Today, AWS announces the general availability of preprocessing queries for AWS Glue Data Quality, enabling you to transform your data before running data quality checks through AWS Glue Data Catalog APIs. This feature allows you to create derived columns, filter data based on specific conditions, perform calculations, and validate relationships between columns directly within your data quality evaluation process.
Preprocessing queries provide enhanced flexibility for complex data quality scenarios that require data transformation before validation. You can create derived metrics like calculating total fees from tax and shipping columns, limiting number of columns that are considered for data quality recommendations or filter datasets to focus quality checks on specific data subsets. This capability eliminates the need for separate data pre-processing steps, streamlining your data quality workflows.
AWS Glue Data Quality preprocessing queries are available through AWS Glue Data Catalog APIs – start-data-quality-rule-recommendation-run and start-data-quality-ruleset-evaluation-run, in all commercial AWS Regions where AWS Glue Data Quality is available. To learn more about preprocessing queries, see the Glue Data Quality documentation.
Today, AWS announces the general availability of preprocessing queries for AWS Glue Data Quality, enabling you to transform your data before running data quality checks through AWS Glue Data Catalog APIs. This feature allows you to create derived columns, filter data based on specific conditions, perform calculations, and validate relationships between columns directly within your data quality evaluation process.
Preprocessing queries provide enhanced flexibility for complex data quality scenarios that require data transformation before validation. You can create derived metrics like calculating total fees from tax and shipping columns, limiting number of columns that are considered for data quality recommendations or filter datasets to focus quality checks on specific data subsets. This capability eliminates the need for separate data pre-processing steps, streamlining your data quality workflows.
AWS Glue Data Quality preprocessing queries are available through AWS Glue Data Catalog APIs – start-data-quality-rule-recommendation-run and start-data-quality-ruleset-evaluation-run, in all commercial AWS Regions where AWS Glue Data Quality is available. To learn more about preprocessing queries, see the Glue Data Quality documentation.
AWS IoT Core announces a new capability to dynamically retrieve IoT thing registry data using an IoT rule, enhancing your ability to filter, enrich, and route IoT messages. Using the new get_registry_data() inline rule function, you can access IoT thing registry data, such as device attributes, device type, and group membership and leverage this information directly in IoT rules.
For example, your rule can filter AWS IoT Core connectivity lifecycle events and then retrieve thingattributes (such as «test» or «production» device) to inform routing of lifecycle events to different endpoints for downstream processing. You can also use this feature to enrich or route IoT messages with registry data from other devices. For instance, you can add a sensor’s threshold temperature from IoT thing registry to the messages relayed by its gateway.
To get started, connect your devices to AWS IoT Core and store your IoT device data in IoT thing registry. You can then use IoT rules to retrieve your registry data. This capability is available in all AWS regions where AWS IoT Core is present. For more information refer to the developer guide and API documentation.
AWS IoT Core announces a new capability to dynamically retrieve IoT thing registry data using an IoT rule, enhancing your ability to filter, enrich, and route IoT messages. Using the new get_registry_data() inline rule function, you can access IoT thing registry data, such as device attributes, device type, and group membership and leverage this information directly in IoT rules. For example, your rule can filter AWS IoT Core connectivity lifecycle events and then retrieve thing attributes (such as «test» or «production» device) to inform routing of lifecycle events to different endpoints for downstream processing. You can also use this feature to enrich or route IoT messages with registry data from other devices. For instance, you can add a sensor’s threshold temperature from IoT thing registry to the messages relayed by its gateway. To get started, connect your devices to AWS IoT Core and store your IoT device data in IoT thing registry. You can then use IoT rules to retrieve your registry data. This capability is available in all AWS regions where AWS IoT Core is present. For more information refer to the developer guide and API documentation.
Today, AWS announces the general availability of Amazon Quick Suite Embedded Chat, enabling you to embed Quick Suite’s conversational AI, which combines structured data and unstructured knowledge in a single conversation – directly into your applications, eliminating the need to build conversational interfaces, orchestration logic, or data access layers from scratch.
