPA, where Personally Identifiable Information (PII) data must be protected and never be exposed.
  • Masked Data also retains integrity and structural format.
  • Developers and testers can get access to the data without any data exposure.
  • Decreases security risk while having data analytics and displaying results.
  • The viable solution against threats like
    • Data breaches
    • Data loss
    • Account or service hijacking
    • Insecure interfaces
    • Malicious use of data by insiders

Masking Techniques

Data Masking can be done in multiple ways, which include

  • Substitution
    Organizations substitute the original data with random data from supplied or custom lookup file. This is an efficient and effective way to disguise data since businesses preserve the data’s integrity and structural format.
  • Shuffling
    In shuffling, organizations substitute the original data with another authentic-looking data, but the same column’s entities are shuffled. The value can move vertically or randomly along the columns.
  • Blurring
    The value stored in the database is altered with a defined range of values available.
  • Character Scrambling
    In this, characters are randomly scrambled, rearranging the order of the characters involved. This process is irreversible, so the original data cannot be obtained from the scrambled data.
  • Tokenization
    Tokenization is a reversible process where the data is substituted with random placeholder values. Tokenization can be implemented with a vault or without, depending on the use case and the cost involved with each solution.

Suitable ways to share data with unauthorized users

  • Nulling out or deletion
    Replacing sensitive data with null values is also one of the approaches organizations may prefer with regular data masking capabilities. This may reduce data analytics or another test accuracy.
  • Masking out
    Here, only some parts of the data are masked. It is similar to nulling out since it also ineffective in test environments. This can help in situations such as shopping receipts where only the last four digits are visible to prevent fraud.

What type of data requires Data Masking?

  • Personally Identifiable Information (PII): This includes any data which can be used to identify a particular person personally.
  • Protected Health Information (PHI): PHI includes demographic information, medical histories, test and laboratory results, mental health conditions, insurance information, and other data that a healthcare professional collects to identify appropriate care.
  • Payment card information (PCI-DSS):This is an information security standard for organizations to follow while handling branded credit cards from the major card schemes.
  • Intellectual property (IP):IP refers to creations of the mind, such as inventions, literary and artistic works, designs, and symbols, names, and images used in commerce.

What are the best practices for Data Masking?

  • All sensitive data should be discovered and masked before being transferred to a testing environment. This can prevent any data exposure, which may lead to further complications.
  • Understanding the sensitive data which requires masking and choosing the most suitable masking technique is also necessary.
  • Irreversible data masking methods may be favorable as the data cannot be transformed back to the original version.

About the Author

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A collection of Encryption related products and resources that every organization should have!

Cyber security experts conference 2022

Free Downloads

Datasheet of Encryption Consulting Services

Encryption Consulting is a customer focused cybersecurity firm that provides a multitude of services in all aspects of encryption for our clients.

Download

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