Every year, data breaches expose millions of people’s sensitive data, causing numerous businesses to lose millions. The average cost of a data breach so far in 2023\u00a0is $4.24 million. Among all the breached data kinds, Personally Identifiable Information (PII) is the most expensive. As a result, data security has become a major issue for many enterprises. As a result, data masking has become a critical tool for many firms to protect their sensitive data. In this article, we’ll discuss about Dynamic and salesforce Data Masking techniques and tools.<\/p>
Data masking also known as Data Obfuscation is a technique for creating a phony but realistic replica of your organization’s data. The purpose is to safeguard sensitive data while also offering a functioning replacement when real data is not required, such as in user training, sales demos, or software testing.<\/p>
Data Obfuscation processes alter the values of data while maintaining the same format. The goal is to develop a version that cannot be decoded or reverse-engineered. Character shuffling, word or character substitution, and encryption are all methods for changing the data.<\/p>
Numerous data masking types are routinely used to secure sensitive data.<\/p>
Static Data Obfuscation techniques might assist you in creating a clean replica of the database. The method modifies all sensitive data until a safe copy of the database can be shared. Typically, the process entails producing a backup copy of a production database, loading it to a separate environment, removing any unneeded data, and then Data Obfuscation while it is in stasis. The masked copy can then be delivered to the desired location.<\/p>
It entails mapping two sets of data with the same type of data so that one value is always replaced by another value. For example, the name “John Smith” is always replaced with “Jim Jameson” in any database where it appears. This approach is useful in many situations, but it is intrinsically less secure.<\/p>
Mask data as it is transported from production systems to test or development systems before saving data to disk. Organizations that often deploy software cannot generate a backup copy of the source database and conceal it\u2014they require a method to continuously feed data from production to various test environments.<\/p>
Data is never kept in a secondary data store in the dev\/test environment, similar to on-the-fly masking. It is instead streamed directly from the production system and ingested by another system in the development\/test environment.<\/p>
Here are several common data masking techniques for protecting sensitive data in your datasets.<\/p>
Allows you to replace an original data set, such as a name or email address, with a pseudonym or an alias. This procedure is reversible\u2014it de-identifies data while yet allowing for eventual re-identification if necessary.<\/p>
A method for encoding identifiers that link individuals to masked data. The purpose is to secure users’ private behavior while maintaining the credibility of the masked data.<\/p>
A production database can be masked using an additional lookup table that supplies alternative values to the original, sensitive data. This enables you to use realistic data in a testing environment while protecting the original.<\/p>
Because lookup tables are easily hacked, it is best to encrypt data so that it can only be accessed with a password. You should combine this with other data masking techniques because the data is unreadable when encrypted but viewable when decoded.<\/p>
If sensitive data is not required for QA or development, it can be replaced with generic values in development and testing settings. There is no realistic data with similar properties to the original in this scenario.<\/p>
You can replace all the numbers in the table with the average value if you wish to reflect sensitive data in terms of averages or aggregates but not on an individual basis. For example, if the table contains employee salaries, you can hide the individual salaries by replacing them all with the average salary, so the overall column reflects the true total value of the combined pay.<\/p>
If you need to preserve uniqueness when masking values, scramble the data such that the true values remain but are assigned to various elements. The actual salaries will be presented in the salary table example, but it will not be known whose salary goes to which employee. This strategy works best with larger datasets.<\/p>
If the data in question contains dates that you want to keep private, you can apply policies to each data field to mask the true date. You can, for example, move the dates of all active contracts back 100 days. The disadvantage of this strategy is that, because the same policy applies to all values in a field, compromising one value means compromising all values.<\/p>
Dynamic Data Masking (DDM) is a security mechanism used in database management systems to prevent unauthorized access to sensitive data. It enables database managers to prevent sensitive data exposure by masking sensitive data from non-privileged users while still granting them access to the data they require.<\/p>
DDM works in real-time by replacing sensitive data with fictional or obfuscated data as the data is searched or retrieved from the database. This ensures that sensitive data is never exposed to non-privileged users or programs while still granting authorized users access to the information they require.<\/p>
DDM can be used to mask data in a variety of ways, including masking the full value, a portion of the value, or the format of the information. A credit card number, for example, may be hidden by replacing all but the last four digits with asterisks (*), while a social security number could be concealed by replacing the first five digits with asterisks.<\/p>
DDM is especially beneficial in contexts where several users or applications require sensitive data access, such as healthcare or financial systems. It can assist enterprises in complying with data privacy rules such as GDPR or HIPAA by preventing sensitive data exposure to unauthorized individuals or applications.<\/p>
Data Masking Tools are security tools that prevent the unauthorized use of complicated information. Also, Data Masking Tools replace complex data with bogus data. They can be used at any part of the application development or testing process where the end-user enters data.<\/p>
In this section, we explored several tools that will help avoid data misuse. These are the most popular and widely used data masking tools for small, large, and mid-sized businesses.<\/p>
The most common Data Masking tools available on the market are listed below. The following table compares the best data masking software on the market.<\/p>
K2View secures sensitive data at rest, in use, and transit throughout the company. The technology organizes data uniquely into business entities while ensuring referential integrity and provides several masking capabilities.<\/p>
IRI is a US-based independent software vendor that was created in 1978 and is best known for its CoSort quick data transformation, FieldShield\/DarkShield\/CellShield Data Obfuscation, and RowGen test data generation and management solutions. Also, IRI bundles and consolidates data discovery, integration, migration, governance, and analytics in Voracity, a large data management platform.<\/p>