Quick Suite Embedded Chat solves a fundamental problem: users want answers where they work, not in another tool. Whether in a CRM, support console, or analytics portal, they need instant, contextual responses. Most conversational tools excel at either structured data or documents, analytics or knowledge bases, answering questions or performing actions—rarely all of the above. Quick Suite closes this gap. Now, users can reference a KPI, pull details from a file, check customer feedback, and trigger actions in one continuous conversation without leaving the embedded chat.
Embedded Chat brings this unified experience into your applications with simple integration, either through 1-click embedding or through API-based iframes for registered users with your existing authentication. You can connect your Agentic Chat to your data through connectors to search SharePoint, websites, send Slack messages, or create Jira tasks and customize the Agent with your brand colors, communication style, and personalized greetings. Security always stays under your control as you choose what the agent accesses and explicitly scope all actions.
Quick Suite Embedded Chat is available the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland), and we’ll expand availability to additional AWS Regions over the coming months. There is no additional cost for Quick Suite Embedded Chat. Existing Quick Suite pricing is available here.
To learn more, see Embedding Amazon Quick Suite launch blog. To get started with Amazon Quick Suite, visit the Amazon Quick Suite product page.
Today, AWS announces the general availability of Amazon Quick Suite Embedded Chat, enabling you to embed Quick Suite’s conversational AI, which combines structured data and unstructured knowledge in a single conversation – directly into your applications, eliminating the need to build conversational interfaces, orchestration logic, or data access layers from scratch.
Quick Suite Embedded Chat solves a fundamental problem: users want answers where they work, not in another tool. Whether in a CRM, support console, or analytics portal, they need instant, contextual responses. Most conversational tools excel at either structured data or documents, analytics or knowledge bases, answering questions or performing actions—rarely all of the above. Quick Suite closes this gap. Now, users can reference a KPI, pull details from a file, check customer feedback, and trigger actions in one continuous conversation without leaving the embedded chat. Embedded Chat brings this unified experience into your applications with simple integration, either through 1-click embedding or through API-based iframes for registered users with your existing authentication. You can connect your Agentic Chat to your data through connectors to search SharePoint, websites, send Slack messages, or create Jira tasks and customize the Agent with your brand colors, communication style, and personalized greetings. Security always stays under your control as you choose what the agent accesses and explicitly scope all actions. Quick Suite Embedded Chat is available the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland), and we’ll expand availability to additional AWS Regions over the coming months. There is no additional cost for Quick Suite Embedded Chat. Existing Quick Suite pricing is available here. To learn more, see Embedding Amazon Quick Suite launch blog. To get started with Amazon Quick Suite, visit the Amazon Quick Suite product page.
Amazon CloudFront announces support for mutual TLS Authentication (mTLS), a security protocol that requires both the server and client to authenticate each other using X.509 certificates, enabling customers to validate client identities at CloudFront’s edge locations. Customers can now ensure only clients presenting trusted certificates can access their distributions, helping protect against unauthorized access and security threats.
Previously, customers had to spend ongoing effort implementing and maintaining their own client access management solutions, leading to undifferentiated heavy lifting. Now with the support for mutual TLS, customers can easily validate client identities at the AWS edge before connections are established with their application servers or APIs. Example use cases include B2B secure API integrations for enterprises and client authentication for IoT. For B2B API security, enterprises can authenticate API requests from trusted third parties and partners using mutual TLS. For IoT use cases, enterprises can validate that devices are authorized to receive proprietary content such as firmware updates. Customers can leverage their existing third-party Certificate Authorities or AWS Private Certificate Authority to sign the X.509 certificates. With Mutual TLS, customers get the performance and scale benefits of CloudFront for workloads that require client authentication.
Mutual TLS authentication is available to all CloudFront customers at no additional cost. Customers can configure mutual TLS with CloudFront using the AWS Management Console, CLI, SDK, CDK, and CloudFormation. For detailed implementation guidance and best practices, visit CloudFront Mutual TLS (viewer) documentation.
