Microsoft Foundry Agent Service introduces defenses against memory poisoning to enhance agent context retention and security.
AutoJack is a novel exploit chain showing how a single malicious webpage can turn an AI browsing agent into a remote code execution vector on the host machine. By abusing trust in localhost, missing authentication, and unsafe parameter handling, attackers can trigger arbitrary process execution through AutoGen Studio’s MCP WebSocket. The research highlights a broader pattern - when agents can browse untrusted content and access local services, traditional boundaries like localhost are no longer secure. The post AutoJack: How a single page can RCE the host running your AI agent appeared first on Microsoft Security Blog.
New Forrester Total Economic Impact™ study shows Microsoft Security consolidation delivers ROI, lowers risk, and prepares organizations to secure AI. The post New Forrester study shows customers who unified with Microsoft Security benefited from 124% ROI appeared first on Microsoft Security Blog.
Reliable analytics starts with reliable connectivity. We understand that when moving your data, data security is of the utmost concern; we are committed to providing the most secure, cutting-edge data connectivity solutions; enabling you to have the confidence that your data is safe, secure, and reliable.As Microsoft Fabric continues to evolve into an end-to-end analytics platform, we are making focused investments to ensure Microsoft delivers enterprise-ready connectivity across the most widely used data platforms.Over the past year, customers have been clear about what they expect from connectivity: strong security posture, predictable behavior, and long-term support. In response, we have been modernizing how data connectors are built, shipped, and supported—placing a deliberate emphasis on securing the connector supply chain and clarifying the connector lifecycle.Securing the connector supply chainMicrosoft is committed to securing the connector supply chain. That’s why Microsoft is now bringing all connectors in-house. This commitment to directly providing our customers with the most secure and reliable connectors decreases long-term security and operational risk for our valued enterprise customers.Our approach moving forward is clear: to build and maintain Microsoft-owned, in-house connectors to:Provide the most secure, stable connectors for our customers.Ensure the highest quality connectors, equipped to most quickly enable new features, and capabilities as the connector evolves.Improve Microsoft security, compliance, and operational standards end-to-end.This shift aligns with Microsoft’s broader security commitments and ensures that connectivity is treated as a first-class platform capability within Fabric, solely managed by Microsoft.A clear connector lifecycle for Power BI’s data connectorsTo help customers understand why we are strengthening our connector supply chain and bringing more connectors in-house, we want to provide clear visibility into the conne
The Model Context Protocol exploded onto the scene because it's easy. Stand up a server, expose a few tools, point Claude or VS Code at it, and your agent can suddenly read files, hit APIs, and run c...Update Type: Announcement, Services: App Service, Categories:
A business-first approach to economics on Microsoft FabricEnterprise planning is evolving across industries - from an isolated finance exercise to a cross-functional capability that spans finance, operations, supply chain, HR, and executive leadership, but the tools and pricing models behind it have not kept up. Planning in Microsoft Fabric (Preview) is bringing planning, analytics, reporting, and data management into one integrated experience in Microsoft Fabric, moving organizations from disconnected processes to real-time, data-driven decisions.As customers adopt Fabric Planning, one question comes up: How does billing work and why is it designed this way? This blog post explains how the model is built for outcomes, impact, and predictability, not just usage.Why pricing for Fabric Planning is fundamentally different from traditional EPM solutionsEnterprise planning behaves differently from traditional analytics or data workloads. Analytics and data workloads are continuous, predictable, and incremental. Planning is cyclical with high-intensity usage during month-end, quarterly forecasting, and annual planning and lower levels of interaction between cycles. This breaks the two common pricing models:Pure