The digital world is filled with opportunities and with them come cloud servers to host, share, and use data non-stop. Data is increasingly abundant and valuable for users and businesses all over the world.

2,5 quintillion bytes of data were produced in 2020, which means 1,7MB of data per person on earth per second. A lot of it was personal data, from geolocation to messages, posts, comments, etc.

Data masking, or data obfuscation, is a data protection technique used to hide sensitive information in non-productive environments such as testing and development systems. This technique replaces the original data with fictitious or synthetic data that resembles the real data but has no real value.

In today's article, we will discuss the options available to protect your data with data masking.

What is data masking and why is so important?

Through data masking, a realistic replica of an organization's data is created, with the aim that this new version cannot be deciphered or reverse-engineered when trying to steal any type of confidential information.

This process, at the heart of any digital data security strategy, protects sensitive or private data while offering a functional alternative in cases where these are not necessary or relevant, such as in software development and testing.

Steps for implementing data masking policies

The following are the steps for implementing data masking policies:

1. Identify confidential/sensitive data: Before you can mask your data, you need to know what parts within the data sets are sensitive and need to be protected. This may include Personal Data (under GDPR and similar legislations such as LGPD in Brazil or APPI in Japan, etc.) or Personally Identifiable Information (PII, under US law), such as names, social security numbers, addresses, credit card numbers, etc.

2. Assess risks to sensitive data. Once you have identified sensitive data, you need to assess the risks to that data. This includes considering the likelihood that data will be accessed, used, disclosed or modified without authorization.

3. Develop a data masking policy. Once the risks are assessed, you can develop a data masking policy. This policy should specify how sensitive data will be masked, who is responsible for data masking, and how masked data will be used.

4. Choose the right masking technique. There are several data masking techniques, such as substitution, obfuscation, synthetic data generation, etc. Choosing the right technique depends on the specific requirements of your project and the type of data you are trying to protect.

5. Implement data masking. Once you have identified the sensitive data and chosen the masking technique, the next step is to implement masking. There are several data masking tools available, such as our own Masker, that can help you with this. Make sure that the tool you choose can mask data both at rest and in motion.

6. Test data masking. After implementing data masking, you should test it to make sure it works as expected. This may include reviewing the masked data to verify that the original information cannot be re-identified.

7. Monitor the implementation of the data masking policy. Once you have implemented the data masking policy, you should monitor it to ensure it is implemented correctly. You must continue to monitor your data and maintain your masking solutions to ensure they remain effective as your data and systems change. This may involve the use of audit tools or the development of review processes.

The main advantages of data masking

Data masking performs an anonymization process designed to mitigate the ability to trace data or electronic traces that would lead to misuse of data or disclosure of personal details. It, therefore, offers the following benefits for companies:

• It prevents data from circulating openly in non-productive environments and, as often happens, succumbing to critical internal or external threats, such as leakage, misuse, or theft. 

• Allows data sharing with authorized users in a secure manner, ensuring that production data is rendered unusable to any attacker seeking to exploit it.

• Reduces the risks associated with data hosting in the cloud or insecure interfaces with easily hacked third-party systems.

• Minimizes security costs: increases workflow efficiency in order to work in compliance with the GDPR and other data protection regulations, and strengthens the privacy protection of the entities and individuals involved to the maximum.

The implementation of data masking policies is an important part of your sensitive data protection policies – an increasing area of concern to many CIOs. By following the steps above, you can help ensure that your sensitive data is protected from the risks of unauthorized access, use, disclosure, or modification.

How does data masking work?

The data masking process alters the data values while respecting the same data format. The manner of altering the data will depend on the nature of the data and may include, among other types of anonymization, a masked word identifier, word substitution, or data encryption using empty spaces or blocks to hide the words.

Regardless of the type and technique used, companies must start by identifying all confidential data to later use algorithms that mask these data and replace them with others that are structurally identical.