Amazon CloudFront announces support for mutual TLS Authentication (mTLS), a security protocol that requires both the server and client to authenticate each other using X.509 certificates, enabling customers to validate client identities at CloudFront’s edge locations. Customers can now ensure only clients presenting trusted certificates can access their distributions, helping protect against unauthorized access and security threats. Previously, customers had to spend ongoing effort implementing and maintaining their own client access management solutions, leading to undifferentiated heavy lifting. Now with the support for mutual TLS, customers can easily validate client identities at the AWS edge before connections are established with their application servers or APIs. Example use cases include B2B secure API integrations for enterprises and client authentication for IoT. For B2B API security, enterprises can authenticate API requests from trusted third parties and partners using mutual TLS. For IoT use cases, enterprises can validate that devices are authorized to receive proprietary content such as firmware updates. Customers can leverage their existing third-party Certificate Authorities or AWS Private Certificate Authority to sign the X.509 certificates. With Mutual TLS, customers get the performance and scale benefits of CloudFront for workloads that require client authentication. Mutual TLS authentication is available to all CloudFront customers at no additional cost. Customers can configure mutual TLS with CloudFront using the AWS Management Console, CLI, SDK, CDK, and CloudFormation. For detailed implementation guidance and best practices, visit CloudFront Mutual TLS (viewer) documentation.
Today, Amazon EC2 announces interruptible Capacity Reservations to help you better utilize your reserved capacity and save costs. On-Demand Capacity Reservations (ODCRs) help you reserve compute capacity in a specific Availability Zone for any duration. When ODCRs are not in use, you can now make them temporarily available as interruptible ODCRs, enabling other workloads within your organization to utilize them while preserving your ability to reclaim the capacity for critical operations.
By repurposing unused capacity as interruptible ODCRs, workloads suitable for flexible, fault-tolerant operations—such as batch processing, data analysis, and machine learning training can benefit from temporarily available capacity. Reservation owners can reclaim their capacity at any time, while consumers of interruptible ODCRs will receive an interruption notice before termination to allow for graceful shutdown or checkpointing before.
Interruptible ODCRs are now available at no additional cost to all Capacity Reservations customers. Refer to the AWS Capabilities by Region website for the feature’s regional availability. CloudFormation support will be coming soon. For more details, please refer to the Capacity Reservations user guide.
Today, Amazon EC2 announces interruptible Capacity Reservations to help you better utilize your reserved capacity and save costs. On-Demand Capacity Reservations (ODCRs) help you reserve compute capacity in a specific Availability Zone for any duration. When ODCRs are not in use, you can now make them temporarily available as interruptible ODCRs, enabling other workloads within your organization to utilize them while preserving your ability to reclaim the capacity for critical operations. By repurposing unused capacity as interruptible ODCRs, workloads suitable for flexible, fault-tolerant operations—such as batch processing, data analysis, and machine learning training can benefit from temporarily available capacity. Reservation owners can reclaim their capacity at any time, while consumers of interruptible ODCRs will receive an interruption notice before termination to allow for graceful shutdown or checkpointing before. Interruptible ODCRs are now available at no additional cost to all Capacity Reservations customers. Refer to the AWS Capabilities by Region website for the feature’s regional availability. CloudFormation support will be coming soon. For more details, please refer to the Capacity Reservations user guide.
AWS Glue announces the general availability of catalog federation for remote Iceberg catalogs. This capability provides direct and secure access to Iceberg tables stored in Amazon S3 and cataloged in remote catalogs using AWS analytics engines.
With catalog federation, you can federate to remote Iceberg catalogs and query remote Iceberg tables using your preferred AWS analytics engines, without moving or copying tables. It synchronizes metadata real-time across AWS Glue Data Catalog and remote catalogs when data teams query remote tables, which means that query results are always completely up-to-date. You can now choose the best price-performance for your workloads when analyzing remote Iceberg tables using your preferred AWS analytics engines, while maintaining consistent security controls when discovering or querying data. Catalog federation is supported by a wide variety of analytics engines, including Amazon Redshift, Amazon EMR, Amazon Athena, AWS Glue, third-party engines like Apache Spark, and Amazon SageMaker with the serverless notebooks.
Catalog federation uses AWS Lake Formation for access controls, allowing you to use fine-grained access controls, cross-account sharing, and trusted identity propagation when sharing remote catalog tables with other data consumers. Catalog federation integrates with catalog implementations that support the Iceberg REST specifications.