consumption pricing undervalues planning, because the critical work happens in short bursts.Seat-based licensing feels rigid, forcing you to pay for users who participate only occasionally.Fabric Planning introduces a hybrid pricing model that reflects real-world usage patterns, with role and session-based pricing for users and job-based pricing for automation. It’s predictable during peak cycles and flexible across a broad set of participants.Figure: User roles segregated with different capabilities.Role-based pricingEach role contributes differently to the planning lifecycle, and the pricing reflects that:Viewers: Executives, decision-makers and business, who consume plans and insights. Read-only access; priced low to drive adoption.Stakeholders: departUpdate Typ
Multi Cloud Data integration and transformation made easy and painless by Fabric Data Factory.Update Type: GA, Services: Microsoft Fabric, Data Factory, Categories:
A couple of months ago I wrote about scaling MCP servers behind App Service's built-in load balancer. The trick back then was to lean on stateless HTTP transport so any instance could serve any reque...Update Type: Announcement, Services: App Service, Categories:
Complex cloud environments have outpaced manual operations. Agentic cloud operations connect people, tools, and data to streamline investigation workflows and move teams from scattered signals to evi...Update Type: GA, Services: , Categories:
What if your cloud environment could help you move from insight to action in real time, with systems already working through the next set of decisions?Update Type: Announcement, Services: Azure AI Services, Categories:
Azure SDK releases every month. In this post, you'll find this month's highlights and release notes. The post Azure SDK Release (May 2026) appeared first on Azure SDK Blog.Update Type: Announcement, Services: , Categories:
The new preview capabilities in Fabric Data Warehouse for approximate string matching, along with modern string-processing functions and operators simplify everyday string processing with T‑SQL language. Together, these additions help developers handle variation directly in SQL while improving query clarity and portability.Update Type: Announcement, Services: Microsoft Fabric, Categories:
Introduction Generative AI (GenAI) is poised to transform the construction industry by addressing chronic challenges such as low productivity, cost overruns, schedule delays, and labor shortages. B...Update Type: Announcement, Services: Azure AI Services, Categories:
We shipped a lot at Build 2026: hosted agents, Toolboxes, Foundry IQ, Memory, Managed Compute, fine‑tuning, Frontier Tuning, and a new evaluation and optimization stack. Read as a feature list, it is a lot to hold in your head. So here is a simpler way to see it: these are the parts you need to […] The post Outcome-driven learning systems: Enterprise RL with OpenEnv and Foundry appeared first on Microsoft Foundry Blog.Update Type: Announcement, Services: Microsoft Foundry, Categories:
The problem we're solving Previously, Microsoft Entra identities in Azure SQL Database could only be created as contained database users - principals scoped to a single database with no server-leve...Update Type: GA, Services: SQL Database, Categories:
Summer heat meets the world stageThe FIFA World Cup 2026 kicked off this month across 16 host cities in the United States, Mexico, and Canada—where it is currently summer, including in cities like Miami, Dallas, Houston, and Atlanta. These cities aren’t exactly known for mild June weather. Player safety protocols, fan comfort advisories, and broadcast scheduling all depend on real-time environmental conditions at each venue. The data exists (weather APIs publish readings every few minutes), but standing up parallel monitoring infrastructure for 11 cities?That’s traditionally a full afternoon’s worth of infrastructure setup, portal configurations and query and dashboard authoring.We built a real-time weather monitoring system for all 11 US-based World Cup venues in under five minutes using natural language prompts and Fabric Eventstream and Eventhouse AI Skills. No portal clicking. No copy-paste-modify-repeat.From 42+ steps to one sentenceBuilding a single Eventstream pipeline through the Fabric portal requires navigating menus, selecting source types, configuring properties, wiring operators, choosing destinations, and publishing. That’s roughly 42 interactions (clicks and text box entries) and more than five minutes per source. Multiply