Amazon CloudWatch Logs Insights expands natural language query result summarization to 15 additional AWS Regions: Asia Pacific (Hong Kong, Malaysia, Mumbai, Thailand, Tokyo, Singapore, Sydney), Europe (Frankfurt, Ireland, London, Spain, Stockholm), South America (Sao Paulo). US East (Ohio), and US West (Oregon).

CloudWatch Logs Insights lets you interactively search and analyze your logs with Logs Insights query language, OpenSearch Service Piped Processing Language (PPL), and OpenSearch Service Structured Query Language (SQL). The query result summarization capability generates a natural language summary of the query results, providing users with clear, actionable insights. Interpreting log entries can be time-consuming and this feature transforms complex query results into concise summaries that help you quickly identify issues and gain actionable insights from your log data.

In addition, natural language query generation capability in CloudWatch Logs is now available for CloudWatch Logs Insights and Metrics Insights in 6 additional AWS Regions: Asia Pacific (Malaysia, Mumbai, Thailand), Europe (London, Spain), and South America (Sao Paulo). Query generation for PPL and SQL languages is available in 3 additional AWS Regions: Asia Pacific (Mumbai), Europe (London), and South America (Sao Paulo). Natural language query generation powered by generative AI allows users to use plain English to quickly generate queries in the context of their logs without needing extensive knowledge of the query language, reducing time to gather insights.

To learn about the log summarizer in CloudWatch Logs Insights, visit the Amazon CloudWatch Logs documentation. To learn about natural language query generation, go to CloudWatch Logs documentation. 

 

​Amazon CloudWatch Logs Insights expands natural language query result summarization to 15 additional AWS Regions: Asia Pacific (Hong Kong, Malaysia, Mumbai, Thailand, Tokyo, Singapore, Sydney), Europe (Frankfurt, Ireland, London, Spain, Stockholm), South America (Sao Paulo). US East (Ohio), and US West (Oregon). CloudWatch Logs Insights lets you interactively search and analyze your logs with Logs Insights query language, OpenSearch Service Piped Processing Language (PPL), and OpenSearch Service Structured Query Language (SQL). The query result summarization capability generates a natural language summary of the query results, providing users with clear, actionable insights. Interpreting log entries can be time-consuming and this feature transforms complex query results into concise summaries that help you quickly identify issues and gain actionable insights from your log data. In addition, natural language query generation capability in CloudWatch Logs is now available for CloudWatch Logs Insights and Metrics Insights in 6 additional AWS Regions: Asia Pacific (Malaysia, Mumbai, Thailand), Europe (London, Spain), and South America (Sao Paulo). Query generation for PPL and SQL languages is available in 3 additional AWS Regions: Asia Pacific (Mumbai), Europe (London), and South America (Sao Paulo). Natural language query generation powered by generative AI allows users to use plain English to quickly generate queries in the context of their logs without needing extensive knowledge of the query language, reducing time to gather insights.
To learn about the log summarizer in CloudWatch Logs Insights, visit the Amazon CloudWatch Logs documentation. To learn about natural language query generation, go to CloudWatch Logs documentation.   

AWS Security Incident Response is now Health Information Trust Alliance Common Security Framework (HITRUST CSF) certified, demonstrating its alignment with stringent security and privacy requirements established by HITRUST for managing sensitive data. This certification validates that AWS Security Incident Response meets comprehensive security controls required by healthcare, life sciences and many other regulated sectors.

HITRUST CSF is a comprehensive security and privacy framework developed by the HITRUST Alliance to help organizations in the healthcare industry and other regulated sectors effectively manage information risk and comply with a variety of security, privacy and regulatory requirements. It provides a scalable, transparent, and certifiable approach based on well-known industry standards and regulations, allowing organizations to demonstrate their commitment to protecting sensitive data and meeting compliance obligations. AWS customers can achieve HITRUST certification using AWS products and inherit AWS HITRUST scores, reducing the audit burden for both parties. Visit the AWS Services in Scope by Compliance Program to see a full list of services also covered by HITRUST.

