Amazon Connect now allows you to customize the visual appearance of the agent workspace. You can apply a custom theme, including a logo, font, and color palette for light and dark modes, so the agent workspace aligns with the brand identity of your company or business unit.
Contact center agents spend hours each day in the Amazon Connect agent workspace, which provides them with all of the customer information, applications, and step-by-step guidance they need to deliver superior customer experiences. With today’s launch, organizations can change the default Amazon Connect theme to their own branded experience, creating a more familiar and intuitive experience for agents who use the agent workspace and other company applications. The agent workspace also has a new header bar where agents can easily access their settings, including their preference of light and dark mode, contributing to greater agent satisfaction and efficiency.
The Amazon Connect agent workspace is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (London), and AWS GovCloud (US-West).
Amazon Connect now allows you to customize the visual appearance of the agent workspace. You can apply a custom theme, including a logo, font, and color palette for light and dark modes, so the agent workspace aligns with the brand identity of your company or business unit. Contact center agents spend hours each day in the Amazon Connect agent workspace, which provides them with all of the customer information, applications, and step-by-step guidance they need to deliver superior customer experiences. With today’s launch, organizations can change the default Amazon Connect theme to their own branded experience, creating a more familiar and intuitive experience for agents who use the agent workspace and other company applications. The agent workspace also has a new header bar where agents can easily access their settings, including their preference of light and dark mode, contributing to greater agent satisfaction and efficiency. The Amazon Connect agent workspace is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (London), and AWS GovCloud (US-West). To learn more and get started, see the administrator guide and developer guide.
AWS Clean Rooms now enables you and your partners to generate privacy-enhancing synthetic datasets from your collective data to train regression and classification machine learning (ML) models.
Synthetic dataset generation allows you and your partners to create training datasets with similar statistical properties to the original data, without the training code having access to real records. This new capability de-identifies subjects—such as people or entities about whom data has been collected—in the original data, mitigating the risk that a model will memorize information about individuals in the training data. This unlocks new ML model training use cases that were previously restricted by privacy concerns, such as campaign optimization, fraud detection, and medical research. For example, an airline with a proprietary algorithm wants to collaborate with a hotel brand to offer joint promotions to high-value customers, but neither organization wants to share sensitive consumer data. Using AWS Clean Rooms ML, they can generate a synthetic version of their collective dataset to train the model without exposing raw data—enabling more accurate promotions targeting while protecting customer privacy.
For more information about the AWS Regions where AWS Clean Rooms ML is available, see the AWS Regions table. To learn more, visit AWS Clean Rooms ML.
AWS Clean Rooms now enables you and your partners to generate privacy-enhancing synthetic datasets from your collective data to train regression and classification machine learning (ML) models.
Synthetic dataset generation allows you and your partners to create training datasets with similar statistical properties to the original data, without the training code having access to real records. This new capability de-identifies subjects—such as people or entities about whom data has been collected—in the original data, mitigating the risk that a model will memorize information about individuals in the training data. This unlocks new ML model training use cases that were previously restricted by privacy concerns, such as campaign optimization, fraud detection, and medical research. For example, an airline with a proprietary algorithm wants to collaborate with a hotel brand to offer joint promotions to high-value customers, but neither organization wants to share sensitive consumer data. Using AWS Clean Rooms ML, they can generate a synthetic version of their collective dataset to train the model without exposing raw data—enabling more accurate promotions targeting while protecting customer privacy.
For more information about the AWS Regions where AWS Clean Rooms ML is available, see the AWS Regions table. To learn more, visit AWS Clean Rooms ML.
Amazon SageMaker Catalog now provides automated data classification that suggests business glossary terms during data publishing, reducing manual tagging effort and improving metadata consistency across organizations.
This capability analyzes table metadata and schema information using Amazon Bedrock’s language models to recommend relevant terms from organizational business glossaries. Data producers receive AI-generated suggestions for business terms defined within their glossaries, which include both functional terms and sensitive data classifications such as PII and PHI, making it easy to tag their datasets with standardized vocabulary. Producers can accept or modify these suggestions before publishing, ensuring consistent terminology across data assets and improving data discoverability for business users.
