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.   

Amazon Web Services (AWS) announces the availability of high performance Storage Optimized Amazon EC2 I7i instances in the AWS Europe (Frankfurt, London), Asia Pacific (Malaysia, Sydney, Tokyo) regions. Powered by 5th generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.2 GHz, these new instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. Powered by 3rd generation AWS Nitro SSDs, I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances.

I7i instances offer the best compute and storage performance for x86-based storage optimized instances in Amazon EC2, ideal for I/O intensive and latency-sensitive workloads that demand very high random IOPS performance with real-time latency to access the small to medium size datasets (multi-TBs). Additionally, torn write prevention feature support up to 16KB block sizes, enabling customers to eliminate database performance bottlenecks.

I7i instances are available in eleven sizes – nine virtual sizes up to 48xlarge and two bare metal sizes – delivering up to 100Gbps of network bandwidth and 60Gbps of Amazon Elastic Block Store (EBS) bandwidth.
To learn more, visit the I7i instances page. 

 

​Amazon Web Services (AWS) announces the availability of high performance Storage Optimized Amazon EC2 I7i instances in the AWS Europe (Frankfurt, London), Asia Pacific (Malaysia, Sydney, Tokyo) regions. Powered by 5th generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.2 GHz, these new instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. Powered by 3rd generation AWS Nitro SSDs, I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances. I7i instances offer the best compute and storage performance for x86-based storage optimized instances in Amazon EC2, ideal for I/O intensive and latency-sensitive workloads that demand very high random IOPS performance with real-time latency to access the small to medium size datasets (multi-TBs). Additionally, torn write prevention feature support up to 16KB block sizes, enabling customers to eliminate database performance bottlenecks. I7i instances are available in eleven sizes – nine virtual sizes up to 48xlarge and two bare metal sizes – delivering up to 100Gbps of network bandwidth and 60Gbps of Amazon Elastic Block Store (EBS) bandwidth. To learn more, visit the I7i instances page.   

Amazon Connect now supports multi-user web, in-app and video calling, allowing multiple users to join the same session with an agent through a web browser or mobile application. Contact center agents can dynamically add participants during a live call or multiple participants can join a scheduled session with the same agent. Participants can engage in audio, video, and screen sharing for a fully collaborative experience.

This capability helps organizations support scenarios such as joint financial planning between spouses, partners and advisors, family medical consultations, or conversations that involve legal representatives, translators, or subject matter experts. With this capability, you can enable a rich, inclusive interaction across stakeholders in a single session, reducing friction and improving the quality of support for complex engagements.

These new features are available in all AWS regions where Amazon Connect is offered. To learn more, visit our product page or refer to our Admin Guide.

 

​Amazon Connect now supports multi-user web, in-app and video calling, allowing multiple users to join the same session with an agent through a web browser or mobile application. Contact center agents can dynamically add participants during a live call or multiple participants can join a scheduled session with the same agent. Participants can engage in audio, video, and screen sharing for a fully collaborative experience. This capability helps organizations support scenarios such as joint financial planning between spouses, partners and advisors, family medical consultations, or conversations that involve legal representatives, translators, or subject matter experts. With this capability, you can enable a rich, inclusive interaction across stakeholders in a single session, reducing friction and improving the quality of support for complex engagements. These new features are available in all AWS regions where Amazon Connect is offered. To learn more, visit our product page or refer to our Admin Guide.