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Amazon Connect Contact Lens now provides sensitive data redaction in 7 additional languages

Amazon Connect Contact Lens now provides sensitive data redaction from voice and chat conversational analytics in French (France, Canada), Portuguese (Portugal, Brazil), Italian, German, and Spanish (Spain). Automatic redaction of sensitive data redaction helps you protect your customer’s privacy by removing personally identifiable information (PII), financial account numbers and PINs, and Internet access details (URLs, usernames, passwords) from conversation transcripts and audio files. You can choose to redact selected or all sensitive data entities, and whether they are replaced with a generic placeholder (e.g., [PII]) or an entity-specific placeholder (e.g., [NAME]) to indicate the type of information redacted.

Amazon Connect is an AI-powered application that provides one seamless experience for your contact center customers and users. Contact Lens provides conversational analytics that enable you to monitor, measure, and continuously improve contact quality and agent performance for a better overall customer experience.

Sensitive data redaction is available in all AWS Regions where Amazon Connect is available. For more information, refer to the following list of resources:

 

​Amazon Connect Contact Lens now provides sensitive data redaction from voice and chat conversational analytics in French (France, Canada), Portuguese (Portugal, Brazil), Italian, German, and Spanish (Spain). Automatic redaction of sensitive data redaction helps you protect your customer’s privacy by removing personally identifiable information (PII), financial account numbers and PINs, and Internet access details (URLs, usernames, passwords) from conversation transcripts and audio files. You can choose to redact selected or all sensitive data entities, and whether they are replaced with a generic placeholder (e.g., [PII]) or an entity-specific placeholder (e.g., [NAME]) to indicate the type of information redacted. Amazon Connect is an AI-powered application that provides one seamless experience for your contact center customers and users. Contact Lens provides conversational analytics that enable you to monitor, measure, and continuously improve contact quality and agent performance for a better overall customer experience. Sensitive data redaction is available in all AWS Regions where Amazon Connect is available. For more information, refer to the following list of resources:

Amazon Connect Contact Lens and pricing
Enable redaction of sensitive information
Supported languages  

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Amazon Connect flow designer now supports analytics mode

Amazon Connect now offers new enhanced analytics in the drag-and-drop flow designer that help you make data-driven decisions when building and optimizing your flows. Amazon Connect flows allow you to create end-to-end self-service and automated customer experiences such as interactive voice response (IVR), step-by-step guides, and back office processes and tasks. With this launch, you can now view aggregate metrics on how customers move through each step in the flow including where they run into errors or abandon the experience. For example, you can see how many conversational AI interactions result in transfers to agent queues or when customers end up in the wrong queue because an error in the flow configuration. These new capabilities help you identify behavioral patterns and evaluate root causes, allowing you to deliver better outcomes for customers.

This new capability is included with Amazon Connect (with unlimited AI) pricing. To learn more about this feature, see the Amazon Connect Administrator Guide. This feature is available in all AWS regions that offers Amazon Connect. To learn more about Amazon Connect, the AWS cloud-based contact center, please visit the Amazon Connect website.

 

​Amazon Connect now offers new enhanced analytics in the drag-and-drop flow designer that help you make data-driven decisions when building and optimizing your flows. Amazon Connect flows allow you to create end-to-end self-service and automated customer experiences such as interactive voice response (IVR), step-by-step guides, and back office processes and tasks. With this launch, you can now view aggregate metrics on how customers move through each step in the flow including where they run into errors or abandon the experience. For example, you can see how many conversational AI interactions result in transfers to agent queues or when customers end up in the wrong queue because an error in the flow configuration. These new capabilities help you identify behavioral patterns and evaluate root causes, allowing you to deliver better outcomes for customers. This new capability is included with Amazon Connect (with unlimited AI) pricing. To learn more about this feature, see the Amazon Connect Administrator Guide. This feature is available in all AWS regions that offers Amazon Connect. To learn more about Amazon Connect, the AWS cloud-based contact center, please visit the Amazon Connect website.  

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AWS launches SiteWise MCP Server for Simplified Modeling

AWS announces Model Context Protocol (MCP) server for AWS IoT SiteWise in the AWS Labs MCP open-source repository. This MCP server simplifies industrial data modeling by providing built-in domain validation and automated modeling capabilities, eliminating the need for extensive API knowledge. The server supports existing AWS IoT SiteWise functionality through familiar tools while adding new conversational interfaces for enhanced user interaction.