AWS Security Incident Response automates security alert monitoring, streamlines incident response coordination, and provides direct access to 24/7 security experts, enabling organizations to efficiently detect, investigate, and mitigate security incidents. To learn more, see the AWS Security Incident Response documentation. Get started today by visiting AWS Security Incident Response via the console, AWS Command Line Interface, or APIs.

 

​AWS Security Incident Response is now Health Information Trust Alliance Common Security Framework (HITRUST CSF) certified, demonstrating its alignment with stringent security and privacy requirements established by HITRUST for managing sensitive data. This certification validates that AWS Security Incident Response meets comprehensive security controls required by healthcare, life sciences and many other regulated sectors. HITRUST CSF is a comprehensive security and privacy framework developed by the HITRUST Alliance to help organizations in the healthcare industry and other regulated sectors effectively manage information risk and comply with a variety of security, privacy and regulatory requirements. It provides a scalable, transparent, and certifiable approach based on well-known industry standards and regulations, allowing organizations to demonstrate their commitment to protecting sensitive data and meeting compliance obligations. AWS customers can achieve HITRUST certification using AWS products and inherit AWS HITRUST scores, reducing the audit burden for both parties. Visit the AWS Services in Scope by Compliance Program to see a full list of services also covered by HITRUST. AWS Security Incident Response automates security alert monitoring, streamlines incident response coordination, and provides direct access to 24/7 security experts, enabling organizations to efficiently detect, investigate, and mitigate security incidents. To learn more, see the AWS Security Incident Response documentation. Get started today by visiting AWS Security Incident Response via the console, AWS Command Line Interface, or APIs.  

AWS Security Incident Response now offers seamless integration with popular IT Service Management (ITSM) tools like Jira and ServiceNow, enabling you to respond faster to security incidents while maintaining your existing processes and operating models. These integrations provide bidirectional synchronization, allowing you to create, update, and delete issues in either platform with automatic data replication into AWS Security Incident Response cases. Comments and attachments are also fully synchronized between platforms.

The integrations are available as open-source projects on GitHub, providing customers and partners the opportunity to contribute to and extend the functionality. The repository includes sample code, deployment instructions, and best practices for building custom integrations with AWS Security Incident Response. The solution features a modular architecture that makes it straightforward to add new integration targets beyond the initial Jira and ServiceNow offerings. The GitHub repository includes guidance on how to leverage tools like Amazon Q Developer, Kiro, or similar AI assistants for rapid customization and use with your favorite ITSM platform.

To get started with AWS Security Incident Response ITSM Integrations, visit our GitHub repository. Visit our technical documentation for Jira and ServiceNow for implementation details. Learn more about AWS Security Incident Response in the service’s User Guide.

 

​AWS Security Incident Response now offers seamless integration with popular IT Service Management (ITSM) tools like Jira and ServiceNow, enabling you to respond faster to security incidents while maintaining your existing processes and operating models. These integrations provide bidirectional synchronization, allowing you to create, update, and delete issues in either platform with automatic data replication into AWS Security Incident Response cases. Comments and attachments are also fully synchronized between platforms. The integrations are available as open-source projects on GitHub, providing customers and partners the opportunity to contribute to and extend the functionality. The repository includes sample code, deployment instructions, and best practices for building custom integrations with AWS Security Incident Response. The solution features a modular architecture that makes it straightforward to add new integration targets beyond the initial Jira and ServiceNow offerings. The GitHub repository includes guidance on how to leverage tools like Amazon Q Developer, Kiro, or similar AI assistants for rapid customization and use with your favorite ITSM platform. To get started with AWS Security Incident Response ITSM Integrations, visit our GitHub repository. Visit our technical documentation for Jira and ServiceNow for implementation details. Learn more about AWS Security Incident Response in the service’s User Guide.  

