Data masking (also known as data scrambling and data anonymization) is the process of replacing sensitive information copied from production databases to test non-production databases with realistic, but scrubbed, data based on masking rules.
You just need to drag and drop different objects (known as transformations) and design process flow for data extraction transformation and load. In many cases, it is either convenient or very necessary to use production data for development, issue investigation or testing purposes. Data masking is a method of hiding data completely or partially thereby making it hard to recognize or understand the data after the masking has been applied.
While there are several readily-available tools for data masking, some datasets require specialized solutions. Data making best practices can help financial firms meet their customer data protection requirements. By masking delicate production data, organizations make available data workers require to perform their roles while reducing the risk of a data breach from a malicious, careless or compromised insider.
Masking is a technique allowing for certain aspects of data to be hidden by the use of random characters or other data. Persistent or static data masking creates a new copy of the data to which the data masking rules have been applied. It comes with hundreds of pre-defined masking rules and protects sensitive data across all non-production systems.
Simplify data privacy compliance in applications, big data analytics, and hybrid IT. Thales data encryption solutions reduce the time and cost to implement best practices for data security and compliance on-premises and across clouds. Managing data effectively requires having a data strategy and reliable methods to access, integrate, cleanse, govern, store and prepare data for analytics.
Finally, if your organization gives developers copies of databases for use in development, there are a number of data masking tools that can automate the provisioning and masking of the copies. Conditional masking provides developers with another tool for protecting sensitive data. A small number of people may have been infectious before their symptoms developed.
Quality assurance plan that addresses data validation and registry procedures, including any plans for site monitoring and auditing. Data security is a set of standards and technologies that protect data from intentional or accidental destruction, modification or disclosure. Organizations have data in silos spread across traditional data warehouses, enterprise applications, big data lakes, operational data stores, the cloud, and more, creating challenges for business teams.
These technologies enable organizations to operationally minimize the footprint and propagation of sensitive data (or its view), without extensive custom development. Data standardization is the critical process of bringing data into a common format that allows for collaborative research, large-scale analytics, and sharing of sophisticated tools and methodologies. There are many similarities between data masking and data encryption, although the differences are substantial.
Want to check how your Data Masking Processes are performing? You don’t know what you don’t know. Find out with our Data Masking Self Assessment Toolkit: