Data security is of prime importance in the digital age. Since companies are increasingly relying on digital systems to store and process sensitive information, the need to protect this information from unauthorized access has become more critical than ever. One such strategy that proves to be most effective in safeguarding sensitive information is data masking. This technique enables organizations to obfuscate real data, rendering it completely useless to unauthorized users while it remains functional and useful to the authorized.
This article focuses on data masking, mainly the essential techniques or tools that are used in implementing it.
Essential Data Masking Techniques
There are several techniques which make data masking feasible, each with their own advantages and use-cases. Below is a list of some most popular techniques of implementing data masking.
Substitution
One of the easiest data masking techniques is substitution, which replaces real data with unreal but realistic data. For instance, the real name of a customer could be substituted by using a random generator to pick a name from a list of names prepared beforehand. It uses the same format and structure of the original data as it is, without any conversion.
Shuffling
Shuffling refers to the random reordering of information in a column such that it hides its actual value. For example, if that column in the data were staff salary figures, for that column, the salary figures are just jumbled up randomly among the different staff names. This technique ensures that the masked data retains the original distribution and statistical properties, making it useful for analysis and reporting.
Encryption
Encryption means changing data from one readable format to another un-readable format, using cryptographic algorithms. Only the right decryption key will decrypt the right data. While the technique provides really strong security, it is not always suited for all the different data masking scenarios. In fact, encryption is best used when the masked data isn’t expected to be readable by a human. It is coupled often with other data masking techniques to shield highly sensitive data.
Tokenization
In tokenization, sensitive data elements are mapped to nonsensitive tokens, which can retrieve the actual sensitive data by a methodical systematic approach of tokenization. This process does not provide reversible data. Tokenization is widely implemented in payment processing systems for protecting payment card information or other sensitive data.
Nulling Out
Nulling out refers to the method of replacing sensitive data with ‘null’ or blank values. The technique is simple, which works well in the abstraction of sensitive data from a dataset meant to be shared outside an organization. However, it is likely that the application of such a process may deviate the values of data from their original values. Therefore, nulling out is generally not done singly but in association with other obfuscation techniques.
Redaction
Redaction is a process that partially or totally obscures the sensitive data element. For example, in a document containing confidential information, certain portions of the text might be blacked out or replaced with generic placeholders. It works well in masking text-based data, such as documents, emails, and logs.
Essential Data Masking Tools
Useful data masking requires appropriate tools. There are several software solutions one can find to automate and expedite the data masking process for any organization. Some common tools are shared below.
IBM InfoSphere Optim
IBM InfoSphere Optim is an all-inclusive data-masking tool that can work with huge databases and applications. It comes with techniques like substitution, shuffling, encryption, and redaction to provide strong data masking capabilities. It also provides features such as data archiving, data for testing management, and compliance with data privacy.
K2View Data Masking Tool
A big player in data masking space is K2View. The K2View Data Masking Tool offers comprehensive and flexible masking capabilities that cater to various business needs. It allows for actual real-time data masking which means that the sensitive information is protected without creating dynamics to manage different masked data sets separately. Furthermore, it supports both dynamic and static data masking.
Among the most significant strengths of K2View is the level of granularity that can be obtained in the process of masking at the field and record level, hiding only the pieces of data you want to. This is especially important in complex datasets where there are varying levels of sensitivity in the information.
Oracle Data Masking and Subsettingy
Oracle Data Masking and Subsetting is a software-inclusive mask policy meant for organizations to safeguard their sensitive data in the Oracle databases. It provides real-time data masking techniques using replacements, shuffling, and encryption. Organizations are allowed to define or apply the set policies with minimum effort, since it integrates with Oracle Database.
Best Practices
Best practices in data masking ensure that an organization can effectively offer high security while maintaining system usability. Below are some best practices one must consider.
Identify Sensitive Data
This is probably the first stage of any data masking program. It includes proper inventory and classification of data to establish which data elements are sensitive and need to be masked. Regulatory requirements, business needs, and potential risks should all be taken into consideration when identifying sensitive data. Once that’s done, you can take the necessary steps to make your platform unhackable.
Masking Policies and Rules
Once sensitive data has been identified, organizations should define masking policies and rules that dictate how data should be masked. Masking policies should specify the masking techniques to be used, the data elements to be masked, and the conditions under which masking should occur.
Automating the Masking Process
Manual data masking is an extremely time-consuming and error-prone process. Organizations must use data masking tools to automate the process and to bring efficiency and accuracy. Such automated tools facilitate not only definition and application of masking rules but also monitoring access to masked data and the warranted compliance with data protection regulations. Automation enables an organization to scale data masking in response to growing data volumes.
In the modern digital world, data masking is one of the most important techniques to ensure data is secured and free from possible breaches. Through an understanding of the different data masking techniques, effective methodologies for masking, and the right tools to use, organizations can protect their data but still maintain its utility in the testing, development, and analysis process.