that by 11 cities and you’re looking at 60 minutes or more of repetitive work before any data starts flowing.With Eventstream AI Skills for Fabric, all we needed to do is type a single prompt describing the desired outcome:Create an eventstream named WorldCupUSCitiesWeatherFeed for all 11 US World Cup host cities including Miami, Dallas, Atlanta, Houston, Seattle, Los Angeles, Philadelphia, Kansas City, San Francisco, New York, and Boston. Ingest real-time weather for each city, filter for heat-stress conditions where relativeHumidity exceeds 70 percent, and land the filtered events in my WorldCupCities KQL database. Set the optional locationName field in the weather feed to the name of the city the weather feed is for.The AI skillU
Introduction A common pattern in large Azure deployments is to route VNet-to-VNet traffic through Microsoft Enterprise Edge (MSEE) routers. This happens when spoke VNets in a hub-and-spoke topology...Update Type: Announcement, Services: Virtual Network, Categories:
Authors: Shuo Qiu, Sydney Lister, Ilya Matiach, Ali Mahmoudzadeh, Salma Elshafey, José Santos, Vivek Bhadauria, Morteza Ziyadi, April Kwong Why Your Agent Needs a Task-Specific Evaluator Picture ...Update Type: Announcement, Services: Microsoft Foundry, Categories:
Co-authored with Lizet Pena, Caroline Mutua, Alvin Kua and Marco Sudahl How analytics rules, playbooks, workbooks, and hunting evolve in Defender—and why the new toolbelt makes detection engineerin...Update Type: Announcement, Services: Microsoft Sentinel, Categories:
As your data warehouse evolves with changing business needs, so does your schema. Whether you're onboarding new data sources, updating business logic, or scaling analytics models, schema updates—such as increasing column length or adjusting numeric precision are a normal part of operating a modern analytical warehouse. Now, even minor schema changes often require rebuilding tables and coordinating downstream deployments. A change as small as expanding a VARCHAR column can turn into a full operational effort impacting ingestion pipelines, CI/CD deployments, and reporting dependencies. Now, we’re introducing support for ALTER TABLE … ALTER COLUMN in Microsoft Fabric Data Warehouse (Preview), enabling supported schema changes directly on existing warehouse tables using familiar T‑SQL syntax. Evolve your schema without rewriting data With ALTER COLUMN support in Fabric Data Warehouse, you can now make supported changes to column definitions without requiring full table rebuilds or rewriting underlying Parquet data files. Capabilities: Expand column sizes as business requirements grow. Adjust numeric precision to reflect evolving calculations. Modify supported data types in place. Update schemas without breaking deployment pipelines. Maintain compatibility with downstream queries and reports. All while continuing to use the same, familiar T‑SQL experience. Why this matters for analytics teams Schema evolution is one of the most disruptive operational tasks in analytical environments. Traditionally in Fabric Data Warehouse, making even minor structural changes to warehouse tables often involves: Creating replacement tables. Copying existing data using CTAS. Reconfiguring ingestion pipelines. Updating dependent reports or semantic models. These workflows introduce deployment delays and increase tUpdate Type: Announcement, Services: Microsoft Fabric, Categories:
Amazon MQ for RabbitMQ now supports private networking, enabling your brokers to connect to private resources in your VPC without exposing those resources publicly.. This helps you meet your security and compliance requirements when your brokers need to reach private identity providers (such as LDAP and OAuth 2.0), other Amazon MQ for RabbitMQ brokers, or self-hosted RabbitMQ brokers. Previously, this connectivity for RabbitMQ Federation, Shovel, or authentication required Network Load Balancer and NAT Gateway workarounds. Amazon MQ establishes this connectivity using Amazon VPC Lattice, AWS Resource Access Manager (AWS RAM), and AWS PrivateLink, and manages the underlying infrastructure on your behalf. To get started, create a VPC Lattice resource gateway, package your resource configurations into an AWS RAM resource share, and associate it with your broker. Private networking is available only for Amazon MQ for RabbitMQ brokers, in all AWS Regions where Amazon VPC Lattice is available. To learn more, see Private networking in the Amazon MQ Developer Guide and the Amazon MQ pricing page.