Automated data classification is available in US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Tokyo, Seoul, Singapore, Sydney, Mumbai), and Europe (Frankfurt, Ireland, London, Paris) AWS regions where Amazon SageMaker operates.
To get started, go to SageMaker Unified Studio to configure your business glossary to generate recommendations for business glossary terms. You can also use the AWS CLI or SDKs to programmatically manage glossary term suggestions. For more information, see the SageMaker Catalog user guide.
Amazon SageMaker Catalog now provides automated data classification that suggests business glossary terms during data publishing, reducing manual tagging effort and improving metadata consistency across organizations. This capability analyzes table metadata and schema information using Amazon Bedrock’s language models to recommend relevant terms from organizational business glossaries. Data producers receive AI-generated suggestions for business terms defined within their glossaries, which include both functional terms and sensitive data classifications such as PII and PHI, making it easy to tag their datasets with standardized vocabulary. Producers can accept or modify these suggestions before publishing, ensuring consistent terminology across data assets and improving data discoverability for business users. Automated data classification is available in US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Tokyo, Seoul, Singapore, Sydney, Mumbai), and Europe (Frankfurt, Ireland, London, Paris) AWS regions where Amazon SageMaker operates. To get started, go to SageMaker Unified Studio to configure your business glossary to generate recommendations for business glossary terms. You can also use the AWS CLI or SDKs to programmatically manage glossary term suggestions. For more information, see the SageMaker Catalog user guide.
Amazon Connect now supports message streaming for AI-powered chat interactions. This new capability shows Connect AI agent responses as they’re being generated, which reduces perceived wait times and improves the customer experience.
When using Amazon Connect AI agents, customers see status updates like «One moment while I review your account» during processing, and watch responses appear progressively. This experience gives customers confidence their request is actively being worked on while AI agents reason, invoke tools, and craft comprehensive solutions.
Message streaming for AI-powered interactions is now available in the following regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (London) and Africa (Cape Town). To learn more, visit the Amazon Connect documentation.
Amazon Connect now supports message streaming for AI-powered chat interactions. This new capability shows Connect AI agent responses as they’re being generated, which reduces perceived wait times and improves the customer experience. When using Amazon Connect AI agents, customers see status updates like «One moment while I review your account» during processing, and watch responses appear progressively. This experience gives customers confidence their request is actively being worked on while AI agents reason, invoke tools, and craft comprehensive solutions. Message streaming for AI-powered interactions is now available in the following regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (London) and Africa (Cape Town). To learn more, visit the Amazon Connect documentation.
Amazon Connect now supports agent-initiated workflows, enabling agents to send interactive forms to collect sensitive data or share general policies and disclosures within customer chat conversations, increasing efficiency and improving customer experience. For example, when a customer needs to update their address, agents can now send a form that customers complete without leaving the chat interface.
Agents can trigger these workflows at any point during a chat conversation, making interactions more dynamic and responsive to customer needs. By handling everything within the ongoing chat conversation, businesses can maintain security and compliance standards while helping customers get faster solutions.
These new agent capabilities are now available in the following regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (London), and Africa (Cape Town). To learn more, visit the Amazon Connect documentation.
Amazon Connect now supports agent-initiated workflows, enabling agents to send interactive forms to collect sensitive data or share general policies and disclosures within customer chat conversations, increasing efficiency and improving customer experience. For example, when a customer needs to update their address, agents can now send a form that customers complete without leaving the chat interface. Agents can trigger these workflows at any point during a chat conversation, making interactions more dynamic and responsive to customer needs. By handling everything within the ongoing chat conversation, businesses can maintain security and compliance standards while helping customers get faster solutions. These new agent capabilities are now available in the following regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (London), and Africa (Cape Town). To learn more, visit the Amazon Connect documentation.
Amazon Connect now provides AI-powered case summaries for complete context into customer issues, reduce manual wrap-up work, and help resolve cases faster. With a single click, agents can generate a concise case summary even when the case spans multiple interactions, follow-up tasks, and teams, capturing key details such as issue background, actions taken, and next steps. Administrators can configure custom prompts and guardrails to ensure that summaries align with organizational style and preferences.