The server comes with built-in industrial knowledge and best practices, automatically applying proper units, data types, and quality indicators. This means your industrial models are production-ready from the start, saving valuable time and resources that would otherwise be spent on multiple iterations and corrections. This foundation enables you to easily implement advanced capabilities like anomaly detection and streamlined asset onboarding, while seamlessly integrating with your existing industrial systems.

Download SiteWise MCP server from AWS Labs MCP open-source repository. To get started, visit:
AWS Labs MCP repository on GitHub
SiteWise MCP server documentation

 

​AWS announces Model Context Protocol (MCP) server for AWS IoT SiteWise in the AWS Labs MCP open-source repository. This MCP server simplifies industrial data modeling by providing built-in domain validation and automated modeling capabilities, eliminating the need for extensive API knowledge. The server supports existing AWS IoT SiteWise functionality through familiar tools while adding new conversational interfaces for enhanced user interaction.
The server comes with built-in industrial knowledge and best practices, automatically applying proper units, data types, and quality indicators. This means your industrial models are production-ready from the start, saving valuable time and resources that would otherwise be spent on multiple iterations and corrections. This foundation enables you to easily implement advanced capabilities like anomaly detection and streamlined asset onboarding, while seamlessly integrating with your existing industrial systems.
Download SiteWise MCP server from AWS Labs MCP open-source repository. To get started, visit: AWS Labs MCP repository on GitHub SiteWise MCP server documentation  

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Amazon RDS for MySQL announces Innovation Release 9.4 in Amazon RDS Database Preview Environment

Amazon RDS for MySQL now supports community MySQL Innovation Release 9.4 in the Amazon RDS Database Preview Environment, allowing you to evaluate the latest Innovation Release on Amazon RDS for MySQL. You can deploy MySQL 9.4 in the Amazon RDS Database Preview Environment which provides the benefits of a fully managed database, making it simpler to set up, operate, and monitor databases.

MySQL 9.4 is the latest Innovation Release from the MySQL community. MySQL Innovation releases include bug fixes, security patches, as well as new features. MySQL Innovation releases are supported by the community until the next innovation minor, whereas MySQL Long Term Support (LTS) Releases, such as MySQL 8.0 and MySQL 8.4, are supported by the community for up to eight years. Please refer to the MySQL 9.4 release notes and Amazon RDS MySQL release notes for more details.

Amazon RDS Database Preview Environment supports both Single-AZ and Multi-AZ deployments on the latest generation of instance classes. Amazon RDS Database Preview Environment database instances are retained for a maximum of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots created in the Preview Environment can only be used to create or restore database instances within the Preview Environment.

Amazon RDS Database Preview Environment database instances are priced the same as production RDS instances created in the US East (Ohio) Region. For further information, see Working with the Database Preview Environment. To get started with the Preview Environment from the RDS console, navigate here.

 

​Amazon RDS for MySQL now supports community MySQL Innovation Release 9.4 in the Amazon RDS Database Preview Environment, allowing you to evaluate the latest Innovation Release on Amazon RDS for MySQL. You can deploy MySQL 9.4 in the Amazon RDS Database Preview Environment which provides the benefits of a fully managed database, making it simpler to set up, operate, and monitor databases. MySQL 9.4 is the latest Innovation Release from the MySQL community. MySQL Innovation releases include bug fixes, security patches, as well as new features. MySQL Innovation releases are supported by the community until the next innovation minor, whereas MySQL Long Term Support (LTS) Releases, such as MySQL 8.0 and MySQL 8.4, are supported by the community for up to eight years. Please refer to the MySQL 9.4 release notes and Amazon RDS MySQL release notes for more details. Amazon RDS Database Preview Environment supports both Single-AZ and Multi-AZ deployments on the latest generation of instance classes. Amazon RDS Database Preview Environment database instances are retained for a maximum of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots created in the Preview Environment can only be used to create or restore database instances within the Preview Environment. Amazon RDS Database Preview Environment database instances are priced the same as production RDS instances created in the US East (Ohio) Region. For further information, see Working with the Database Preview Environment. To get started with the Preview Environment from the RDS console, navigate here.  