Catalog federation is available in Lake Formation console and using AWS Glue and Lake Formation SDKs and APIs. This feature is generally available in all AWS commercial regions where AWS Glue and Lake Formation are available. With just a few clicks in the console, you can federate to remote catalogs, discover its databases and tables, grant permissions to access table data, and query remote Iceberg tables using AWS analytics engines. To learn more, visit the documentation.
AWS Glue announces the general availability of catalog federation for remote Iceberg catalogs. This capability provides direct and secure access to Iceberg tables stored in Amazon S3 and cataloged in remote catalogs using AWS analytics engines. With catalog federation, you can federate to remote Iceberg catalogs and query remote Iceberg tables using your preferred AWS analytics engines, without moving or copying tables. It synchronizes metadata real-time across AWS Glue Data Catalog and remote catalogs when data teams query remote tables, which means that query results are always completely up-to-date. You can now choose the best price-performance for your workloads when analyzing remote Iceberg tables using your preferred AWS analytics engines, while maintaining consistent security controls when discovering or querying data. Catalog federation is supported by a wide variety of analytics engines, including Amazon Redshift, Amazon EMR, Amazon Athena, AWS Glue, third-party engines like Apache Spark, and Amazon SageMaker with the serverless notebooks. Catalog federation uses AWS Lake Formation for access controls, allowing you to use fine-grained access controls, cross-account sharing, and trusted identity propagation when sharing remote catalog tables with other data consumers. Catalog federation integrates with catalog implementations that support the Iceberg REST specifications. Catalog federation is available in Lake Formation console and using AWS Glue and Lake Formation SDKs and APIs. This feature is generally available in all AWS commercial regions where AWS Glue and Lake Formation are available. With just a few clicks in the console, you can federate to remote catalogs, discover its databases and tables, grant permissions to access table data, and query remote Iceberg tables using AWS analytics engines. To learn more, visit the documentation.
Customers can now use Claude Opus 4.5 in Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies. Opus 4.5 is Anthropic’s newest model, setting new standards across coding, agentic workflows, computer use, and office tasks while making Opus-level intelligence accessible at one-third the cost.
Opus 4.5 excels at professional software engineering tasks, achieving state-of-the-art performance on SWE-bench. The model handles ambiguity, reasons about tradeoffs and can figure out fixes for bugs that require reasoning across multiple systems. It can help transform multi-day team development projects into hours-long tasks with improved multilingual coding capabilities. This generation of Claude spans the full development lifecycle: Opus 4.5 for production code and lead agents, Sonnet 4.5 for rapid iteration and scaled user experiences, Haiku 4.5 for sub-agents and free-tier products.
Beyond coding, the model powers agents that produce documents, spreadsheets, and presentations with consistency, professional polish, and domain awareness, making it ideal for finance and other precision-critical verticals. As Anthropic’s best vision model yet, it unlocks workflows that depend on complex visual interpretation and multi-step navigation. Through the Amazon Bedrock API, Opus 4.5 introduces two new capabilities: tool search and tool use examples. Together, these updates enable Claude to navigate large tool libraries and accurately execute complex tasks. A new effort parameter, available in beta, lets you control how much effort Claude allocates across thinking, tool calls, and responses to balance performance with latency, and cost.
Claude Opus 4.5 is now available in Amazon Bedrock via global cross region inference in multiple locations. For the full list of available regions, refer to the documentation. To get started with the model in Amazon Bedrock, read the launch blog or visit the Amazon Bedrock console.