Amazon Connect Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town) AWS regions. To learn more and get started, visit the Amazon Connect Cases webpage and documentation.
Amazon Connect now provides AI-powered case summaries for complete context into customer issues, reduce manual wrap-up work, and help resolve cases faster. With a single click, agents can generate a concise case summary even when the case spans multiple interactions, follow-up tasks, and teams, capturing key details such as issue background, actions taken, and next steps. Administrators can configure custom prompts and guardrails to ensure that summaries align with organizational style and preferences. Amazon Connect Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town) AWS regions. To learn more and get started, visit the Amazon Connect Cases webpage and documentation.
Las 5 amenazas de seguridad de IA generativa que necesitan conocer detalladas en un nuevo libro electrónico
Por: Equipo de Microsoft Security.
La IA generativa transforma la manera en que operan los equipos de seguridad: acelera la detección de amenazas, automatiza flujos de trabajo y facilita la escalabilidad. Pero a medida que los defensores adoptan la IA para fortalecer su postura, los ciberatacantes hacen lo mismo para evolucionar más rápido de lo que las defensas tradicionales pueden adaptarse. El Informe de Amenazas Digitales de Microsoft 2025 reveló que ciberatacantes como Rusia, China, Irán y Corea del Norte han más que duplicado su uso de IA para llevar a cabo ciberataques y difundir desinformación. En la actualidad, la IA se utiliza para traducir correos de phishing al inglés fluido, generar vídeos deepfake de ejecutivos y automatizar malware que se adapta en tiempo real para evadir la detección.1
El cambio ya está en marcha:
El 66% de las organizaciones están en proceso de desarrollo o planean desarrollar aplicaciones personalizadas de IA generativa.2
El 88% de las organizaciones están algo o bastante preocupadas por los ataques indirectos de inyección inmediata.3
El 80% de los líderes empresariales cita la filtración de datos sensibles a través de la IA como una de las principales preocupaciones.4
Para ayudar a las organizaciones a navegar por este nuevo panorama, Microsoft ha publicado una nueva guía titulada 5 amenazas de seguridad de IA generativa que deben conocer. En esta entrada del blog, destacaremos los temas clave tratados en el libro electrónico, incluidos los desafíos a los que se enfrentan las organizaciones, las principales amenazas de IA generativa para las organizaciones y cómo las empresas pueden mejorar su postura de seguridad para hacer frente a los peligros de los entornos impredecibles de IA actuales.
Los líderes de seguridad se enfrentan a desafíos urgentes
A medida que la IA generativa se integra en los flujos de trabajo empresariales, los líderes de seguridad se enfrentan a un nuevo conjunto de desafíos que exigen un cambio de estrategia. No son solo obstáculos técnicos, son riesgos arquitectónicos, conductuales y operativos que requieren un enfoque más amplio y unificado de la seguridad.
Vulnerabilidades en la nube. La mayoría de las aplicaciones de IA generativa están basadas en la nube, lo que significa que los ciberatacantes pueden explotar debilidades en el modelo, la aplicación o la infraestructura para moverse de manera lateral y comprometer datos sensibles o la integridad del modelo.
Riesgos de exposición a datos. GenAI prospera con grandes conjuntos de datos, pero esa escala también la convierte en un objetivo principal. Los equipos de seguridad deben afrontar el riesgo de filtraciones de datos y la complejidad de hacer cumplir la gobernanza en entornos extensos.
Comportamiento impredecible de modelos. Los modelos de IA generativa no siempre se comportan de manera predecible. La misma entrada puede producir diferentes salidas, lo que dificulta anticipar cómo responderán los modelos a indicaciones maliciosas o manipulaciones. Esto abre la puerta a ataques de inyección provocados y al abuso de agentes de IA.
Estos riesgos fundamentales preparan el terreno para una realidad aún más urgente: a medida que la IA generativa crece, los ciberatacantes explotan sus debilidades únicas de formas que requieren la atención inmediata de los líderes de seguridad, y hay que empezar por las principales amenazas cibernéticas que ustedes deben vigilar.