DATPROF offers an intelligent method of masking and producing data for database testing. It contains a patented algorithm for quickly and easily subsetting the database.<\/p>
With an easy-to-use interface, the software can handle complex data linkages. It offers an extremely clever method for temporarily bypassing all triggers and limits, making it the best-performing tool on the market.<\/p>
IRI DarkShield will simultaneously find and de-identify sensitive data in numerous “dark data” sources. Use Eclipse’s DarkShield GUI to identify, detect, and mask personally identifiable information (PII) “hidden” in free-form text and C\/BLOB DB columns, complicated JSON, XML, EDI, and web\/app log files, Microsoft and PDF documents, pictures, NoSQL DB collections, and so on.<\/p>
Accutive’s Data Discovery and Data Masking solution, or ADM, allows you to identify and conceal your vital, sensitive data while guaranteeing that data attributes and fields are preserved across many sources.<\/p>
Data Discovery identifies sensitive datasets efficiently based on either pre-configured, configurable compliance criteria or user-defined search terms. You can either incorporate your Data Discovery findings into your Data Obfuscation configuration or create your own.<\/p>
Oracle Data Masking and Subsetting help database clients by improving security, speeding up submission, and lowering IT costs.<\/p>
By deleting redundant data and files, it aids in the removal of duplication for testing data, development, and other operations. This tool recommends data plotting and employs a masking description. It generates encoded HIPAA, PCI DSS, and PII guidelines.<\/p>
Salesforce Data Masking is a security tool that obscures or replaces sensitive data in a Salesforce org with fake or obfuscated data. It is a type of Dynamic Data Masking (DDM) that masks sensitive data when it is searched or retrieved from the Salesforce org in real-time.<\/p>
Administrators can use Salesforce Data Masking to designate which fields or objects contain sensitive data and then apply masking rules to those fields or objects. The masking rules can be configured to mask the complete value, a portion of the value, or the value format.<\/p>
Salesforce Data Masking can be used to comply with data privacy standards like GDPR, CCPA, and HIPAA by limiting sensitive data exposure to unauthorized individuals or apps. It can also assist firms in safeguarding sensitive data from internal dangers such as unintentional or purposeful data leaks.<\/p>
Salesforce Data Masking is a premium add-on functionality offered for Salesforce orgs. It can be customized with the Salesforce Shield platform, which adds security features including event monitoring, encryption, and compliance reporting.<\/p>
Overall, Salesforce Data Masking is a useful solution for businesses who need to secure sensitive data in their Salesforce orgs while also complying with data privacy rules.<\/p>
Companies must understand what information must be safeguarded, who has access to it, which apps use the data, and where it lives, both in production and non-production domains, to perform Data Obfuscation properly. While this may appear to be a simple process on paper, due to the complexity of operations and various lines of business, it may need significant work and must be designed as a separate stage of the project.<\/p>
Referential integrity requires that each “type” of information originating from a business application be masked with the same algorithm.
A single Data Obfuscation solution utilized throughout the entire enterprise is not viable in large enterprises. Because of budget\/business requirements, various IT administration procedures, or different security\/regulatory requirements, each line of business may be required to develop its own Data Obfuscation.<\/p>
It is vital to address how to safeguard the data-generation algorithms, as well as alternate data sets or dictionaries used to obfuscate the data. Because only authorized users should have access to the actual data, these algorithms must be treated with extreme caution. Someone who discovers which recurring masking strategies are being employed can reverse engineer big blocks of sensitive information.<\/p>
Masking is the act of concealing or disguising information to safeguard sensitive data from unwanted access or exposure. Masking can be used on a variety of data types, including personally identifiable information (PII), credit card numbers, and financial information.<\/p>
Both Data Obfuscation and encryption are used to secure sensitive data, but they serve different objectives and operate in different ways.<\/p>
The primary distinction between Data Obfuscation and encryption is that masking provides no further security beyond the masking itself, but encryption provides a high level of security by rendering the data unreadable to unauthorized users.<\/p>
Data masking and data hiding are two approaches for protecting sensitive data that work in distinct ways.<\/p>
The primary distinction between data masking and data hiding is that masking allows authorized users to access data while hiding prohibits all users from obtaining sensitive data. Data Obfuscation is often used when authorized users require sensitive data access, such as in development or testing environments, whereas data hiding is used to shield sensitive data from all users, such as in production environments.<\/p>
There are various Data Obfuscation methods available to protect sensitive data, but two of the most prominent are substitution and shuffling.<\/p>
Substitution and shuffling can both be used to safeguard sensitive data in a variety of scenarios, including database management, application development, and data analytics.<\/p>
Depending on the organization’s needs and the context in which the data is used, there are several ways to mask data in SQL. Here are some common SQL Data Obfuscation methods:<\/p>
There are numerous ways to mask data in Excel, depending on the organization’s needs and the environment in which the data is used. Here are some common Excel Data Obfuscation methods:<\/p>
Data Obfuscation is required to secure sensitive data from unwanted access or exposure while allowing authorized users to get the information they require. Personal identification information (PII), financial data, and medical records, for example, can be lucrative targets for attackers or malevolent insiders who may use the data for identity theft, fraud, or other harmful objectives.<\/p>
Data masking has evolved into a pillar of technology that worldwide corporations utilize to comply with privacy requirements. Although Data Obfuscation has been practiced for many years, the sheer volume of data\u2014structured and unstructured\u2014as well as the constantly changing regulatory environment have escalated the complexity of Data Obfuscation at the enterprise scale.<\/p>
The present Data Obfuscation vendors’ offerings are proving insufficient. A new entity-based technique, on the other hand, is setting the norm for Data Obfuscation at some of the world’s top enterprises.<\/p>