Open Menu Close Menu

Cloud

New AWS Service Uses Machine Learning to Protect Data

Amazon Web Services today introduced Amazon Macie, a security service that uses machine learning to automatically discover, classify and protect sensitive data housed within AWS. The service is available for data stored in Amazon Simple Storage Service (Amazon S3), with support for additional AWS data stores to come later this year.

Amazon Macie identifies sensitive data, "using machine learning to better understand where an organization's sensitive information is located and how it's typically accessed, including user authentication, locations and times of access," the company explained in a news announcement. It then monitors data access activity for anomalies that may indicate risks or suspicious behavior. Users receive alerts and recommendations for resolving issues; they can also define automated remediation actions for specific scenarios.

"When a customer has a significant amount of content stored in Amazon S3, identifying and classifying all of the potentially sensitive data can feel a bit like finding needles in a very large haystack — especially with monitoring tools that aren't smart enough to effectively automate what is now a very manual process," said Stephen Schmidt, chief information security officer for Amazon Web Services, in a statement. "By using machine learning to understand the content and user behavior of each organization, Amazon Macie can cut through huge volumes of data with better visibility and more accurate alerts, allowing customers to focus on securing their sensitive information instead of wasting time trying to find it."

AWS customers can enable Amazon Macie from the AWS Management Console. Pricing is based on the amount of Amazon S3 content classified and AWS CloudTrail events analyzed. For more information, visit the Amazon Macie site.

About the Author

Rhea Kelly is editor in chief for Campus Technology, THE Journal, and Spaces4Learning. She can be reached at [email protected].

comments powered by Disqus