Figura 1. Diapositiva que muestra los riesgos, superficies de ataque y vectores de amenaza de la IA generativa.
Amenazas críticas de IA generativa a vigilar
La IA generativa introduce una nueva clase de ciberamenazas que van más allá de las vulnerabilidades tradicionales en la nube, al atacar la propia arquitectura y el comportamiento de los sistemas de IA. Estos riesgos no son solo técnicos: desafían la confianza, integridad y resiliencia de los modelos en los que cada vez confían más las organizaciones. Los ciberatacantes han comenzado a encontrar formas creativas de explotar la naturaleza basada en datos de la IA, lo que convierte sus fortalezas en debilidades que exigen nuevas estrategias y defensas.
Entre las ciberamenazas más críticas están los ataques de envenenamiento, donde los ciberatacantes manipulan los datos de entrenamiento para distorsionar los resultados y erosionar la precisión. Los ataques de evasión toman un camino diferente, a través del uso de prompts de ofuscación o jailbreak para filtrar contenido dañino a través de los filtros de la IA. Y quizá lo más insidioso son los ataques de inyección inmediata: entradas elaboradas de manera cuidadosa que anulan las instrucciones originales, para guiar a los modelos hacia acciones no intencionadas o maliciosas. Estas ciberamenazas y otras subrayan por qué los líderes de seguridad deben replantearse los enfoques tradicionales y construir salvaguardas específicas para la IA. Para profundizar en amenazas críticas y orientaciones prácticas sobre mitigación, lean la guía completa de Microsoft: 5 amenazas de seguridad de IA generativa que deben conocer.
Construir una defensa proactiva para entornos de IA y multi nube
La ciberseguridad moderna requiere un enfoque holístico que correlacione las señales entre aplicaciones, infraestructura y comportamiento del usuario. En el libro electrónico, exploramos cómo las plataformas de protección de aplicaciones nativas en la nube (CNAPP, por sus siglas en inglés) simplifican esta complejidad a través de la unificación de herramientas como la gestión de postura de seguridad en la nube (CSPM, por sus siglas en inglés), la gestión de derechos de infraestructura en la nube (CIEM, por sus siglas en inglés) y la plataforma de protección de cargas de trabajo en la nube (CWPP, por sus siglas en inglés) en una sola plataforma. Al unir datos de identidad, registros de almacenamiento, vulnerabilidades de código y exposición a internet, CNAPP proporciona a los equipos de seguridad un contexto completo para detectar y remediar las ciberamenazas más rápido. Esta visión integrada es fundamental, ya que la IA generativa introduce comportamientos impredecibles, lo que hace insuficientes las defensas tradicionales aisladas.
Microsoft Defender for Cloud ejemplifica este modelo proactivo al ofrecer seguridad de IA de extremo a extremo durante el desarrollo y la ejecución. Escanea los repositorios de código en busca de configuraciones incorrectas, monitoriza imágenes de contenedores en busca de vulnerabilidades y mapea de manera continua las rutas de ataque a activos sensibles. En tiempo de ejecución, Defender for Cloud detecta amenazas específicas de IA como ataques de jailbreak, robo de credenciales y filtraciones de datos, para aprovechar más de 100 billones de señales diarias de Microsoft Threat Intelligence.2 Al combinar la gestión de postura con la protección contra amenazas en tiempo real, las organizaciones pueden asegurar cargas de trabajo de IA generativa y mantener la confianza en un panorama de ciberamenazas en evolución.
Redefinir la seguridad para la era de la IA generativa
A medida que la IA generativa se convierte en algo fundamental, los líderes en seguridad deben evolucionar sus estrategias. Microsoft ayuda a las organizaciones a unificar la seguridad y la gobernanza a lo largo de todo el ciclo de vida de las aplicaciones en la nube y la IA. Con visibilidad completa, priorización proactiva de riesgos y detección y respuesta en tiempo real, Microsoft protege sus activos modernos en la nube y la IA desde el código hasta el tiempo de ejecución, mientras les ayuda a cumplir con las normativas y estándares en evolución.
Organizaciones como Icertis ya han comenzado a actuar.