AWS Clean Rooms now supports error message configurations for PySpark, enabling companies and their partners to develop and test sophisticated analytics faster in a Clean Rooms collaboration. With this launch, you and your partners can specify how much information appears in error messages for analyses that use PySpark, the Python API for Apache Spark. Code authors can configure a PySpark analysis to return detailed error messages when a PySpark analysis fails, provided that each collaboration member approves the analysis to run on their data. For example, when a code author is testing their code for a marketing attribution model in a clean rooms collaboration, they can enable detailed error messages for faster troubleshooting, reducing time-to-insights from weeks to hours or days.

AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.

 

​AWS Clean Rooms now supports error message configurations for PySpark, enabling companies and their partners to develop and test sophisticated analytics faster in a Clean Rooms collaboration. With this launch, you and your partners can specify how much information appears in error messages for analyses that use PySpark, the Python API for Apache Spark. Code authors can configure a PySpark analysis to return detailed error messages when a PySpark analysis fails, provided that each collaboration member approves the analysis to run on their data. For example, when a code author is testing their code for a marketing attribution model in a clean rooms collaboration, they can enable detailed error messages for faster troubleshooting, reducing time-to-insights from weeks to hours or days.
AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.  

Amazon Managed Service for Apache Flink now supports Amazon Key Management Service (KMS) Customer Managed Keys (CMK). Amazon Managed Service for Apache Flink has always provided encryption by default using AWS-owned KMS keys. Now, customers have the option to use their own Customer Managed Keys providing greater control on how they can encrypt data stored in MSF.

Amazon Managed Service for Apache Flink simplifies the development and operation of real-time data stream processing applications by eliminating the complexity of managing Flink infrastructure. Apache Flink is an open source framework and engine for processing data streams.

For Amazon Managed Service for Apache Flink region availability, refer to the AWS Region Table.

For detailed information about implementing Customer Managed Keys in Amazon Managed Service for Apache Flink, visit our documentation.

 

​Amazon Managed Service for Apache Flink now supports Amazon Key Management Service (KMS) Customer Managed Keys (CMK). Amazon Managed Service for Apache Flink has always provided encryption by default using AWS-owned KMS keys. Now, customers have the option to use their own Customer Managed Keys providing greater control on how they can encrypt data stored in MSF. Amazon Managed Service for Apache Flink simplifies the development and operation of real-time data stream processing applications by eliminating the complexity of managing Flink infrastructure. Apache Flink is an open source framework and engine for processing data streams. For Amazon Managed Service for Apache Flink region availability, refer to the AWS Region Table. For detailed information about implementing Customer Managed Keys in Amazon Managed Service for Apache Flink, visit our documentation.  

Amazon Managed Streaming for Apache Kafka (Amazon MSK) now supports Graviton3-based M7g instances for Standard brokers for MSK Provisioned clusters in AWS GovCloud (US-West), AWS GovCloud (US-East), Asia Pacific (Jakarta), Asia Pacific (Melbourne), Asia Pacific (Osaka), Europe (Zurich), Israel (Tel Aviv), and Asia Pacific (Hong Kong) Regions.

Graviton M7g instances for Standard brokers deliver up to 24% compute cost savings and up to 29% higher write and read throughput over comparable MSK clusters running on M5 instances.

Visit the AWS Regions page for all the regions where Amazon MSK is available. To learn more, check out our blog on M7g based Standard brokers. To get started, see the Amazon MSK Developer Guide.

 

​Amazon Managed Streaming for Apache Kafka (Amazon MSK) now supports Graviton3-based M7g instances for Standard brokers for MSK Provisioned clusters in AWS GovCloud (US-West), AWS GovCloud (US-East), Asia Pacific (Jakarta), Asia Pacific (Melbourne), Asia Pacific (Osaka), Europe (Zurich), Israel (Tel Aviv), and Asia Pacific (Hong Kong) Regions.
Graviton M7g instances for Standard brokers deliver up to 24% compute cost savings and up to 29% higher write and read throughput over comparable MSK clusters running on M5 instances.
Visit the AWS Regions page for all the regions where Amazon MSK is available. To learn more, check out our blog on M7g based Standard brokers. To get started, see the Amazon MSK Developer Guide.  

