AWS WAF now lets you generate labels and customize your WAF rules based on those labels. With this feature, you can configure WAF to add descriptive labels to web requests when a WAF rule matches the request, regardless of the action associated with the rule. You can also check for the presence of those labels in subsequent WAF rules and combine with other WAF rules to take action on web requests that include the label. Creating a label also generates a corresponding CloudWatch metric and adds the label to your WAF logs for improved visibility.

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to analyze text documents and identify insights such as sentiment, entities, and topics in text. Today, we are announcing support for two security focused features: support for IAM condition keys and support for encrypting custom models with customer managed KMS keys. With AWS Identity and Access Management (IAM) condition keys, you can control the Amazon Virtual Private Cloud (VPC) and encryption settings for your Amazon Comprehend APIs. You can use the new condition keys in IAM policies when granting permissions to create asynchronous jobs and creating custom classification or custom entity training jobs in your organization. For example, As a security requirement, you can now enforce that all Comprehend asynchronous jobs must use VPC endpoints.Additionally, while Amazon Comprehend already supported Customer Managed Keys (CMK) for encrypting your training data and machine learning volumes used for inference, you can now encrypt your custom models in Amazon Comprehend using a Customer Managed Key (CMK).

You can now clean and transform data with an interactive, point-and-click visual interface from Amazon Redshift, Snowflake, Microsoft SQL Server, MySQL, Oracle Database, and PostgreSQL through native JDBC connectors in AWS Glue DataBrew. In just a few clicks, you can configure these JDBC connections from the AWS Management Console to directly explore and experiment with datasets from AWS data lakes, data warehouses, and databases without writing code.

Starting today, Bring Your Own IP (BYOIP) is available in four additional AWS Regions: GovCloud (US-East), GovCloud (US-West), Asia Pacific (Hong-Kong), and US West (Northern California).  

Today, we are excited to announce the general availability (GA) of Queue notifications for Amazon GameLift. Trusted by some of the most successful game companies in the world like Wargaming, Ubisoft, and more, GameLift deploys, operates, and scales dedicated servers for multiplayer games. With this new update, you as a game developer can now receive automatic notifications when changes take place to the state of your player queues on GameLift.

AMI Factory is an AWS Solutions Consulting Offer delivered via a consulting engagement from Capgemini, an AWS Migration Competency Partner. AMI Factory helps customers integrate and build any type of AMIs with custom configurations, setting security standards for operating system (OS)-level benchmarks and vulnerability scans based on customer requirements within a few hours. Customers that request this consulting offer will participate in an engagement that delivers identification of eligible AMIs, template creation, and deployment.

Amazon EMR now supports Amazon EC2 Instance Metadata Service (IMDS) v2, in addition to v1, for all IMDS calls to EMR clusters. Instance metadata is data about your instance that you can use to configure or manage the running instance. IMDSv1 is fully secure and AWS will continue to support it. But IMDSv2 adds new “belt and suspenders” protections for four types of vulnerabilities that could be used to try to access the IMDS. For more information, please read the
AWS Security blogpost .

Amazon SageMaker Autopilot, which makes it easy to create highly accurate machine learning models, now provides a model explainability report generated by Amazon SageMaker Clarify, making it easier to understand and explain how the models you create with SageMaker Autopilot make predictions. Explainability reports include feature importance values so you can understand how each attribute in your training data contributes to the predicted result as a percentage. The higher the percentage, the more strongly that feature impacts your model’s predictions. You can download the explainability report as a human readable file, view model properties including feature importance in Amazon SageMaker Studio, or access feature importance using the SageMaker Autopilot APIs .

AWS Data Exchange now enables providers to copy descriptions, data sets, and public offers from their existing products to a new product. With this launch, providers can select any existing product, published or unpublished, and copy all of its details to a new draft product, which they can then modify as needed and publish within a few seconds. For providers that frequently create new products that share similar metadata, this will save a significant amount of time and effort.

We are pleased to announce the launch of Building Data Lakes on AWS. Data lakes enable organizations to generate business value by identifying and acting upon opportunities for business growth. This new one-day classroom course will help you practice building a data lake in a hands-on environment.  An expert instructor will teach you how to build and secure a data lake using AWS Lake Formation while also showing you how to optimize for cost and performance.