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Amazon boosts AWS data security with new Macie service

(Image credit: Image Credit: Ken Wolter / Shutterstock)

Amazon Web Services has launched a new machine learning service aimed at helping organisations protect their sensitive data in the cloud.

Macie's general premise is quite simple: it monitors data on the S3 storage service, and is capable of identifying names, addresses, credit card numbers, driver licenses or social security numbers, stuff like that.

It then monitors that data and keeps an eye out for anomalies. It can't independently conclude if the leak is malicious or not, but once it identifies that there is a leak it will send out notifications.

Macie also comes with a centralised dashboard that is able to highlight high risk files along with information on users and the apps they are accessing.

At the moment, Macie is available in two US regions – Northern Virginia and Oregon, but a major expansion is expected soon.

Pricing will depend on the level of the platform used, with Amazon saying that users will be charged based on the amount of content classified, and the amount of Amazon S3 audit history that is captured by AWS CloudTrail and assessed by Amazon Macie for anomalies.

“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 at Amazon Web Services. 

“Amazon Macie approaches information security in a more intelligent way. 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.”

You can find out more about Macie on this link.

Image Credit: Ken Wolter / Shutterstock