Okera Enhances Automatic Discovery of Sensitive Data Using Machine Learning

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According to a recent press release, “Okera announced today version 2.0 of its secure data access platform. The new version uses machine learning to enhance the automatic discovery of sensitive data such as social security numbers and credit card numbers so that organizations are able to protect their consumers’ data and comply with data privacy regulations like GDPR and CCPA. With a visual policy builder, Okera’s secure data access platform allows data owners and stewards to easily create policies that can be enforced dynamically now on Microsoft Azure Data Lake Storage Gen2 in addition to the previously available support of ADLS Gen1 and Amazon S3. Okera is further enhancing its ecosystems by adding support for AWS Glue Data Catalog.”

The release goes on, “Okera has enhanced data lake security by automating data discovery and enabling visual policy creation and large scale enforcement of secure data access policies including: (1) Continuous tagging and automatic discovery of sensitive data using machine learning to ensure that sensitive data is protected as soon as it enters the data lake. Continuous tagging ensures that not only the existing catalog but new raw data is tagged and sensitive data discovered on an ongoing basis. (2) The visual policy builder has been enhanced further to make it easier for data owners and stewards to create data access policies based on roles and attributes and automatically detects policy conflicts to protect against accidental privilege escalation. (3) Enforce fine-grained access control and industry-leading support for consent management and right-to-erasure required by privacy regulations such as GDPR and CCPA. (4) Support of Hive Bucketing resulting in efficient joins for big datasets and improved query performance.”

Read more at PR Newswire.

Image used under license from Shutterstock.com

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