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[123]Amazon Macie is a fully managed service that helps you discover and protect your sensitive data, using machine learning to automatically spot and classify data for you. Over time, Macie customers told us what they like, and what they didn’t. The service team has worked hard to address this feedback, and today I am very happy to share that we are making available a new, enhanced version of Amazon Macie!
This new version has simplified the pricing plan: you are now charged based on the number of [124]Amazon Simple Storage Service (S3) buckets that are evaluated, and the amount of data processed for sensitive data discovery jobs. The new [125]tiered pricing plan has reduced the price by 80%. With higher volumes, you can reduce your costs by more than 90%.
At the same time, we have introduced many new features: * An expanded sensitive data discovery, including updated machine learning models for personally identifiable information (PII) detection, and customer-defined sensitive data types using [126]regular expressions. * Multi-account support with [127]AWS Organizations. * Full API coverage for programmatic use of the service with [128]AWS SDKs and [129]AWS Command Line Interface (CLI). * Expanded regional availability to 17 Regions. * A new, simplified [130]free tier and free trial to help you get started and understand your costs. * A completely redesigned console and user experience.
Macie is now tightly integrated with S3 in the backend, providing more advantages: * Enabling S3 data events in [131]AWS CloudTrail is no longer a requirement, further reducing overall costs. * There is now a continual evaluation of all buckets, issuing security findings for any public bucket, unencrypted buckets, and for buckets shared with (or replicated to) an AWS account outside of your Organization.
The anomaly detection features monitoring S3 data access activity previously available in Macie are now in private beta as part of [132]Amazon GuardDuty, and have been enhanced to include deeper capabilities to protect your data in S3.
Enabling Amazon Macie In the Macie console, I select to Enable Macie. If you use AWS Organizations, you can delegate an AWS account to administer Macie for your Organization.
After it has been enabled, Amazon Macie automatically provides a summary of my S3 buckets in the region, and continually evaluates those buckets to generate actionable security findings for any unencrypted or publicly accessible data, including buckets shared with AWS accounts outside of my Organization.
Below the summary, I see the top findings by type and by S3 bucket. Overall, this page provides a great overview of the status of my S3 buckets.
In the Findings section I have the full list of findings, and I can select them to archive, unarchive, or export them. I can also select one of the findings to see the full information collected by Macie.
Findings can be viewed in the web console and are sent to [133]Amazon CloudWatch Events for easy integration with existing workflow or event management systems, or to be used in combination with [134]AWS Step Functions to take automated remediation actions. This can help meet regulations such as Payment Card Industry Data Security Standard (PCI-DSS), Health Insurance Portability and Accountability Act (HIPAA), General Data Privacy Regulation (GDPR), and California Consumer Protection Act (CCPA).
In the S3 Buckets section, I can search and filter on buckets of interest to create sensitive data discovery jobs across one or multiple buckets to discover sensitive data in objects, and to check encryption status and public accessibility at object level. Jobs can be executed once, or scheduled daily, weekly, or monthly.
For jobs, Amazon Macie automatically tracks changes to the buckets and only evaluates new or modified objects over time. In the additional settings, I can include or exclude objects based on tags, size, file extensions, or last modified date.
To monitor my costs, and the use of the free trial, I look at the Usage section of the console.
Creating Custom Data Identifiers Amazon Macie supports natively the most common sensitive data types, including personally identifying information (PII) and credential data. You can extend that list with custom data identifiers to discover proprietary or unique sensitive data for your business.
For example, often companies have a specific syntax for their employee IDs. A possible syntax is to have a capital letter, that defines if this is a full-time or a part-time employee, followed by a dash, and then eight numbers. Possible values in this case are F-12345678 or P-87654321.
To create this custom data identifier, I enter a [135]regular expression (regex) to describe the pattern to match:
[A-Z]-\d{8}
To avoid false positives, I ask that the employee keyword is found near the identifier (by default, less than 50 characters apart). I use the Evaluate box to test that this configuration works with sample text, then I select Submit.
Available Now For [136]Amazon Macie regional availability, please see the [137]AWS Region Table. You can find more information on how the new enhanced Macie in the [138]documentation.
This release of Amazon Macie remains optimized for S3. However, anything you can get into S3, permanently or temporarily, in an object format supported by Macie, can be scanned for sensitive data. This allows you to expand the coverage to data residing outside of S3 by pulling data out of custom applications, databases, and third-party services, temporarily placing it in S3, and using Amazon Macie to identify sensitive data.
For example, we’ve made this even easier with [139]RDS and Aurora now supporting snapshots to S3 in [140]Apache Parquet, which is a format Macie supports. Similarly, [141]in DynamoDB, you can use AWS Glue to export tables to S3 which can then be scanned by Macie. With the new API and SDKs coverage, you can use the new enhanced Amazon Macie as a building block in an automated process exporting data to S3 to discover and protect your sensitive data across multiple sources.
â€" [142]Danilo
Danilo Poccia
[143]Danilo Poccia
Danilo works with startups and companies of any size to support their innovation. In his role as Chief Evangelist (EMEA) at Amazon Web Services, he leverages his experience to help people bring their ideas to life, focusing on serverless architectures and event-driven programming, and on the technical and business impact of machine learning and edge computing. He is the author of AWS Lambda in Action from Manning. [144]View Comments
Resources
[145]Getting Started [146]What's New [147]Top Posts [148]Official AWS Podcast [149]Case Studies __________________________________________________________________
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