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Privitar announced the launch of a new seamless, native pattern designed to protect sensitive data for use on Amazon Web Services (AWS).
The release is an entirely new end-to-end deployment and usage pattern of the Privitar Data Privacy Platform for AWS that enables customers to protect their sensitive data in the cloud easily and with minimal infrastructure setup and maintenance.
By bringing deployment automation to AWS, Privitar is helping customers enable serverless policy execution with AWS Glue, a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development, and importing schemas from the AWS Glue Data Catalog.
“Organizations turn to AWS to increase the value of the data they collect by consolidating it in data lakes and committing to analytics and machine learning initiatives, but they also need to ensure that data remains safe while maintaining its analytical utility,” said Steve Totman, Chief Product Officer at Privitar.
“Data privacy plays a key role in fully unlocking the value of the cloud for analytics, and Privitar’s new native pattern for AWS has made that process seamless, enabling customers to be up and running with minimal effort.
“Rather than locking sensitive data away and treating it as a liability, customers can protect their data by baking privacy in so they can take full advantage of AWS analytics and machine learning services, deriving value as they leverage sensitive data as an asset.”
Privitar enables organizations worldwide to safely and efficiently unlock maximum value from their sensitive data without compromising data privacy.
Data transformed by Privitar is utilized in a wide range of use cases powered by big data and analytics platforms.
Privitar’s comprehensive data privacy capabilities enable organization-wide access, use, and distribution of data. Using Privitar, organizations eliminate slow, manual provisioning and enable cloud-based analytics, machine learning, and other services to quickly and safely access insights from even the most sensitive datasets.
The latest release of the Privitar Data Privacy Platform introduces an entirely new end-to-end AWS deployment and usage pattern.
Customers can now easily deploy Privitar on AWS, import a schema from the AWS Glue Data Catalog, define a Privitar Policy, and then apply the Privitar Policy to de-identify data on Amazon Simple Storage Service (Amazon S3), leveraging AWS Glue. The benefits consist of three major changes:
Out-of-the-box automated deployment and upgrades
The Privitar Data Privacy Platform can now be deployed in a matter of clicks with Amazon CloudFormation, the native AWS service that enables customers to deploy infrastructure as code, and AWS Cloud Development Kit (AWS CDK).
Upgrades to newer versions of the Privitar Data Privacy Platform are also managed and automated.
Serverless policy execution with AWS Glue ETL
Privitar’s batch jobs can now run using AWS Glue, a fully managed cloud-based ETL service on AWS.
This enables ephemeral, highly scalable, serverless compute native to AWS, with minimal configuration and seamless application of a Privacy Policy to datasets on Amazon S3.
Integration with AWS Glue ETL removes any requirement for customers to manage long-running clusters, or automate their lifecycle.
Schema inference from the AWS Glue Data Catalog
Privitar connects directly to the AWS Glue Data Catalog, an index to the location, schema, and runtime metrics of data. Schemas can be created within Privitar by importing directly from AWS Glue Data Catalog.
This enables customers storing data in Amazon S3 to use the capabilities provided by AWS Glue Data Catalog to manage and discover schemas for their Amazon S3 datasets, before importing these schemas into Privitar. Minimal configuration is needed before they can start protecting data in the cloud.
As an AWS Partner in the AWS Partner Network (APN), Privitar has achieved AWS Security Competency status and AWS Data and Analytics Competency status, and is available in AWS Marketplace.
from Help Net Security https://ift.tt/3mwPNLf
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