Data breaches in the US have significantly increased from 662 in 2010 to more than 1,000 in 2021.
The growing number of data breaches has made protecting data a priority for firms. One of the assured methods to do so is Data Masking.
With increased data-driven applications and software, organizations have introduced challenging protocols to protect their sensitive data when stored or implemented. Implementing a definite security fabric among the technology development team, IT professionals, cloud architects, and non-technical business people also gain confidence in the system.
Table of Contents
What is Data Masking?
Data obfuscation is a process that hides the actual data using modified content, such as characters or numbers. This is a process more commonly known as Data Masking, meaning that data will be structurally similar to the original but hides the sensitive data so that it remains unidentified and safe from being reverse-engineered.
It allows access to information while protecting sensitive data with a functional alternative when actual data is not required. This is to avoid the data being identified by unauthorized users or reverse engineered.
Here are the common goals that your data masking technique should be able to deliver:
- The data should be meaningful and valid for the application
- It should undergo enough changes that it cannot be reverse-engineered
- Data should be consistent across multiple databases within the organizations
Why is Data Masking Important?
According to the University of Maryland, a cyber-attack occurs every 39 seconds!
The most significant risk factor is that most of the sensitive data is stored in a non-productive environment, which is then used for testing and development.
Here’s what makes masking data an effective practice for an organization:
- It solves critical problems such as data loss, data exfiltration, account compromise, insecure interfaces with third-party systems, and cloud migrations
- Protects data in downstream environments
- Helps firms comply with the General Data Protection Regulation (GDPR)
- Reduces risks associated with cloud adoption and outsourcing
4 Types of Data Masking
There are several types of data masking that you can implement depending on your use case. Check them out below:
1. Static Data Masking
Static data masking creates sanitized and duplicated version of the database, containing fully or partially masked data.
This technique generally works on a database copy by making it look accurate to develop, train and test it without revealing actual data.
Static data masking processes sensitive data until a copy of the database can be safely shared. The process is divided into the following steps:
- Creating a backup copy of a database in production
- Loading it in a separate environment
- Eliminating any unnecessary data
- Masking it while it is in stasis
- Pushing it to a target location
2. Deterministic Data Masking
Deterministic data mining involves mapping two data sets that have similar types of data in a way that the other value replaces one value. Although when you mask the data, it will give you the same results.
This technique is convenient for multiple scenarios but less secure.
3. On-the-Fly Data Masking
This technique refers to masking sensitive data while it is transferred from one environment to the other before saving it to the disk. This confirms that the data is hidden before it reaches the target environment.
This technique is ideal for organizations that migrate data between systems, maintain dispersed data sets, deploy software continuously, or have heavy integrations.
Since it can be challenging to always keep a copy of the masked data, the process will only send a subset of the masked data when needed.
4. Dynamic Data Masking
It is similar to an on-the-fly masking technique. However, the data is never stored in the development or test environment.
The data temporarily hides or replaces data in transit resulting in the original data remaining intact. It alters the information in real-time as the users access it. It is applied directly to the production datasets, ensuring that the data is only accessed by authorized users. However, the method doesn’t permanently alter sensitive data values for use in non-productive environments.
The method is implemented for processing role-based security for applications, processing customer inquiries, and handling medical records. The technique applies to the read-only scenarios to avoid writing the masked data into the production system.
Which Data Requires Data Masking?
- Personally Identifiable Information (PII)
- Protected Health Information (PHI)
- Payment Card Information (PCI)
- Intellectual Property (IP)
Here are the most common types of data that require masking:
Personally Identifiable Information (PII)
Refers to data that can be used to identify individuals. Examples include – full names, social security numbers, passports, and driving license numbers.
Protected Health Information (PHI)
Refers to data collected by healthcare service providers to identify suitable treatment. Examples include i insurance information, demographics, laboratory test results and medical histories for health conditions.
Payment Card Information (PCI)
This requires merchants that can handle credit and debit card transactions to secure the cardholder’s data and comply with the Payment Card Industry Data Security Standard (PCI DSS) regulations.
Intellectual Property (IP)
Intellectual Property data is a product of the human intellect.
Including intellectual inventions, business plans, and designs have a high value for firms and therefore need to be protected from unauthorized access.
Data Masking Techniques
- Data Encryption
- Data Scrambling
- Data Switching
- Nulling Out
- Data Substitution
- Data Shuffling
These are some of the most common ways masking is applied to an organization’s sensitive data. They are as follows:
The data is masked by the encryption algorithm using mathematical calculations and algorithms. When the data is encrypted, it is rendered useless unless the user has the decryption key. This is the most secure form of masking but also the most complex one. This is so because it requires technology to perform ongoing data encryption and mechanisms to manage and share encryption keys.
Since look-up tables can be compromised easily, it is important to encrypt the data so it can be accessed with a password.
Data Encryption is more suitable for production data and can be combined with other masking techniques. It is also vital to ensure proper management of the encryption key.
This method refers to sensitive data masking that is reflected aggregate instead of separately.
Averaging allows you to replace all the values with the average value.
As the name suggests, the characters and numbers are shuffled randomly when data scrambling is applied, replacing their originality.