Microsoft Defender for Cloud surgió como nuestra elección natural como primera línea de defensa frente a amenazas relacionadas con la IA. Evalúa con cuidado la seguridad de nuestros despliegues de Azure OpenAI, monitoriza los patrones de uso y nos alerta con rapidez sobre posibles amenazas. Estas capacidades permiten a nuestros equipos del Centro de Operaciones de Seguridad (SOC, por sus siglas en inglés) tomar decisiones más informadas basadas en las detecciones de IA, lo que asegura que nuestra gestión de contratos impulsada por IA siga segura, fiable y que esté por delante de amenazas emergentes.
—Subodh Patil, Arquitecto Principal de Ciberseguridad, Icertis
La IA generativa transforma la ciberseguridad: empodera a los defensores mientras ofrece a los ciberatacantes nuevas herramientas para escalar phishing, deepfakes y malware adaptativo. Para entender las principales ciberamenazas impulsadas por IA y cómo mitigarlas, consigan el libro electrónico: 5 amenazas de seguridad de IA generativa que deben conocer.
Exploren más recursos:
Descubran más sobre las soluciones de seguridad en la nube de Microsoft.
Para saber más sobre las soluciones de seguridad de Microsoft, visiten nuestra página web. Guarden el blog de Seguridad en sus Favoritos para estar al día con nuestra cobertura experta sobre temas de seguridad. Además, síganos en LinkedIn (Microsoft Security) y X (@MSFTSecurity) para las noticias y actualizaciones más recientes sobre ciberseguridad.
2Acelerar la transformación de la IA con una seguridad sólida: El camino para adoptar de forma segura la adopción de la IA en tu organización, Microsoft Security.
Amazon Aurora PostgreSQL-Compatible Edition has added support for PostgreSQL versions 17.6, 16.10, 15.14, 14.19, and 13.22. The update includes the PostgreSQL community’s product improvements and bug fixes, and also includes Aurora-specific enhancements.
Dynamic Data Masking (DDM) (16.10 and 17.6 only) is a new database-level security feature that protects sensitive data like personally identifiable information by masking column values dynamically at query time based on role-based policies, without altering the actual stored data. This release also includes a shared plan cache, improved performance and recovery-time-objective (RTO) and improvement for Global Database switchovers.
To use the new versions, create a new Aurora PostgreSQL-compatible database with just a few clicks in the Amazon RDS Management Console. You can also upgrade your existing database. Please review the Aurora documentation to learn more about upgrading. Refer to the Aurora version policy to help you to decide how often to upgrade and how to plan your upgrade process. These releases are available in all commercial AWS Regions and the AWS GovCloud (US) Regions.
Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.
Amazon Aurora PostgreSQL-Compatible Edition has added support for PostgreSQL versions 17.6, 16.10, 15.14, 14.19, and 13.22. The update includes the PostgreSQL community’s product improvements and bug fixes, and also includes Aurora-specific enhancements.
Dynamic Data Masking (DDM) (16.10 and 17.6 only) is a new database-level security feature that protects sensitive data like personally identifiable information by masking column values dynamically at query time based on role-based policies, without altering the actual stored data. This release also includes a shared plan cache, improved performance and recovery-time-objective (RTO) and improvement for Global Database switchovers. To use the new versions, create a new Aurora PostgreSQL-compatible database with just a few clicks in the Amazon RDS Management Console. You can also upgrade your existing database. Please review the Aurora documentation to learn more about upgrading. Refer to the Aurora version policy to help you to decide how often to upgrade and how to plan your upgrade process. These releases are available in all commercial AWS Regions and the AWS GovCloud (US) Regions. Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.
AWS now supports deletion vectors and row lineage as defined in the Apache Iceberg Version 3 (V3) specification. These new features are available with Apache Spark on Amazon EMR 7.12, AWS Glue, Amazon SageMaker notebooks, Amazon S3 Tables, and the AWS Glue Data Catalog.
These Iceberg V3 capabilities help customers build petabyte-scale data lakes with improved performance for data modifications and functionality to easily track changed records. Deletion vectors write optimized delete files that speed up data pipelines and reduce data compaction costs. Row lineage provides metadata fields on each record to track changes with a simple SQL query, eliminating the computational expense of finding small changes in large tables.