Customers can now use Claude Opus 4.5 in Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies. Opus 4.5 is Anthropic’s newest model, setting new standards across coding, agentic workflows, computer use, and office tasks while making Opus-level intelligence accessible at one-third the cost. Opus 4.5 excels at professional software engineering tasks, achieving state-of-the-art performance on SWE-bench. The model handles ambiguity, reasons about tradeoffs and can figure out fixes for bugs that require reasoning across multiple systems. It can help transform multi-day team development projects into hours-long tasks with improved multilingual coding capabilities. This generation of Claude spans the full development lifecycle: Opus 4.5 for production code and lead agents, Sonnet 4.5 for rapid iteration and scaled user experiences, Haiku 4.5 for sub-agents and free-tier products. Beyond coding, the model powers agents that produce documents, spreadsheets, and presentations with consistency, professional polish, and domain awareness, making it ideal for finance and other precision-critical verticals. As Anthropic’s best vision model yet, it unlocks workflows that depend on complex visual interpretation and multi-step navigation. Through the Amazon Bedrock API, Opus 4.5 introduces two new capabilities: tool search and tool use examples. Together, these updates enable Claude to navigate large tool libraries and accurately execute complex tasks. A new effort parameter, available in beta, lets you control how much effort Claude allocates across thinking, tool calls, and responses to balance performance with latency, and cost. Claude Opus 4.5 is now available in Amazon Bedrock via global cross region inference in multiple locations. For the full list of available regions, refer to the documentation. To get started with the model in Amazon Bedrock, read the launch blog or visit the Amazon Bedrock console.
Today, AWS announces enhanced log analytics capabilities in Amazon OpenSearch Service, making Piped Processing Language (PPL) and natural language the default experience in OpenSearch UI’s Observability workspace. This update combines proven pipeline syntax with simplified workflows to deliver an intuitive observability experience, helping customers analyze growing data volumes while controlling costs. The new experience includes 35+ new commands for deep analysis, faceted exploration, and natural language querying to help customers gain deeper insights across infrastructure, security, and business metrics.
With this enhancement, customers can streamline their log analytics workflows using familiar pipeline syntax while leveraging advanced analytics capabilities. The solution includes enterprise-grade query capabilities, supporting advanced event correlation using natural language that help teams uncover meaningful patterns faster. Users can seamlessly move from query to visualization within a single interface, reducing mean time to detect and resolve issues. Admins can quickly stand up an end-to-end OpenTelemetry solution using OpenSearch’s Get Started workflow in the AWS console. The unified workflow includes out-of-the-box OpenSearch Ingestion pipelines for OpenTelemetry data, making it easier for teams to get started quickly.
Amazon OpenSearch UI is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Osaka), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Europe (Ireland), Europe (London), Europe (Frankfurt), Europe (Paris), Europe (Stockholm), Europe (Milan), Europe (Spain), Europe (Zurich), South America (São Paulo), and Canada (Central).
To learn more about the new OpenSearch log analytics experience, visit the OpenSearch Service observability documentation and start using these enhanced capabilities today in OpenSearch UI.
Today, AWS announces enhanced log analytics capabilities in Amazon OpenSearch Service, making Piped Processing Language (PPL) and natural language the default experience in OpenSearch UI’s Observability workspace. This update combines proven pipeline syntax with simplified workflows to deliver an intuitive observability experience, helping customers analyze growing data volumes while controlling costs. The new experience includes 35+ new commands for deep analysis, faceted exploration, and natural language querying to help customers gain deeper insights across infrastructure, security, and business metrics. With this enhancement, customers can streamline their log analytics workflows using familiar pipeline syntax while leveraging advanced analytics capabilities. The solution includes enterprise-grade query capabilities, supporting advanced event correlation using natural language that help teams uncover meaningful patterns faster. Users can seamlessly move from query to visualization within a single interface, reducing mean time to detect and resolve issues. Admins can quickly stand up an end-to-end OpenTelemetry solution using OpenSearch’s Get Started workflow in the AWS console. The unified workflow includes out-of-the-box OpenSearch Ingestion pipelines for OpenTelemetry data, making it easier for teams to get started quickly. Amazon OpenSearch UI is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Osaka), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Europe (Ireland), Europe (London), Europe (Frankfurt), Europe (Paris), Europe (Stockholm), Europe (Milan), Europe (Spain), Europe (Zurich), South America (São Paulo), and Canada (Central). To learn more about the new OpenSearch log analytics experience, visit the OpenSearch Service observability documentation and start using these enhanced capabilities today in OpenSearch UI.
Cinco de cada diez empresas colombianas ya utilizan agentes de IA para fortalecer su ciberseguridad
El 57% de las compañías usa agentes de IA en materia de ciberseguridad y 39% evalúa adoptarlos, en un contexto en el que seis de cada diez ya cuentan con políticas formales para su uso.