You can now generate AI-powered forecasts and visualizations on time-series data that has been indexed into Amazon OpenSearch 3.1+ domains.

Forecasts can be used to enhance various analytics use cases to power insights into trending infrastructure utilization and events, application or business metrics, and more. They can help you anticipate upcoming changes in areas such as business metrics, website traffic, system performance, and more. You can easily get started with this feature by setting up forecasts within OpenSearch dashboards or the OpenSearch UI. No data science or AI expertise is required.

AI-powered forecasts are available in all Amazon OpenSearch Service regions that support OpenSearch 3.1 domains. Learn more from the documentation.

 

​You can now generate AI-powered forecasts and visualizations on time-series data that has been indexed into Amazon OpenSearch 3.1+ domains. Forecasts can be used to enhance various analytics use cases to power insights into trending infrastructure utilization and events, application or business metrics, and more. They can help you anticipate upcoming changes in areas such as business metrics, website traffic, system performance, and more. You can easily get started with this feature by setting up forecasts within OpenSearch dashboards or the OpenSearch UI. No data science or AI expertise is required. AI-powered forecasts are available in all Amazon OpenSearch Service regions that support OpenSearch 3.1 domains. Learn more from the documentation.  

Today, AWS announces the general availability of AWS Billing and Cost Management Dashboards, a new feature within AWS Billing and Cost Management that helps you visualize and analyze your AWS spending in one consolidated view. This feature enables you to create customized dashboards that combine data from AWS Cost Explorer and Savings Plans and Reserved Instance coverage and utilization reports. With Billing and Cost Management Dashboards, you can quickly understand your AWS cost patterns and make informed financial decisions for your organization.

Billing and Cost Management Dashboards allows you to create and customize widgets using various widget types including cost widgets, usage widgets, Savings Plans utilization and coverage widgets, and Reserved Instances utilization and coverage widgets. Each widget can be configured with different visualizations, such as line chart, bar chart, stacked bar chart or tables, and you can customize dashboard layouts by adjusting widget sizes and positions. You can share these dashboards across accounts within or outside your organization, enabling FinOps teams to establish standardized cost reporting practices throughout their organization. 

AWS Billing and Cost Management Dashboards is available at no additional cost in all AWS commercial Regions, excluding AWS China Regions. To get started with AWS Billing and Cost Management Dashboards, visit the AWS Billing and Cost Management console and select «Dashboards» from the left navigation menu. For more information, see the AWS Billing and Cost Management Dashboards user guide or blog.

 

​Today, AWS announces the general availability of AWS Billing and Cost Management Dashboards, a new feature within AWS Billing and Cost Management that helps you visualize and analyze your AWS spending in one consolidated view. This feature enables you to create customized dashboards that combine data from AWS Cost Explorer and Savings Plans and Reserved Instance coverage and utilization reports. With Billing and Cost Management Dashboards, you can quickly understand your AWS cost patterns and make informed financial decisions for your organization.
Billing and Cost Management Dashboards allows you to create and customize widgets using various widget types including cost widgets, usage widgets, Savings Plans utilization and coverage widgets, and Reserved Instances utilization and coverage widgets. Each widget can be configured with different visualizations, such as line chart, bar chart, stacked bar chart or tables, and you can customize dashboard layouts by adjusting widget sizes and positions. You can share these dashboards across accounts within or outside your organization, enabling FinOps teams to establish standardized cost reporting practices throughout their organization. 
AWS Billing and Cost Management Dashboards is available at no additional cost in all AWS commercial Regions, excluding AWS China Regions. To get started with AWS Billing and Cost Management Dashboards, visit the AWS Billing and Cost Management console and select «Dashboards» from the left navigation menu. For more information, see the AWS Billing and Cost Management Dashboards user guide or blog.  