When a security event occurs in your Amazon Web Services (AWS) environment, rapid response is critical. However security teams often struggle with time-consuming, manual processes that slow down investigations. Analysts must recall complex AWS Command Line Interface (AWS CLI) syntax for multiple services, manually correlate findings across Amazon GuardDuty, AWS CloudTrail, and other security tools, […]
Today, Amazon Elastic Kubernetes Service (Amazon EKS) introduces customer-routed control plane egress, a capability that lets you route outbound Kubernetes API server traffic through your own Amazon VPC. This includes admission webhook callbacks, OpenID Connect (OIDC) provider lookups, and aggregate API server requests. With customer-routed control plane egress, this traffic flows through your VPC, where you control the routing, security groups, and egress path. Organizations with data perimeter requirements, compliance mandates, or private network infrastructure can use customer-routed control plane egress to reach private OIDC providers and webhook servers that are accessible only within their VPC, and control how that traffic routes through their network. To get started, set controlPlaneEgressMode to CUSTOMER_ROUTED when creating a new cluster or updating an existing cluster. To enforce this configuration organization-wide, use the eks:controlPlaneEgressMode IAM condition key with AWS Organizations Service Control Policies. Customer-routed control plane egress is available at no additional cost in all AWS Regions where Amazon EKS is available. To learn more, see Configure control plane egress routing in the Amazon EKS User Guide.
Amazon Bedrock AgentCore Memory now enables cross-account access, allowing you to build multi-account architectures where memory resources and consuming agents span multiple AWS accounts. You can grant principals in one account permission to call memory data plane APIs against resources in another account using resource-based policies, and configure memory delivery destinations (Amazon S3, Amazon SNS, Amazon Kinesis Data Streams) that reside in a separate account. Cross-account access is configured by attaching a resource-based policy to your memory resource. Once configured, principals in the consuming account can create events, write memory records, retrieve records, and perform semantic search by referencing the full memory ARN. Cross-account delivery destinations allow your memory resource to deliver payloads and stream events to S3 buckets, SNS topics, and Kinesis Data Streams in other accounts. To get started, see Cross-account memory access in the Amazon Bedrock AgentCore Developer Guide. Amazon Bedrock AgentCore Memory cross-account access is available in all AWS Regions where Amazon Bedrock AgentCore Memory is supported.
AWS HealthOmics adds ephemeral storage for private workflows, giving bioinformatics workloads dedicated scratch space that delivers more consistent run performance and lower costs. Each workflow task now receives a dedicated local volume mounted at /tmp, and workflows that generate significant scratch data, such as genomic sequence alignment, BAM sorting, and variant calling, can experience faster run times. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed bioinformatics workflows. With this launch, workflow tasks can write temporary data to their own local volume, keeping scratch I/O isolated from shared run storage that hosts the working directory. By default, each task includes 16 GiB of ephemeral storage at no additional charge. You can increase the amount of ephemeral storage allocated to individual tasks, up to a maximum of 3,072 GiB per task, using the appropriate directive in your WDL, Nextflow, or CWL workflow definition. You can enable ephemeral storage at runtime with the StartRun API. All ephemeral storage volumes are encrypted and deleted when a task terminates. You can use ephemeral storage in all AWS Regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more about ephemeral storage, visit the AWS HealthOmics User Guide. For more information on pricing, visit AWS HealthOmics pricing.
Today, AWS announces new automated refinement workflows for Automated Reasoning checks in Amazon Bedrock Guardrails. Automated Reasoning checks use formal logic to mathematically validate the accuracy of generative AI responses against a policy you define, helping detect hallucinations and provide verifiable explanations. The quality of validation results depends on how well a policy is defined. The new workflows help customers improve their policies with less manual effort, leading to more reliable Guardrail validation results. The launch introduces two refinement workflows. With the iterative policy improvement workflow, customers who have created natural language tests for a policy can start an iterative refinement run, letting the system deduce the changes needed for the policy to pass those tests. With the ambiguity reduction workflow, customers who frequently encounter ambiguous translation results can run the resolve policy ambiguities workflow to automatically refine variable descriptions and type definitions, reducing how often ambiguous translations occur. Both workflows are available through the Amazon Bedrock APIs and in the AWS Management Console, where customers can start a workflow by choosing Refine policy on the policy page. These workflows are available in all AWS Regions where Automated Reasoning checks in Amazon Bedrock Guardrails are available. To learn more, visit the Amazon Bedrock Guardrails product page and the Automated Reasoning checks User Guide.