Despite being simple to implement, it is less secure. Its implementation to the other types of data is limited too.
You can apply the method to certain types of data, but it might not be a sure-fire way.
If the data involves dates, then data switching helps make the actual date unclear.
Despite the benefits, data switching has a drawback too. Since this method is applied to one value, it is thereby used to all the values in the dataset too.
Nulling out masks the data by applying a null value to a data column.
The data appears to be missing or ‘null’ when an unauthorized user views it.
Since this method renders the data less useful for testing or development purposes, it can reduce the data integrity and make both goals harder to implement.
This method refers to substituting data with fake, realistic-looking alternative values. The technique is applied to credit card numbers, zip codes, social security numbers, and others.
Data substitution is the most effective method as it protects the original look and feel of the data and can be applied to several types of data.
Like substitution, data shuffling refers to data values being switched within the same dataset. This allows the uniqueness to be maintained by shuffling so that the fundamental values are maintained but assigned to diverse elements. Shuffling is ideal for large datasets.
It is essential to note that the data can be reverse-engineered if anyone tracks the shuffling algorithm.
The process switches an original data set with a pseudonym. This process ensures that the data cannot be used for personal identification. The process requires removing direct and multiple identifiers, which can identify a person when combined.
The process is reversible and enables future use in case re-identification is needed.
Data Masking Best Practices
- Tap into Data Discovery
- Ensure Referential Identity
- Secure Algorithms
- Identify the Project Scope
- Testing the Results
Here are some of the data masking best practices to follow:
Tap into Data Discovery
Before you move on to the masking phase, you need to grasp the data you currently have and distinguish it, as it can be of varying sensitivities.
Usually, an exhaustive record of the data is prepared for all the data in organizations. Outline the sensitive data location, authorized person, and their use.
Identify the sensitive data in the production and the non-production environment. This will help to decide the ideal strategies for each dataset.
Ensure Referential Identity
Referential identity refers to the type of information from a business application that must be masked with the same algorithm.
A single tool used across varied data sets may not be feasible in large organizations. Each line might have a different requirement, as per the budget, administration, or regulatory practices.
Referential identity ensures that the tools and practices throughout the organization are aligned when working with the same dataset.
If unauthorized users learn about the repeatable masking algorithms; they can harness the data by reverse-engineering them. This makes it essential to ensure the separation of duties.
This is crucial as specific datasets can be used in general, while others can only be used by specific teams.
Identify the Project Scope
To select the proper techniques, organizations should know which technique to apply, who is authorized, which applications use the data, the data to protect, and where it resides. The technique might also be required to conform to internal security policies or budgeting constraints. You may also be required to develop your masking techniques if needed.
Due to complexities and multiple lines of business involved, this process needs to be designed differently on a separate stage.
Organizations should also establish required guidelines to allow authorized personnel to access masked data.
Making Masking Repeatable
On a large scale, changes to an organization’s project may also result in changes in the data. Ensure to make masking an automated process, so you can implement them as and when there are changes.
For heavy datasets, it is not possible to implement a single approach to all the datasets. They have to be funneled to multiple needs such as engineering, usages, and arrangements.
Testing the Results
The QA and the testing team ensure that the techniques implemented can produce the desired results.
If the technique doesn’t offer a valid solution, the database is restored to the original unmasked state and requires new calculations.
Five Challenges of Data Masking
Despite appearing straightforward, creating a functional, masked replica of production data poses several challenges for a data masking system.
Preserving the Format
The data masking system must have a comprehension of the data's meaning. When substituting with counterfeit data, the masking system should retain the format, particularly for dates and data strings where the order and structure are critical.
In a relational database, tables are linked through primary keys. If the masking system modifies or substitutes the values of a table's primary key, the same value must be uniformly altered across the entire database.
Preserving the Gender
When substituting individuals' names in a database, the masking system must consider the distinction between male and female names. Randomly altering the name could disrupt the gender distribution in the table.
Many databases have regulations regarding the allowable range of values, such as a specific salary range. The masked data must fall within this range to maintain the significance or semantics of the data.
In cases where the original data within a table is distinctive, the masking system must provide distinct values for each data element. For instance, if a table contains employees' Social Security Numbers (SSNs), each employee should still have a unique SSN after masking. The masked data should also maintain any significant frequency distribution, such as geographic distribution. In contrast, the average value of columns in the masked data should remain close to that of the original data.
What is Data Masking – Key Takeaways
Cybercrime is expected to grow to $10.5 trillion globally, by increasing 15% every year. With organizations being excessively dependent on cloud applications and software, safety barricades are likely to be breached.
Emphasis on ensuring security protocols has paved the way for firms to implement data masking to safeguard the foundations of their operations. Since data masking is a broad concept, before diving head first into it, firms should also realize the prominence of choosing the aspect that fits their workflows.
Data Masking FAQs
1. Why is data masking needed?
Data masking is necessary because it generates a simulated but authentic representation of your organization's data. The aim is to safeguard confidential information while offering a functional substitute when actual data is unnecessary, such as during user training, sales presentations, or software testing.
2. What is the difference between data masking and data hiding?
The difference between data hiding and data masking is that hidden variables are absent, while masked values are substituted with asterisks during Trace and debug sessions.