The main uses of data masking

A robust data masking solution will be aimed at protecting different kinds of data; from personal or protected health data to payment and intellectual property information. The use of data masking systems provides a variety of scenarios in the business environment.

Optimization of the anonymity system

In order to automate the identification and replacement of confidential information in the workflow, there are data masking tools such as Pangeanic’s Masker that allow storing, using, sharing, and monetizing data in a simple way by using a configurable Artificial Intelligence system for the highest anonymization levels.

This offers an effective way to optimize communication between organizations and protect the client's trust generating total security when sharing data and saving time and money.

Compliance with privacy standards

To manage the risk of losing millions of dollars, reduce social liability and avoid the fines associated with a data breach, companies use data mining as a secure method of complying with privacy standards (such as CCPA / CPRA, HIPAA, GDPR or APP) by leveraging the anonymization solutions created for public administrations.

Areas in which data masking is used

Data masking is available for numerous types of data sources, both structured and unstructured, including the following formats:

• Text files: word processing, PDFs, spreadsheets, presentations, emails, logs, etc.

• Social networks: data from Facebook, Twitter, LinkedIn, etc.

• Commercial applications: MS Office documents, productivity applications, etc. 

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Practical data masking applications

To avoid being the subject of negative press, class action lawsuits, or be the subject of a cautionary tale in the future, organizations around the world use different types and techniques of data masking to ensure data security.

As we have said, the technique consists of hiding confidential information in a specific data set. This can be done by replacing, deleting, or altering some or all of the data. Data masking is used to protect sensitive data such as credit card numbers, social security numbers, and e-mail addresses.

There are many practical applications of data masking. Some of the most common applications include:

1. Protecting Personal Information: Data masking can be used to protect personal information such as names, addresses, phone numbers, and Social Security numbers from unauthorized access. For example, a company can use data masking to hide employees' Social Security numbers during a layoff process to protect their privacy.

2. Secure data exchange: Data masking can enable organizations to share data with external partners, vendors, or contractors without revealing sensitive information. For example, a healthcare provider may share patient data with a research company by hiding personal data to protect patient privacy.

3. Software Testing: Data masking can be used to create test data sets that do not contain sensitive information and thus realistic test environments for software development and testing. By masking production data, developers can work with realistic data sets without compromising sensitive information. This helps ensure that the software is not vulnerable to data attacks.

4. Application development: Data masking can be used to create development datasets that do not contain sensitive information. This helps to ensure that applications are not vulnerable to data attacks.

5. Training: Data masking can be used to create training datasets that do not contain sensitive information. This helps to ensure that employees do not have access to confidential information that they don’t need to perform their job.

6. Data Analysis: Data masking can be used to create analysis data sets that do not contain confidential information. This helps ensure that analysts do not have access to confidential information they do not need to perform their work.

7. Compliance and Audit: Many regulations, like HIPAA, PCI DSS, and GDPR, require organizations to protect sensitive data. Data masking can assist organizations in complying with these regulations by concealing sensitive information during audits and evaluations.

8. Training and Education: Data masking can be used to create training data sets that contain realistic data but do not expose sensitive information. This helps train employees in data handling and security procedures without putting sensitive data at risk.

9. Fraud Prevention: Masking sensitive data can help prevent fraud by making it more difficult for attackers to use stolen data for malicious purposes. For example, credit card companies may mask credit card numbers during transactions to prevent fraudulent activity.

10. Protection of Intellectual Property: Data masking can help protect intellectual property by hiding proprietary information from competitors or unauthorized parties. For example, a technology company may mask source code or product designs during patent filings to protect its intellectual property.

11. Cybersecurity Incident Response: In the event of a cyberattack, data masking can help incident responders analyze and investigate the breach without exposing sensitive information. This allows responders to contain and remediate the attack without compromising data privacy.

12. Business intelligence and analytics: Data masking can be applied to business intelligence and analytics data sets to protect sensitive information while enabling meaningful insight and decision-making.

