Discover sensitive information from millions of fields without manual effort.
- Manual work to identify sensitive information from millions of fields
- Difficulty incorporating different team members in data identification and tagging process
- Custom development of classifiers without confidence intervals
- Automate detection of sensitive data to save hundreds of hours
- Easily allow different team members to inspect data through custom workflows
- Leverage pre-built classifiers with high confidence levels
Apply 60+ prebuilt classifiers alongside domain-specific, custom classifiers based on a desired confidence level, without worrying about false positives.
Enable different teams to inspect tags through workflows that certify data has been properly identified and tagged.
Assess sensitive data footprint by profiling registered tags for different elements such as PII, PHI, or other sensitive data.
Author policies that reference existing metadata in Alation, Collibra, Snowflake, and more, without managing policy metadata in multiple places.
What is data discovery and classification?
Data discovery and classification is a multi-step process aimed at providing a more detailed understanding of user data. Data discovery tools assess the data environment and identify data source locations. Next, the data is classified, using predefined parameters to identify and label certain data types that reside in these sources. Immuta’s sensitive data discovery feature automatically assesses incoming data, classifying sensitive data in columns as tags.
What are the most common types of data classification?
Data classification is a way of tagging data according to its type, sensitivity, and value to the organization if altered, stolen, or destroyed. This process is integral to the protection of data, as policies can be built based on how certain data is classified and its level of sensitivity. While the specificity of types of classification can vary, the most common types of data classification include public data, private and/or internal data, confidential data, and restricted data.
Why is automated data classification important for cloud data platforms?
Taking a more traditional manual approach to data classification is incredibly labor-intensive, time-consuming, and prone to human error. By contrast, automated data classification streamlines the classification process by automatically analyzing and categorizing data in real-time. This automated classification is carried out based on predetermined parameters, which are set by data teams in advance and allowed to run as new data enters a given data ecosystem.
What are the key capabilities of data discovery software?
An effective data discovery software should provide teams with a few key capabilities. These include an automated data discovery and classification process, giving enterprises clear visibility across their ecosystem as new data sources are added to different components. This software should also reduce costs and time-to-data, as well as reduce the risk of data breaches and leaks and ensure the compliance with various applicable data laws and regulations.
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