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AWS Organizations supports full IAM policy language for service control policies (SCPs)

AWS Organizations now offers full IAM policy language support for service control policies (SCPs), enabling you to write SCPs with the same flexibility as IAM managed policies. With this launch, SCPs now support use of conditions, individual resource ARNs, and the NotAction element with Allow statements. Additionally, you can now use wildcards at the beginning or middle of Action element strings and the NotResource element.

With these policy language enhancements, you can now create more concise and precise policies to implement sophisticated permissions guardrails across your organization. For example, you can restrict access to specific resources with condition statements. The enhanced functionality maintains backward compatibility with existing SCPs, so no changes to current policies are required.

This feature is now available in all AWS commercial and AWS GovCloud (US) Regions.

To learn more about the enhanced SCP capabilities, see service control policies in the AWS Organizations User Guide and AWS blog.

 

​AWS Organizations now offers full IAM policy language support for service control policies (SCPs), enabling you to write SCPs with the same flexibility as IAM managed policies. With this launch, SCPs now support use of conditions, individual resource ARNs, and the NotAction element with Allow statements. Additionally, you can now use wildcards at the beginning or middle of Action element strings and the NotResource element. With these policy language enhancements, you can now create more concise and precise policies to implement sophisticated permissions guardrails across your organization. For example, you can restrict access to specific resources with condition statements. The enhanced functionality maintains backward compatibility with existing SCPs, so no changes to current policies are required. This feature is now available in all AWS commercial and AWS GovCloud (US) Regions. To learn more about the enhanced SCP capabilities, see service control policies in the AWS Organizations User Guide and AWS blog.  

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Amazon OpenSearch Ingestion now supports cross-account ingestion

Amazon OpenSearch Ingestion now supports cross-account ingestion for push-based sources such as HTTP and OpenTelemetry (OTel). With this launch, customers can easily share OpenSearch Ingestion pipelines across AWS accounts without relying on additional configurations like VPC peering or AWS Transit Gateway.

This capability makes it simpler for organizations with multiple accounts to centralize observability and analytics workflows. For example, a central logging team can create ingestion pipelines and grant access to development teams across different accounts, enabling them to ingest logs, metrics, and traces directly into OpenSearch domains or OpenSearch Serverless collections. This reduces operational overhead and lowers the cost of sharing ingestion pipelines across accounts.

Cross-account ingestion for Amazon OpenSearch Ingestion is available today in all AWS regions where OpenSearch Ingestion is offered. Customers can get started by creating resource policies in the AWS Management Console or using the AWS CLI, and then enabling pipeline endpoints from their VPCs to ingest data seamlessly.

To learn more about this feature, see the Amazon OpenSearch Service Developer Guide and the launch blog.

 

​Amazon OpenSearch Ingestion now supports cross-account ingestion for push-based sources such as HTTP and OpenTelemetry (OTel). With this launch, customers can easily share OpenSearch Ingestion pipelines across AWS accounts without relying on additional configurations like VPC peering or AWS Transit Gateway.
This capability makes it simpler for organizations with multiple accounts to centralize observability and analytics workflows. For example, a central logging team can create ingestion pipelines and grant access to development teams across different accounts, enabling them to ingest logs, metrics, and traces directly into OpenSearch domains or OpenSearch Serverless collections. This reduces operational overhead and lowers the cost of sharing ingestion pipelines across accounts.
Cross-account ingestion for Amazon OpenSearch Ingestion is available today in all AWS regions where OpenSearch Ingestion is offered. Customers can get started by creating resource policies in the AWS Management Console or using the AWS CLI, and then enabling pipeline endpoints from their VPCs to ingest data seamlessly.
To learn more about this feature, see the Amazon OpenSearch Service Developer Guide and the launch blog.  

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Amazon Redshift announces the general availability of Multidimensional Data Layouts

Amazon Redshift announces the general availability of Multidimensional Data Layouts (MDDL) that dynamically sort data based on actual query filters, accelerating query performance. Unlike traditional sorting methods that sort data based on fixed columns, MDDL sorts data based on query filters (for example, Sales in US), achieving up to 10x better end-to-end performance compared to using only optimal single-column sort keys for query workloads with repetitive query filters.