Bogotá, Colombia – Como parte de su compromiso con un futuro digital más seguro, Microsoft presenta los resultados de la encuesta “Ciberseguridad 2025: Desafíos y estrategias en la era de la IA para grandes empresas”, comisionada a Edelman. La encuesta, aplicada a 100 profesionales de seguridad y TI en Colombia, ofrece una visión actualizada sobre cómo las organizaciones están ajustando sus enfoques frente a los cambios del entorno digital.
Los hallazgos muestran que seis de cada diez especialistas en TI consideran las amenazas cibernéticas un reto concreto para sus organizaciones, en un contexto donde los riesgos digitales continúan en aumento. El 73% percibe que estas amenazas han crecido en los últimos dos o tres años y el 76% prevé que la ciberseguridad seguirá siendo una prioridad en sus compañías durante el mismo periodo.
La percepción del riesgo varía según el tipo de empresa. En las organizaciones nativas digitales, es decir, aquellas cuyo modelo de operación depende en gran medida de plataformas tecnológicas y servicios en la nube, el 66% reporta niveles altos de riesgo, lo que refleja una mayor exposición al entorno digital. En las empresas no nativas digitales, el 48% identifica un riesgo elevado y una proporción más amplia se ubica en niveles moderados o bajos, lo que sugiere diferencias en términos de madurez o sensibilidad frente al riesgo cibernético.
Además, entre los riesgos más mencionados por las organizaciones, el malware (software malicioso) con un 51% y el phishing (ataques diseñados para engañar a los usuarios y obtener información confidencial) con un 35%, se consolidan como las principales amenazas de ciberseguridad.
Ante este escenario, la inteligencia artificial (IA) empieza a ocupar un lugar central en la respuesta de las organizaciones. Más del 90% de las empresas considera que la IA será un factor de impacto en sus prácticas de ciberseguridad, al fortalecer la defensa frente a amenazas cada vez más sofisticadas. En la actualidad, cinco de cada diez compañías declaran tener una alta o moderada dependencia de la IA para enfrentar los riesgos de ciberseguridad.
Madurez de las estrategias de ciberseguridad en las empresas colombianas
En este contexto se ha evidenciado un fortalecimiento de las capacidades internas de las organizaciones a partir de la consolidación de estrategias formales. Actualmente, el 60% de las empresas colombianas cuenta con una estrategia definida de ciberseguridad y el 90% indica que este componente está integrado en la estrategia general de negocio.
La participación de los equipos directivos también fortalece este avance y refuerza la consolidación de nuevas estrategias. El 58% de las compañías reporta un alto involucramiento de la junta en decisiones relacionadas con ciberseguridad, nivel que alcanza el 69% en las organizaciones nativas digitales. En las no nativas digitales, el involucramiento se distribuye entre niveles altos y moderados.
Como resultado de estos avances, el 34% de las empresas encuestadas se considera altamente preparada frente al panorama actual de amenazas y el 46% se ubica en un nivel moderado de preparación. Entre las principales fortalezas se destacan la protección de datos (55%) y el cumplimiento normativo (46%), mientras que la capacitación de empleados continúa siendo un área pendiente por fortalecer (26%).
Este desarrollo se complementa con la incorporación de componentes claves dentro de las estrategias de seguridad. Entre los aspectos más presentes se encuentran la privacidad y protección de datos (75%), los planes de respuesta ante incidentes (70%) y la adopción de tecnologías basadas en IA (47%).
Agentes de IA: un nuevo capítulo en la estrategia de ciberseguridad
La evolución tecnológica continúa ampliando las herramientas disponibles para reforzar la seguridad digital. En este escenario, los agentes de IA han adquirido un papel destacado gracias a su capacidad para optimizar procesos críticos y mejorar la eficiencia operativa en distintos ámbitos.
Seis de cada diez empresas cuentan con políticas formales para el uso de agentes de IA y nueve de cada diez consideran que estos lineamientos son efectivos. Aunque la percepción de efectividad varía según el tipo de organización, en las empresas nativas digitales el 62% califica estas políticas como altamente efectivas, mientras que en las no nativas digitales este nivel alcanza solo el 38%.
