Every organization has sensitive data and information stored in production databases. Because this data must always be moved to a lower level environment in real time for various development and testing purposes, it is important to ensure adequate protection of this critical data while the production database is being copied to the non-production environment.
To reproduce production issues in environments such as dev, staging, test, UAT, etc., data professionals typically create test data simply by copying production data to an environment with a lower life cycle. The development team usually has full access to all sensitive information without encrypting or obscuring the production database recovery in this environment. This easily accessible data puts confidential company data at risk. In addition, compliance with regulations such as the GDPR, PCI, and HIPAA requires maintaining confidentiality and important information within the organization. Let’s see how to do Data masking.
What do you need to know about masking data?
Data masking is a privacy method that copies data sets, but with sensitive data. These benign replicas are then used in place of authentic data for testing or training purposes. Data masking, also known as data obfuscation, is masquerading original/sensitive data with modified content. Data masking doesn’t just replace confidential data with spaces. It creates intact, but not authentic, copies of personal data, or other highly sensitive data to preserve data complexity and unique properties. In this way, tests performed on properly covered data will produce the same results as the original data set.
In general, the main function of data masking is to protect sensitive personal information in situations where someone could see without the information’s permission. Imagine a scenario where your organization works with a database builder. By hiding your data, contractors can test the database environment without actually having access to sensitive customer information.
How is data masking done?
Data disguise can be static or dynamic. To achieve data masking, it is important to make a true copy of the database. Data hiding protects personal data in real time. When a request is directed to the database, the record is replaced with dummy data and then a masking procedure is applied to it.
Let’s try to expand on the explanation of data masking. How can we ensure that valid test data is available, but at the same time does not lose employee salaries? We cannot change the employee ID or number because the employee ID or number may be the primary key in the database which will confuse these records and will apply encryption. Changing the primary key makes data records unusable.
We can solve this problem by mixing employee salary data. However, there are a few challenges before we mess up the details. If you are expecting to recover mixed data, it may not be straight. Assuming that the real-time data is independent and we don’t care what happens to the test data, we can use data field replacements to change the test data as long as the limitations of length, data type and other business are met.
We can create a copy of the employee salary field and sort the records randomly. Once we have a complete list of random payment fields, we can use them to replace the existing payment fields by replacing them with a new list. Alternatively, we can create our own payroll and then replace it. The ultimate goal is to ensure that each employee’s salary is mixed with realistic data – but not with actual data.
Data masking Process
↓ In this step Replace secret data fields with dummy data
Database ⇒ Dummy Data
Advantages of data masking
Data obfuscation is essential in many regulated industries where personal data must be protected from overexposure. By hiding data, organizations can make data available to test teams or database administrators if necessary without sacrificing data or leaving mismatches. The main benefit is a reduced security risk.
General data masking technique
There are a number of techniques that IT professionals can use when covering data. Below given is a list of data masking techniques and how they could help shielding date relating to your business:
- Encryption: Although data is encrypted to protect it from unauthorized viewing, authorized users should be provided with a passcode or a key to decrypt the data. Encryption is the most commonly used data masking technique and is an effective one. An encryption algorithm is used to encrypt the data.
- Shuffle characters: Shuffling characters is the most basic masking technique. This technique involves rearranging the characters to hide the original data from viewing. This shuffling of characters is done in a random manner. For example, a bank account number of a customer is 0183567, shuffling technique rearranges the numbers and displays the account number as 8506713, thereby shielding the data.
- Cancel or delete: As the name suggests, when you use this approach, data becomes null for anyone who is not allowed to access it.
- Number and date variance: This method of data masking is useful when dealing with data involving numbers and dates. In this method, values of data are either increased or decreased by some random percentage. This helps not only in hiding the original data but also keeping the data within the range of original data. For example, original salaries of employees could be increased or decreased by 10% variance.
- Substitution: As the name suggests, this method replaces the original data with that of information that is identical but is completely unrelated. For example, surnames of your customers would be substituted with that of your employees surnames.
- Shuffling: This method is similar to substitution, but differs in that the data in columns are randomly moved thereby altering the rows as well.
When covering data, pay attention to the following
Below are some things to consider when designing / choosing a solution to cover sensitive data fields.
- Non reversible: It shouldn’t be possible to retrieve the original sensitive data by reversing the masking process. If someone were to reverse the process of retrieving sensitive data, the entire purpose of withholding data was destroyed.
- The hidden data must be similar to the production data: This is another important point to remember. The data must be similar to the live data – otherwise testing can be challenging. Therefore, this point should be considered when designing or purchasing sensitive data masking solutions.
- Maintain reference integrity: If the data field to be manipulated is a primary key, the corresponding foreign key must also refer to the masked data field, otherwise the reference integrity is not preserved and there is a foreign key that does not contain an appropriate primary key in the table. That is, if the employee ID is the primary key and this field is encrypted, all instances of this field must be identically encoded.
- Can be repeated: The masking must be repeated. Production dates change frequently – sometimes by the hour. If the data backup solution only supports one-time cracking, this could be a problem because the newly added record is not covered up.
