Amazon Web Services (AWS), a renowned web service and cloud technology company and an Amazon.com subsidiary, has reportedly announced several key improvements to Amazon Macie, a machine learning-based cloud security tool. The company would be delivering crucial new features, substantially reduced pricing, and ensuring greater availability worldwide.
The new features of Amazon Macie include upgraded machine learning models for higher accuracy in detection of PII (Personally Identifiable Information), added support for data types that are customer-defined, and native multiple account management with the AWS Organizations.
Currently, Amazon Macie operates across 17 AWS Regions globally, with more regions planned to come online over the upcoming months. The novel Amazon Macie service optimizations allow consumers to discover and secure their sensitive and confidential data in AWS at a massive discount of 80% or more in comparison to the previous pricing.
There are no upfront commitments or extra charges needed to use Amazon Macie, and consumers only pay for the Amazon S3 (Amazon Simple Storage Service) buckets evaluated and the data processed.
As companies relentlessly work to manage the increasing volumes of data, they need to recognize and locate their sensitive information to make sure that it is protected properly and is also being maintained according to the numerous regulatory compliance requirements. finding and protecting this information at scale, however, is a time-consuming and expensive process that could be prone to errors.
The machine learning-based Amazon Macie decrease this burden by offering a cost-effective and scalable service that helps consumers easily find and secure their confidential data in AWS. Amazon Macie automatically gives users a full inventory of Amazon S3 buckets once it is enabled in the AWS Management Console.
Customers just have to choose the buckets they need to submit for the discovery of sensitive data to Amazon Macie, which scans the buckets utilizing pattern matching and machine learning to identify and differentiate the information against a predefined group of some common sensitive data types. Users get an actionable security finding that shows any data the matches the sensitive data types.