On August 5, 2025, AWS announced the availability of two new OpenAI models with open weights in Amazon Bedrock. Today, we’re simplifying access to these foundation models by making them automatically available to all Amazon Bedrock users, eliminating the need to explicitly enable model access. Customers can immediately start using these models through the Amazon Bedrock Console playground or through Amazon Bedrock’s unified API in AWS SDK in the regions where the models are available.

This streamlined access enables customers to quickly begin using OpenAI’s gpt-oss-120b and gpt-oss-20b models without needing to manually activate model access.Soon, we will extend this simplified access approach to other existing non-legacy serverless models in Amazon Bedrock. Going forward, Amazon Bedrock will launch all new serverless foundation models with default access for AWS accounts. Account administrators retain full control over model access through IAM policies and Service Control Policies (SCPs) to restrict model usage as needed.

For information about implementing access controls, visit our documentation and our blog post on implementing least privilege access in Amazon Bedrock. To get started with GPT-OSS models, visit the Amazon Bedrock Console.

 

 

 

 

 

​On August 5, 2025, AWS announced the availability of two new OpenAI models with open weights in Amazon Bedrock. Today, we’re simplifying access to these foundation models by making them automatically available to all Amazon Bedrock users, eliminating the need to explicitly enable model access. Customers can immediately start using these models through the Amazon Bedrock Console playground or through Amazon Bedrock’s unified API in AWS SDK in the regions where the models are available. This streamlined access enables customers to quickly begin using OpenAI’s gpt-oss-120b and gpt-oss-20b models without needing to manually activate model access.Soon, we will extend this simplified access approach to other existing non-legacy serverless models in Amazon Bedrock. Going forward, Amazon Bedrock will launch all new serverless foundation models with default access for AWS accounts. Account administrators retain full control over model access through IAM policies and Service Control Policies (SCPs) to restrict model usage as needed. For information about implementing access controls, visit our documentation and our blog post on implementing least privilege access in Amazon Bedrock. To get started with GPT-OSS models, visit the Amazon Bedrock Console.
 
 
 
   

Amazon announces the expansion of the TwelveLabs’ Pegasus 1.2 video understanding model to the US East (N. Virginia) and Asia Pacific (Seoul) AWS Regions. This expansion makes it easier for customers to build and scale generative AI applications that can understand and interact with video content at an enterprise level.

Pegasus 1.2 is a powerful video-first language model that can generate text based on the visual, audio, and textual content within video. Specifically designed for long-form video, it excels at video-to-text generation and temporal understanding. With Pegasus 1.2’s availability in these additional regions, you can now build video intelligence applications closer to your data and end users in key geographic locations, reducing latency and simplifying your architecture.

With today’s expansion, Pegasus 1.2 is now available in Amazon Bedrock across four regions: US West (Oregon), Europe (Ireland), US East (N. Virginia), and Asia Pacific (Seoul). To get started with Pegasus 1.2, visit the Amazon Bedrock console and request model access. To learn more, read the blog, product page, Amazon Bedrock pricing, and documentation. 

 

​Amazon announces the expansion of the TwelveLabs’ Pegasus 1.2 video understanding model to the US East (N. Virginia) and Asia Pacific (Seoul) AWS Regions. This expansion makes it easier for customers to build and scale generative AI applications that can understand and interact with video content at an enterprise level. Pegasus 1.2 is a powerful video-first language model that can generate text based on the visual, audio, and textual content within video. Specifically designed for long-form video, it excels at video-to-text generation and temporal understanding. With Pegasus 1.2’s availability in these additional regions, you can now build video intelligence applications closer to your data and end users in key geographic locations, reducing latency and simplifying your architecture. With today’s expansion, Pegasus 1.2 is now available in Amazon Bedrock across four regions: US West (Oregon), Europe (Ireland), US East (N. Virginia), and Asia Pacific (Seoul). To get started with Pegasus 1.2, visit the Amazon Bedrock console and request model access. To learn more, read the blog, product page, Amazon Bedrock pricing, and documentation.