With the ubiquitous connectivity across mobile devices, cloud, computers, and apps, there is an increasing need for organizations to handle enormous amounts of data securely.
Traditional approaches to data protection – firewalls, encryption, and passwords, often fail to lock down the data adequately. The challenge for organizations is that they need to follow innovative approaches to expose data only to the required people while maintaining confidentiality and adhering to regulatory compliance standards. Enterprises need to go beyond the legacy methods and move towards a comprehensive solution that offers data protection at a granular level. Data Masking is evolving to bridge the gap left by the traditional approaches to data protection.
What is Data Masking?
Gartner defines data masking as “a technology aimed at preventing the abuse of sensitive data by giving users fictitious (yet realistic) data instead of the original data.”What is Data Masking? Click To Tweet
Also known as Data Anonymization or Data Pseudonymization, data masking is the process of interchanging or varying certain elements of the data, enabling privacy and confidentiality of data. While the structure of the data remains the same, the presentation of information is changed to protect sensitive and confidential information.
Data masking is essential in a few scenarios where the functional substitute of the real data does the job instead of using the actual data. For example, if you need to mask a postal code, you can merely randomize the numbers. But if you need the data for application testing, it is important to maintain the right format for the application to recognize it.
Data Masking – Why & How?
Here are three situations in which data masking is critical:
- Securing data in non-production environments
Organizations continue to improve the functionality of their existing applications. As a result, application development often compels developers to test the functionality in the production-like environments to ensure that it is in line with the standards set. For testing, developers need to obtain data from production. Organizations often breach information unknowingly when they share the data from regulated production environments to non-production environments. Data breaches in non-production environments can cause loss of millions of dollars to organizations.
- Handling insider threats
While most data breaches happen from malicious external attacks, they can also take place due to internal factors – within an organization. Insider threats that cause loss or damage to data include accidents, phishing, theft, carelessness, malware attacks, hacking, etc. As per a 2017 Insider Threat Report, 53 percent of companies estimate remediation costs of $100,000 and more, with 12 percent estimating a cost of more than $1 million. Developers and testers need access to the data from the Production environment. It is important to protect data that is vital for the purposes such as development, testing, and QA cycles. Hence, data masking is becoming a standard practice that is often necessary to secure data. More so, organizations are now compelled to have compliance with the national and international data protection legislation. By masking the production data, developers and testers would have the liberty to work with the real data without compromising on the confidentiality of data.
- Ensuring compliance with General Data Protection Regulation (GDPR)
The General Data Protection Regulation (GDPR) is the latest regulation passed by the European Union to govern the way businesses administer and protect customers’ personal data. It has come into effect from May 25th, 2018. This regulation focuses on the protecting an individual’s privacy rights and emphasizes the need for “Pseudonymization,” an umbrella term that encapsulates procedures like Data Masking, encryption and hashing. It also directs organizations to regulate the amount of data they collect and minimize it to a bare minimum. Under the GDPR regulation, personal information collected by a company cannot be used for any other purpose unless it is pseudonymized.
All in all, ‘Data Masking’ offers organizations a highly efficient way to comply with the data security requirements. It effectively reduces the risk of data breaches and protects the sensitive data from malicious and accidental thefts.
AutoRABIT, an end-end Continuous Delivery suite for SaaS platforms, has rolled out Data Masking feature in its 4.2 GA release to help enterprises successfully achieve data security. Our enterprise-class data masking solution encompasses data masking best practices and enables organizations to balance the need to use and secure the data.
Click here to learn more about AutoRABIT 4.2 GA Release Data Masking Solution and more.