For each table with an AUTO sort key, which is the default for tables without an explicit sort key, Redshift analyzes the table’s query history and automatically selects either a single-column sort key or MDDL for your table, depending on potential performance improvements for your workload. Redshift with MDDL automatically constructs a multidimensional virtual sort key that co-locates rows typically accessed by the same queries. This virtual column, equivalent to a new sort key for the table, is subsequently used during query execution to skip data blocks and even skip scanning entire predicate columns. Redshift with MDDL provides a more expressive generalization of existing compound and interleaved sort keys that significantly improves the performance of table scans, especially when your query workload contains repetitive query filters. For pre-existing tables with manually defined sort keys, you can take advantage of MDDL by altering the sortkey of the table to AUTO.

MDDL is available in all AWS commercial regions where Redshift is available. To get started, read the documentation, blog, and Amazon Science publication to learn more about benchmarks on query performance improvements. 

 

​Amazon Redshift announces the general availability of Multidimensional Data Layouts (MDDL) that dynamically sort data based on actual query filters, accelerating query performance. Unlike traditional sorting methods that sort data based on fixed columns, MDDL sorts data based on query filters (for example, Sales in US), achieving up to 10x better end-to-end performance compared to using only optimal single-column sort keys for query workloads with repetitive query filters. For each table with an AUTO sort key, which is the default for tables without an explicit sort key, Redshift analyzes the table’s query history and automatically selects either a single-column sort key or MDDL for your table, depending on potential performance improvements for your workload. Redshift with MDDL automatically constructs a multidimensional virtual sort key that co-locates rows typically accessed by the same queries. This virtual column, equivalent to a new sort key for the table, is subsequently used during query execution to skip data blocks and even skip scanning entire predicate columns. Redshift with MDDL provides a more expressive generalization of existing compound and interleaved sort keys that significantly improves the performance of table scans, especially when your query workload contains repetitive query filters. For pre-existing tables with manually defined sort keys, you can take advantage of MDDL by altering the sortkey of the table to AUTO. MDDL is available in all AWS commercial regions where Redshift is available. To get started, read the documentation, blog, and Amazon Science publication to learn more about benchmarks on query performance improvements.   

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Cinco años que sacudieron el mundo de los negocios

septiembre 19, 2025

Cinco años que sacudieron el mundo de los negocios

Un extracto de la nueva antología de WorkLab

Composición abstracta en capas recortadas de papel que representa una cabeza con una apertura circular, en una gama de azules.

Por Colette Stallbaumer, cofundadora de WorkLab y directora general de Copilot.

Para comprender el mundo laboral actual impulsado por la IA, comiencen por observar los cinco años que nos llevaron aquí. En abril de 2020, cuando el mundo pasó al trabajo remoto casi de la noche a la mañana, lanzamos WorkLab para ayudar a los líderes a comprender los cambios radicales que redefinen los negocios. Desde entonces, nos hemos asociado con líderes de opinión, investigadores y ejecutivos de todas las industrias para arrojar luz sobre los patrones que remodelan el trabajo, desde el trabajo híbrido hasta las empresas fronterizas en el borde irregular de la reinvención de la IA.

Nadie puede predecir el futuro, solo podemos aprender sobre la marcha y compartir lo que aprendemos en el camino. Ahora, reunimos esas lecciones en una nueva antología de nuestros datos e historias más convincentes: WorkLab: Cinco años que sacudieron el mundo de los negocios y provocaron un futuro donde la IA es primero.

Estamos encantados de compartir que WorkLab llegará a los estantes el 21 de octubre. Asegúrense de reservar su copia hoy a través de Amazon, Bookshop.org, Barnes & Noble o su librería local favorita.

Lean a continuación para ver un extracto de WorkLab: Cinco años que sacudieron el mundo de los negocios y provocaron un futuro donde la IA es primero, por la cofundadora de Worklab, Colette Stallbaumer.

The post Cinco años que sacudieron el mundo de los negocios appeared first on Source LATAM.

 

​The post Cinco años que sacudieron el mundo de los negocios appeared first on Source LATAM.  