En la práctica, el 57% ya utiliza agentes de IA en procesos de ciberseguridad y un 39% está evaluando incorporarlos, lo que evidencia un avance progresivo en su adopción. Esta incorporación no se limita al ámbito de la seguridad, ya que los agentes de IA también tienen presencia en otras áreas operativas: la atención al cliente registra un 61% de uso y la infraestructura de TI alcanza el 60%. Estas cifras muestran su integración en funciones que requieren agilidad y gestión continua, lo que a su vez contribuye a fortalecer los procesos de seguridad digital.
De acuerdo con la encuesta, el aporte de estas soluciones se observa en el fortalecimiento de tareas clave. Los agentes, diseñados para automatizar actividades, orquestar flujos y operar bajo salvaguardas, facilitan la reducción de tiempos de investigación, la priorización de alertas, la contención de incidentes y la aplicación consistente de políticas de identidad y datos. La combinación de modelos de IA con telemetría a gran escala y controles basados en Zero Trust contribuye a robustecer la postura de seguridad frente a amenazas cada vez más complejas.
“LA IA no solo aumenta la velocidad y el alcance de las defensas: a través de agentes especializados, ayuda a cerrar brechas de talento, estandarizar procesos y elevar la resiliencia del negocio. El desafío ya no es ‘si’ invertir en seguridad, sino cómo escalarla con IA y con gobernanza responsable en cada capa de la organización”, afirmó Marcelo Felman, Director de Ciberseguridad de Microsoft para América Latina.
Recomendaciones de ciberseguridad en la era de la IA:
Hacer de la Seguridad una Prioridad, gestionando el riesgo desde el nivel directivo.
Refuerza tu Higiene Digital: prioriza la protección de identidades, asegura el perímetro de tu empresa y monitorea continuamente los activos en la nube.
Fortalecer la Resiliencia digital: identifica tus vulnerabilidades, planifica con anticipación y capacítate para responder y recuperarte rápidamente ante cualquier brecha.
Invertir en las Personas, no solo en las herramientas.
Prepararse para el Futuro. Anticipar cambios, entender que no sabemos lo que no sabemos, y siempre intercambiar inteligencia de amenazas.
Hacer que la decisión más fácil, sea la correcta. En lugar de bloquear y prohibir, fomentar el uso seguro.
Acerca de Microsoft
Microsoft (Nasdaq «MSFT» @microsoft) crea plataformas y herramientas impulsadas por la IA para ofrecer soluciones innovadoras que satisfagan las necesidades cambiantes de nuestros clientes. La empresa de tecnología está comprometida con hacer que la IA esté ampliamente disponible, de manera responsable, con la misión de empoderar a cada persona y a cada organización en el planeta para lograr más.
Amazon Connect flow modules now support custom inputs, outputs, and branches, along with version and alias management. With this launch, you can now define flexible parameters for your reusable flow modules to math your specific business logic. For example, you can create an authentication module that accepts a phone number and PIN as inputs, then returns the customer name and authentication status as outputs with branches such as «authenticated» or «not authenticated». All parameters are customizable to meet your specific needs.
Additionally, advanced versioning and aliasing capabilities allow you to manage module updates more seamlessly. You can create immutable version snapshots and map aliases to specific versions. When you update an alias to point to a new version, all flows using that module automatically reference the updated version. These new features make flow modules more powerful and reusable, allowing you to build and maintain flows more efficiently.
Amazon Connect flow modules now support custom inputs, outputs, and branches, along with version and alias management. With this launch, you can now define flexible parameters for your reusable flow modules to math your specific business logic. For example, you can create an authentication module that accepts a phone number and PIN as inputs, then returns the customer name and authentication status as outputs with branches such as «authenticated» or «not authenticated». All parameters are customizable to meet your specific needs. Additionally, advanced versioning and aliasing capabilities allow you to manage module updates more seamlessly. You can create immutable version snapshots and map aliases to specific versions. When you update an alias to point to a new version, all flows using that module automatically reference the updated version. These new features make flow modules more powerful and reusable, allowing you to build and maintain flows more efficiently. To learn more about these 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.
