Dynamic data masking tools

Here we discuss the introduction to Data Masking Tools, features and top data masking tools respectively. You may also have a look at the following articles to learn more —. Submit Next Question. By signing up, you agree to our Terms of Use and Privacy Policy.

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Compared to other approaches, k-anonymization has been shown to be the most effective for data masking. Watch the Demo. Protect yourself from data leaks without resorting to manual changes. Protect yourself from data attacks without resorting to time-intensive security methods that require coding or new ETL pipelines. Differential privacy statistically guarantees that any individual record within a dataset cannot be identified.

Immuta is one of the few data platforms to provide differential privacy. It is one of our dynamic privacy enhancing technologies PETs and it works by injecting noise into queries to protect the privacy of individual records and enable increased data sharing.

Data de-identification is the removal of personal information, such as names, specific geographic locations, telephone numbers, and Social Security numbers, to prevent the identification of specific individuals within a data set. Learn Data Integration.

Dynamic Data Masking Data security and privacy for data in use by both mission-critical and line-of-business applications. Prevent unauthorized users from accessing sensitive information with real-time data de-identification and de-sensitization. Dynamic Data Masking also lets you: Dramatically decrease the risk of a data breach Easily customize data-masking solutions for different regulatory or business requirements Protect personal and sensitive information while supporting offshoring, outsourcing, and cloud-based initiatives Secure big data by dynamically masking sensitive data in Hadoop.

Here are the five top criteria for an effective data masking tool. Dynamic data masking allows you to manage access and privacy to data in order to stay compliant with your own internal rules and federal or industry regulations, all without having to copy or move data. Manually removing or copying data can be time consuming and inefficient, leading to delays or weakening data utility. Dynamic policies enable robust data masking capabilities without slowing down time to data access and use. The best data masking and data access control tools , such as Immuta, provide the ability to automatically classify and tag identifiers as direct, indirect, or sensitive.

This reduces manual processes, and accelerates and simplifies human inspection when necessary. One of the most basic but important tenets of data governance is data access control, and to keep sensitive data truly secure, you need a tool that automates access control with fine-grained parameters. With platforms like Immuta, data engineering and operations teams can use fine-grained access controls to ensure only the people who need access to data have it, without making copies or working within complicated, static role-based access parameters.

The ability to enforce these access controls universally across any cloud compute platform helps ensure policies are consistent and scalable so no data falls through the cracks. Platforms such as Immuta offer automated policy enforcement, an essential tool that makes auditing data usage easier and faster than ever. The platform you choose should allow you to gather real-time insights into data usage across your organization with detailed, auto-generated reports that show which data was accessed, by whom, when it was accessed, and for what purpose.

When it comes to data masking, there are several different types available. Each type has its own advantages and disadvantages, and may be most efficient for specific applications. Here are some of the most common types of data masking for analytical databases. Static data masking allows you to create a copy of your existing database and sanitize it to remove all identifying information.

The data is altered in the copy to the point that it can be safely shared without risk of privacy breaches. In this instance, an entirely new database is created, moved to a separate location, stripped of unnecessary information, then masked and shared with the intended audience or location. A major limitation with static data masking is that by creating an altered copy of the original data, it can become difficult to maintain a single source of truth.

As new data products are created, this may lead to confusion and data silos. With dynamic data masking , data never needs to be stored in a secondary data storage environment the way it is with static masking.