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Announcing AWS Neuron SDK 2.26.0

Today, AWS announces the general availability of Neuron SDK 2.26.0, delivering improvements for deep learning workloads on AWS Inferentia and Trainium-based instances. This release introduces support for PyTorch 2.8 and JAX 0.6.2, along with enhanced inference capabilities on Trainium2 (Trn2) instances. These updates enable developers to leverage the latest frameworks while benefiting from improved model deployment flexibility and performance optimizations.

With Neuron SDK 2.26.0, customers can now deploy FLUX.1-dev image generation model, along with Llama 4 Scout and Maverick variants (beta) on Trn2 instances. The release introduces expert parallelism support (beta) for efficient distribution of Mixture-of-Experts (MoE) models across multiple NeuronCores, and adds new capabilities through new Neuron Kernel Interface (NKI) APIs. The updated Neuron Profiler provides improved capabilities, including system profile grouping for distributed workloads.

The new SDK version is available in all AWS Regions supporting Inferentia and Trainium instances, offering enhanced performance and monitoring capabilities for machine learning workloads.

To learn more and for a full list of new features and enhancements, see:

 

​Today, AWS announces the general availability of Neuron SDK 2.26.0, delivering improvements for deep learning workloads on AWS Inferentia and Trainium-based instances. This release introduces support for PyTorch 2.8 and JAX 0.6.2, along with enhanced inference capabilities on Trainium2 (Trn2) instances. These updates enable developers to leverage the latest frameworks while benefiting from improved model deployment flexibility and performance optimizations. With Neuron SDK 2.26.0, customers can now deploy FLUX.1-dev image generation model, along with Llama 4 Scout and Maverick variants (beta) on Trn2 instances. The release introduces expert parallelism support (beta) for efficient distribution of Mixture-of-Experts (MoE) models across multiple NeuronCores, and adds new capabilities through new Neuron Kernel Interface (NKI) APIs. The updated Neuron Profiler provides improved capabilities, including system profile grouping for distributed workloads. The new SDK version is available in all AWS Regions supporting Inferentia and Trainium instances, offering enhanced performance and monitoring capabilities for machine learning workloads. To learn more and for a full list of new features and enhancements, see:

AWS Neuron 2.26.0 release notes
Trn2 Instances
Trn1 Instances
Inf2 Instances  

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Amazon Kinesis Data Streams expands Internet Protocol version 6 support to the AWS GovCloud (US) Regions

Amazon Kinesis Data Streams now allows customers to make API requests over Internet Protocol version 6 (IPv6) in the AWS GovCloud (US) Regions. Customers have the option of using either IPv6 or IPv4 when sending requests over dual-stack public or VPC endpoints. The new endpoints have also been validated under the Federal Information Processing Standard (FIPS) 140-3 program.

Kinesis Data Streams allows users to capture, process, and store data streams in real time at any scale. IPv6 increases the number of available addresses by several orders of magnitude, so customers will no longer need to manage overlapping address spaces. Many devices and networks today already use IPv6, and now they can easily write to and read from data streams. FIPS-compliant endpoints help companies contracting with the US federal governments meet the FIPS security requirement to encrypt sensitive data in supported Regions.

Support for IPv6 with Kinesis Data Streams is now available in all Regions where Kinesis Data Streams is available, including AWS GovCloud (US) and China Regions. See here for a full listing of our Regions. To learn more about Kinesis Data Streams, please refer to our Developer Guide.

 

​Amazon Kinesis Data Streams now allows customers to make API requests over Internet Protocol version 6 (IPv6) in the AWS GovCloud (US) Regions. Customers have the option of using either IPv6 or IPv4 when sending requests over dual-stack public or VPC endpoints. The new endpoints have also been validated under the Federal Information Processing Standard (FIPS) 140-3 program. Kinesis Data Streams allows users to capture, process, and store data streams in real time at any scale. IPv6 increases the number of available addresses by several orders of magnitude, so customers will no longer need to manage overlapping address spaces. Many devices and networks today already use IPv6, and now they can easily write to and read from data streams. FIPS-compliant endpoints help companies contracting with the US federal governments meet the FIPS security requirement to encrypt sensitive data in supported Regions. Support for IPv6 with Kinesis Data Streams is now available in all Regions where Kinesis Data Streams is available, including AWS GovCloud (US) and China Regions. See here for a full listing of our Regions. To learn more about Kinesis Data Streams, please refer to our Developer Guide.