Get started creating V3 tables by setting the table property to ‘format-version = 3’ in the CREATE TABLE command in Spark or a SageMaker notebook. To upgrade existing tables, simply update the table property in metadata with the new format version. When you do this, AWS query engines that support V3 will automatically begin to use deletion vectors and row lineage.
Iceberg V3 deletion vectors and row lineage are now available in all AWS Regions where each respective service/feature—Amazon EMR, AWS Glue, SageMaker notebooks, S3 Tables, and AWS Glue Data Catalog—is supported. To learn more about AWS support for Iceberg V3, visit Apache Iceberg V3 on AWS, and read the blog post.
AWS now supports deletion vectors and row lineage as defined in the Apache Iceberg Version 3 (V3) specification. These new features are available with Apache Spark on Amazon EMR 7.12, AWS Glue, Amazon SageMaker notebooks, Amazon S3 Tables, and the AWS Glue Data Catalog. These Iceberg V3 capabilities help customers build petabyte-scale data lakes with improved performance for data modifications and functionality to easily track changed records. Deletion vectors write optimized delete files that speed up data pipelines and reduce data compaction costs. Row lineage provides metadata fields on each record to track changes with a simple SQL query, eliminating the computational expense of finding small changes in large tables. Get started creating V3 tables by setting the table property to ‘format-version = 3’ in the CREATE TABLE command in Spark or a SageMaker notebook. To upgrade existing tables, simply update the table property in metadata with the new format version. When you do this, AWS query engines that support V3 will automatically begin to use deletion vectors and row lineage. Iceberg V3 deletion vectors and row lineage are now available in all AWS Regions where each respective service/feature—Amazon EMR, AWS Glue, SageMaker notebooks, S3 Tables, and AWS Glue Data Catalog—is supported. To learn more about AWS support for Iceberg V3, visit Apache Iceberg V3 on AWS, and read the blog post.
AWS announces a new warm storage tier for Amazon Kinesis Video Streams (Amazon KVS), delivering cost-effective storage for extended media retention. The standard Amazon KVS storage tier, now designated as the hot tier, remains optimized for real-time data access and short-term storage. The new warm tier enables long-term media retention with sub-second access latency at reduced storage costs.
The warm storage tier enables developers of home security and enterprise video monitoring solutions to cost-effectively stream data from devices, cameras, and mobile phones while maintaining extended retention periods for video analytics and regulatory compliance. Moreover, developers now have the flexibility to configure fragment sizes based on their specific requirements — selecting smaller fragments for lower latency use cases or larger fragments to reduce ingestion costs. Both hot and warm storage tiers integrate seamlessly with Amazon Rekognition Video and Amazon SageMaker, enabling continuous data processing to support the creation of computer vision and video analytics applications.
Amazon Kinesis Video Streams with the new warm storage tier is available in all regions where Amazon Kinesis Video Streams is available, except the AWS GovCloud (US) Regions.
AWS announces a new warm storage tier for Amazon Kinesis Video Streams (Amazon KVS), delivering cost-effective storage for extended media retention. The standard Amazon KVS storage tier, now designated as the hot tier, remains optimized for real-time data access and short-term storage. The new warm tier enables long-term media retention with sub-second access latency at reduced storage costs. The warm storage tier enables developers of home security and enterprise video monitoring solutions to cost-effectively stream data from devices, cameras, and mobile phones while maintaining extended retention periods for video analytics and regulatory compliance. Moreover, developers now have the flexibility to configure fragment sizes based on their specific requirements — selecting smaller fragments for lower latency use cases or larger fragments to reduce ingestion costs. Both hot and warm storage tiers integrate seamlessly with Amazon Rekognition Video and Amazon SageMaker, enabling continuous data processing to support the creation of computer vision and video analytics applications. Amazon Kinesis Video Streams with the new warm storage tier is available in all regions where Amazon Kinesis Video Streams is available, except the AWS GovCloud (US) Regions. To learn more, refer to the getting started guide.
