With the realization of what data can do in catering to users in providing a unique experience of a product or service, businesses are collating data from all sources. The collected data is huge in volume and is shared with many stakeholders to derive meaningful insights or to serve the customers.
This data sharing results in regular data breach occurrences that affect companies of all sizes and in every industry — exposing the sensitive data of millions of people every year and costing businesses millions of dollars. According to an IBM report, the average cost of a data breach in 2022 is $4.35 million, up from $4.24 million in 2021. It becomes imperative to secure access to sensitive data that flows across an organization for faster development, service, and production at scale without compromising its privacy.
Data masking anonymizes and conceals sensitive data
Data masking anonymizes or conceals this sensitive data while allowing it to be leveraged for various purposes or within different environments.
Create an alternate version in the same format as of data
The data masking technique protects data by creating an alternate version in the same format as of data. The alternate version is functional but cannot be decoded or reverse-engineered. The modified version of the original data is consistent across multiple Databases. It is used to protect different types of data.
Common data types (Sensitive data) for Data Masking
- PII: Personally Identifiable Information
- PHI: Protected Health Information
- PCI-DSS: Payment Card Industry Data Security Standard
- ITAR: Intellectual Property Information
According to a study by Mordor Intelligence, “The Data Masking Market” was valued at USD 483.90 million in 2020 and is expected to reach USD 1044.93 million by 2026, at a CAGR of 13.69% over the forecast period 2021 — 2026.
In this information age, cyber security is very important.” Data masking helps secure this sensitive data by providing a masked version of the real-time data while preserving its business value (see: k2view dotcom; “what is data masking”). It also addresses threats, including Data Loss, Data Exfiltration, insider threats or account breach, etc.
Many data masking techniques are used to create a non-identifiable or undeciphered version of sensitive data to prevent any data leaks. It maintains data confidentiality and helps businesses to comply with data security standards such as General Data Protection Regulation (GDPR), Payment Card Industry Data Security Standard (PCI DSS), etc.
Common Methods of Data Masking
1. Static Data Masking
This method of data masking is very commonly used to mask data in a production environment. In this method, the hidden data retains its original structure without revealing the actual information. The data is altered to make it look accurate and close to its original characteristics so that it can be leveraged in development, testing, or training environments.
2. Dynamic data masking
This method is different from static masking in a way that active or live data is masked without altering the original data form. Thus, in this method, the data is masked only at a particular database layer to prevent unauthorized access to the information in different environments.
With this method, organizations can conceal data dynamically while managing data requests from third-party vendors, parties, or internal stakeholders. It is used to process customer inquiries around payments or handle medical records within applications or websites.
Informatica offers PowerCenter with PowerExchange for Extract Transform Load (ETL) and ILM for data masking. These products embody best practices for handling large datasets across multiple technologies and sources.
Informatica Dynamic Data Masking anonymizes data and manages unauthorized access to sensitive information in production environments, such as customer service, billing, order management, and customer engagement. Informatica PowerCenter Data Masking Option transforms production data into real-looking anonymized data.
3. On-the-fly data masking
The on-the-fly data masking method is considered ideal for organizations that integrate data continuously. With this method, the data is masked when transferred from a production environment to another environment, such as a development or test. A portion of data or smaller subsets of data is masked, as required, thus eliminating the need to create a continuous copy of masked data in a staging environment, which is used to prepare data.
Different platforms use each or a combination of these methods to implement data masking. For example, K2view offers data masking through the data product platform that simplifies the data masking process of all the data related to specific business entities, such as customers, orders, credit card numbers, etc.
The K2view platform manages the integration and delivery of this sensitive data of each business entity masked in its encrypted Micro-Database. It uses dynamic data masking methods for operational services like customer data management (customer 360) or Test data (test data management), etc.
Another example of using both static and dynamic data masking methods is Baffle Data Protection Services (DPS). It helps to mitigate the risks of data leakage from different types of data, such as PII, Test data across a variety of sources. With Baffle, businesses can build their own Data Protection Service layer to store personal data at the source and manage strong access controls at that source with Adaptive Data Security.
Popular Data Masking Techniques
Data Encryption is the most common and reliable data-securing technique. This technique hides data that needs to be restored to its original value when required. The encryption method conceals the data and decrypts it using an encryption key. Production data or data in motion can be secured using data encryption technology, as the data access can be limited to only authorized individuals and can be restored as required.
The Data Scrambling technique secures some types of data by rearranging the original data with characters or numbers in random order. In this technique, once the data is scrambled with random content, the original data cannot be restored. It is a relatively simple technique, but the limitation lies with only particular types of data and less security. Any data undergoing scrambling is viewed differently (with randomized characters or numbers) in different environments.
The Nulling Out technique assigns a null value to sensitive data in order to bring anonymity to the data to protect data from unauthorized usage. In this technique, the null value in place of original information changes the characteristics of data and affects the usefulness of data. The method of removing data or replacing data with a null value takes away its usefulness — making it unfit for test or development environments. Data integration becomes a challenge with this type of data manipulation, which is replaced with empty or null values.
The shuffling data technique makes the hidden data look authentic by shuffling the same column values that are shuffled randomly to reorder the values. For instance, this technique is often used to shuffle employee names columns of records such as Salaries; or, in the case of patient names, columns shuffled across multiple patient records.
The shuffled data appear accurate but do not give away any sensitive information. The technique is popular for large datasets.
-
Data Redaction (blacklining)
The Data Redaction technique, also known as blacklining, does not retain the attributes of the original data and masks data with generic values. This technique is similar to nulling out and is used when sensitive data in its complete and original state is not required for development or testing purposes.
For instance, the replacement of credit card number with x’s (xxxx xxxx xxxx 1234) displayed on payment pages in the online environment helps to prevent any data leak. At the same time, the replacement of digits by x helps developers to understand what the data might look like in real-time.
The Substitution technique is considered to be the most effective for preserving the data’s original structure, and it can be used with a variety of data types. The data is masked by substituting it with another value to alter its meaning.
For example, in the customer records substituting the first name ‘X’ with ‘Y’ retains the structure of the data and makes it appear to be a valid data entry, yet provides protection against accidental disclosure of the actual values.
Conclusion
Data masking has emerged as a necessary step for transforming real-time data to non-production environments while maintaining the security and privacy of sensitive data.
Masking of data is crucial when managing large volumes of data and gives the authorization to dictate the access of data in the best possible way.
Featured Image Credit: Provided by the Author; Pexels; Thank you!