13. Blockchain applications: Data masking can be used in blockchain applications to enhance privacy and confidentiality while maintaining the integrity of the distributed ledger. For example, a supply chain management system could use data masking to protect supplier information while enabling traceability and transparency.

These are just a few examples of the many practical applications of data masking, as it is a vital tool for protecting sensitive data.

By concealing sensitive information, data masking helps organizations safeguard customer privacy, prevent data theft, maintain compliance with privacy regulations, and mitigate risks associated with data breaches and unauthorized access.

The most common data masking techniques

• Identification labels replace data with a data tag.

• Substitution replaces data with a temporary and realistic identifier.

• Data blending exchanges values within the same dataset.

• Gaps replace data with blank spaces.

• Redaction replaces data with a solid black line.

• Encryption requires a unique key to unmask the data and is the most secure way to protect information.

Legislation to be taken into account

With the introduction of increasingly stringent international privacy standards, organizations have a greater responsibility to protect the personal data of subjects including customers, employees, and prospects.

European Legislation

The European regulation relating to the protection of natural persons was put in place in 2018, with the aim of protecting natural persons with regard to the processing of their personal data and the free movement of such data. Fines for non-compliance with the GDPR can be up to 20 million euros. 

In order to comply with this legislation, companies must obtain express and unequivocal consent for the use of the data, specify what data they are using, how they are processing it, for what purpose and who the person responsible for it is.

Legislation in the United States and Japan

Although the United States and Japan are many steps behind Europe in terms of data protection, the California Consumer Privacy Act (CCPA) came into force in the United States in 2020, and Japan's Personal Data Protection Act (PDPA) has already adopted a number of amendments that plan to come into force in 2022.

It is, therefore, essential to adhere to the current standards and, above all, never sidestep the obligations, for both ethical and legal reasons. Bad practices can jeopardize your business's reputation and lead to multimillion-dollar lawsuits that can put your company on the line.

Legislation in Brazil

The General Data Protection Law (LGPD) is a federal law in Brazil that came into effect on August 18, 2020. The law aims to protect the personal data of Brazilian citizens and sets out a range of requirements for businesses that collect, use, or process personal data.

The LGPD is based on the principles of privacy by design and privacy by default. This means that businesses must consider individuals' privacy from the outset of the development of their products and services, and they should take measures to minimize the collection and use of personal data.

The LGPD also grants individuals a number of rights with respect to their personal data, including the rights of access, rectification, erasure, portability, and objection. Individuals also have the right to file a complaint with the National Data Protection Agency (ANPD) if they believe their personal data has been breached.

The LGPD is one of the strictest privacy laws in the world. It is expected to have a significant impact on businesses operating in Brazil, and it may also affect companies operating outside of Brazil but collect or process personal data from Brazilian citizens.

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Pangeanic, your strongest ally when it comes to data masking

Pangeanic's Pangea Masker artificial intelligence tool will become your best ally, guiding and helping you implement data masking best practices in a highly efficient manner. Automatically identifies insider information and customizes specific features to your business needs; adjusting the sensitivity level and choosing unique labels to optimize the way you perform different types of anonymization.

What are you waiting for? Contact us to find out more.

Request a free trial of our anonymization tool and discover all the possibilities it can offer to your business.

What does the demo include:

We will provide you with free access to the tool, allowing you to anonymize up to 50 pages and see firsthand the following:

• How Pangeanic's masking platform (ECO) works.
  
  
• Functionalities and advantages that it can offer you.
  
  
• The anonymization possibilities that guarantee data privacy and compliance with GDPR.
  
  

1. Fill out the form.
   
   
2. A sales representative will get in touch with you to provide access to the platform.
   
   
3. Free access to the ECO platform to try MASKER for a limited time.
   
   

This demo is relevant to you if your company:

- Deals with personal data.

- Needs to mask large amounts of data.

- Needs to save costs and improve productivity.

- Needs to anonymize in different languages.

- Cares about the security of the data you handle.

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What you will achieve with our tool:

• Efficiency.
  
  
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• Economic savings.

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