An effective way to mitigate the risk of non-production environment breaches is to mask sensitive data used in these environments. The goal is to make it useful for developers and testers while rendering it useless for thieves and hackers. Data masking replaces the original data values with fictitious but realistic equivalents in an irreversible manner. This ensures that developers can test valid data without compromising data privacy. Furthermore, masking helps bring the test data in compliance with data privacy regulations by replacing regulated data with realistic substitutes. By eliminating the risk of personal exposure in the event of a breach, data masking provides peace of mind to users.
In my experience, to mitigate the risk of non-production environment breaches, it's crucial to maintain an immutable baseline of data and configurations. Hackers are skilled at covering their tracks, so having an immutable copy of the dataset helps in rapidly identifying and correcting any surgical redaction or subtraction they might attempt. With this approach, hackers find it much harder to alter files and cover their tracks effectively, especially when it comes to configuration and log files. Additionally, immutability provides an extra layer of protection against attempts to permanently destroy data, as it remains out of reach for the hackers. Non-production environments are often targeted as they are usually less protected compared to production environments, making the maintenance of an immutable baseline vital for mitigating breaches. Hope this helps!
Delphix is a software company that specializes in data virtualization and data management solutions. The company's primary product is the Delphix Dynamic Data Platform, which enables organizations to streamline data operations and accelerate application development.
Delphix focuses on solving data challenges faced by enterprises, such as data provisioning, data masking, data integration, and data versioning. The platform works by creating virtual copies of data, known as "data pods," which...
An effective way to mitigate the risk of non-production environment breaches is to mask sensitive data used in these environments. The goal is to make it useful for developers and testers while rendering it useless for thieves and hackers. Data masking replaces the original data values with fictitious but realistic equivalents in an irreversible manner. This ensures that developers can test valid data without compromising data privacy. Furthermore, masking helps bring the test data in compliance with data privacy regulations by replacing regulated data with realistic substitutes. By eliminating the risk of personal exposure in the event of a breach, data masking provides peace of mind to users.
In my experience, to mitigate the risk of non-production environment breaches, it's crucial to maintain an immutable baseline of data and configurations. Hackers are skilled at covering their tracks, so having an immutable copy of the dataset helps in rapidly identifying and correcting any surgical redaction or subtraction they might attempt. With this approach, hackers find it much harder to alter files and cover their tracks effectively, especially when it comes to configuration and log files. Additionally, immutability provides an extra layer of protection against attempts to permanently destroy data, as it remains out of reach for the hackers. Non-production environments are often targeted as they are usually less protected compared to production environments, making the maintenance of an immutable baseline vital for mitigating breaches. Hope this helps!