- Database integrity: Apart from maintaining reference integrity, the solution must also be able to manage triggers, keys, indexes, etc. It should be able to automatically detect connections between all database objects and maintain the appropriate state.
- Previous masked data: If the solution is purchased, the company should also seek support for closed data pre-built for common uses such as credit card numbers, social security numbers, and more. Associated solutions must contain ready-to-use sample data – especially for data fields authorized by compliance standards such as PCI DSS, HIPAA, SOXetc.
The need for masking and data subsets
- Limiting the Dissemination of Sensitive Information: Increasing security threats have increased the need to limit the disclosure of sensitive information. At the same time, copying production data for non-productive purposes such as testing and development, spreading sensitive data, pushing the boundaries of security and compliance, and increasing the likelihood of data breaches.
- Share What You Need: Organizations often need to share production records with internal and external parties for a variety of reasons. For example, a cloud application provider may need to request and share information specific to each of its customers. Another example is a company completing court orders. It must extract a subset of the production data and pass it to the court. In some cases, obtaining and sharing some or subset of information is more effective than sharing the entire production data set.
- Compliance with data protection laws and standards: Data protection standards such as PCIDSS and the European Union (EU) General Data Protection Regulation (GDPR) emphasize the protection of sensitive information in non-productive environments, as these are not normally protected or monitored like production systems. The EU GDPR also grants an individual the right to be forgotten, deleted, transferred or corrected, which requires the identification and processing of a subset of information.
- Minimizes storage costs: Use of all production data for testing, development, and quality assurance purposes incurs additional storage costs and extends the test and development cycle, which increases the overall costs for storage and operation.
- Masking and subsetting of data addresses used above. Data obfuscation replaces confidential data with fictitious but realistic looking data. When selecting data, it is reduced in size by removing or retrieving data. Masking limits the spread of sensitive data by anonymizing sensitive production data. Subsets help minimize storage costs by deleting data or retrieving subsets of data for sharing or archiving. Data masking is also known as static data masking, and a subset of data is also known as test data management.
What does the GDPR mean?
GDPR refers to the General Data Protection Regulations. This is at the heart of European digital data protection legislation.
How to Establish Data masking Standards with GDPR
At its core, the GDPR is a new set of rules that should give EU citizens more control over their personal data. The aim is to simplify the regulatory environment for businesses so that citizens and businesses in the European Union can take full advantage of the digital economy.
These rules were framed to meet customers’ need for getting things done swiftly in this age of the Internet and also introducing laws and obligations for data security, privacy and consent across Europe.
In essence, nearly every aspect of our lives revolves around data. From social media companies to banks, retailers and governments, nearly every service we use involves the collection and analysis of our personal information. Your name, address, credit card number, etc., all of this is collected, analyzed and, perhaps most importantly, stored by the organization.
What is the GDPR Compliance?
Data breaches are bound to happen. Information is lost, stolen, or falls into the hands of people who never want to see it – and those people are often mean.
Under the provisions of the GDPR, organizations must not only ensure that personal data is collected lawfully and under strict conditions, but those who collect and manage it are also obliged to protect them from misuse and exploitation and respect the rights of data owners, or impose a penalty for failing to do so.
What is personal data under the GDPR?
Types of data that are considered private according to applicable law include name, address and photo. The GDPR extends the definition of personal data so that something like an IP address can become private data. It also contains sensitive personal data such as genetic data and biometric data which can be processed to uniquely identify individuals.
Who does the GDPR apply to?
The GDPR applies to all organizations operating in the EU as well as non-EU organizations that offer goods or services to customers or companies in the EU. Ultimately, this means that almost every major company in the world needs a strategy to comply with the GDPR.
One practical solution, data masking, will help you comply with the GDPR in three main ways:
- You can prove that you take GDPR compliance seriously
- You can improve your customer and business relationships
- You can optimize the functionality of your Devops
What is the best data masking tool?
Data masking tools protect tools that prevent misuse of complex information. Data masking tool eliminates complex data with fake data. They can be used during application development or testing when the end user enters data. List of best data masking tools that are the best and most widely used data scanning tool for small, large and medium businesses. Below are the most popular data incognito tools available in the market.
Data mask tool function
Here are the different functions of these tools:
- The masking procedure provides data on request.
- The Data Protection Act helps monitor compliance.
- Non-coding masking rules are available.
- Access to data stored in various databases.
- Accurate but fictitious data is available for testing.
- Format preserving encryption conversion.
This tools can apply the following types of masking in different methods
- Key masking: Produces a deterministic result for the same output.
- Random masking: Generates unique and random results for the same data source.
- Expression masking: Apply an expression to the port to modify data or create data.
- Replace: Replace data fields with similar but unrelated dictionary data.
- Custom Form Formats: Apply custom form formats to change SSN, credit card numbers, phone numbers, URLs, email addresses or IP addresses.
The application of data masking to achieve the confidentiality of information is used to increase the confidentiality of sensitive data. Data confidentiality is valued by all people in all walks of life, not only for pride but also for security reasons.