Protecting sensitive data from data breaches is every company’s lawful responsibility, but data breaches come in many forms. Hacking and Malware are common causes of data breaches, but leaks can occur in every corner of your organization – and without.
  

Data masking is a thorough method of protecting sensitive data that comes recommended by risk frameworks such as the NIST framework, and by the GDPR law itself. 
 

In this blog, we will unpack how data masking works against the most common types of data breaches. 

Types of Breaches
 

First, let’s look at the types of breaches that can affect an organization.
 

Card:
Debit and credit card fraud, not specifically due to hacking.
 

Hack:

A hack perpetrated by an external source or Malware.
 

Insider:

This is when the threat is an insider, like a member of staff or vendor.
 

Physical:

Loss or theft of physical documents.
 

Portable:

Loss or stolen portable devices such as hard drive, laptop or mobile phone.
 

Stationary: 

Prohibited access to a stationary computer or server.
 

Disclosure:

Unintentional disclosure of information.
 

Each of the breaches mentioned above has one defining characteristic in common – loss of data.

How Data Masking combats several types of breach at once
 

Data masking works by anonymizing certain elements of data to render it safe. There are different ways to mask data. Different methods are used for different needs, with different organizational roles, and in different scenarios. 
 

Static masking uses a stable, non-changing environment with an original copy of the production database to anonymize data. It is mainly used to refresh non-production environments and prevent insider threats.
 

Another way of safeguarding data within your organization is with dynamic masking, which helps you control the flow of sensitive data in your business. For example, customer service representatives who do not have access to a credit card besides the last four digits, or judicial clerks who should not have access to addresses and names. Dynamic masking retains the referential integrity of the data, but masks certain elements that could be used for identification or fraud, like the last four digits of a credit card. 
 

Data masking is especially useful for threats outside the organization, like third parties, hackers and malware attacks, or if documents or devices are stolen from your premises. 
 

Random substitution masks data by replacing a given value with a random value from a pre-compiled data set, like a credit card database. Data masking components randomly mask credit card values and retain the card issuer as is. So a Visa card number, for example, will always transform into another Visa card number, but the card number cannot be used for fraudulent activity.
 

A substitution algorithm masks data by replacing a given value with another value suitable for the given entity, be it a field or part of the text or any other type of entity. Substitution is the most effective method of data masking as it preserves the authentic look and feel of the data. 
 

Shuffling is another method of retaining the look and feel of a dataset. With a shuffling algorithm, the data is randomly shuffled within a column. This is especially useful if a customer database falls into the wrong hands as the contact details will have been shuffled.
 

These masking methods are irreversible, so no matter whose hands the data falls into, the privacy of the sensitive data remains intact. 
 

What the law says
 

Any organization that collects or handles customer or patient data is subject to privacy laws and regulations including the GDPR, HIPAA, PCI DSS and the CCPA among others. 
 

Data masking is specifically listed by the GDPR as an effective means of protecting private data. The GDPR extends to all businesses (including businesses that operate outside of Europe) that offer goods and services to European residents and collect personal data in the process.
 

For the health industry, HIPAA regulates the safeguarding of private, individually identifiable health information and requires healthcare companies to establish national standards for electronic health care transactions. 
 

In the banking sector, the GLBA requires organizations to establish financial institution standards for protecting the security and confidentiality of said financial institution's customers' private information. 
 

Ecommerce companies should take note of the terms of PCI DSS, which facilitates consistent measures for data security globally. PCI DSS makes it mandatory to protect credit cardholder information both in production and in development environments. 
 

No matter what sector you operate in or what threats you face, data masking is an effective and comprehensive method of safeguarding sensitive data and lessening the risk of a data breach. 
 

Hush-Hush Data Masking Components is a proven method of ensuring complete privacy protection throughout your organization. Our patented tools have been trusted by organizations across the globe for nearly a decade. 
 

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