AWS Transform for migrations now supports all AWS commercial regions as migration targets. A migration target region is the AWS region where migrated resources are deployed, including landing zones, network infrastructure, and server rehosting. Customers can now deploy workloads in any commercial region, making it easier to meet data residency requirements. The new migration target regions are: US East (N. California), Africa (Cape Town), Asia Pacific (Bangkok), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Kuala Lumpur), Asia Pacific (Melbourne), Asia Pacific (New Zealand), Asia Pacific (Taipei), Canada (Calgary), Europe (Milan), Europe (Spain), Europe (Zurich), Mexico (Querétaro) and Middle East (Tel Aviv). Target region selection is available in the AWS Transform for migrations workflow. For the most up-to-date availability information, see the supported migration target region list.
Today, AWS announces the general availability of a new Local Zone in Hanoi, Vietnam, bringing AWS infrastructure closer to end users. This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon Simple Storage Service (Amazon S3) and Amazon Elastic Block Store (Amazon EBS) Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally. AWS Local Zones are AWS infrastructure deployments that extend core services, such as compute, storage, networking, and other select services, closer to metropolitan areas worldwide. AWS Local Zones help you achieve single-digit millisecond latency for end-user workloads, meet data residency requirements, support AI/ML inference workloads, and accelerate migration and modernization of legacy applications to the cloud, all while maintaining consistent AWS APIs, tools, and services as AWS Regions. AWS Local Zones are available in more than 30 metropolitan areas worldwide. The Hanoi Local Zone supports Amazon Elastic Compute Cloud (Amazon EC2) with C7i, M7i, and R7i instances, Amazon S3 with the One Zone-Infrequent Access storage class, Amazon EBS with Local Snapshots and volume types gp3, gp2, io1, sc1, and st1, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Virtual Private Cloud (Amazon VPC), AWS Direct Connect, and Application Load Balancer. To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API. For pricing information, visit the AWS Local Zones pricing page. To learn more, visit the AWS Local Zones overview page.
This post was co-written with Bharadwaj Tanikella (AI/ML Product Engineering Leader) and Mohammad Jama (Product Marketing Manager) from Datadog. In December 2025, we showed how AWS DevOps Agent and Datadog MCP Server could work together to autonomously correlate monitoring data with the infrastructure deployed and configured on AWS to resolve incidents in minutes instead of […]
Amazon MSK Provisioned clusters with Express brokers now support Intelligent Rebalancing on all existing clusters, at no additional cost. Previously available only on newly created clusters, Intelligent Rebalancing is now available on all MSK Provisioned clusters running Express brokers, making it effortless for customers to benefit from automatic partition balancing when scaling their Express-based clusters up or down. Intelligent Rebalancing maximizes the capacity utilization of MSK Express-based clusters by optimally rebalancing Kafka resources for better performance, eliminating the need for customers to manage partitions themselves or via third-party tools. Intelligent Rebalancing performs these operations up to 180 times faster compared to Standard brokers. Clusters are continuously monitored for resource imbalance or overload based on intelligent Amazon MSK defaults to maximize cluster performance. When required, brokers are efficiently scaled without affecting cluster availability for clients to produce and consume data. Intelligent Rebalancing is now available on all MSK Provisioned clusters with Express brokers in all AWS Regions where Express brokers are available. To learn more, see the Amazon MSK Developer Guide.
Announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, delivering high performance GPU acceleration for AI inference, graphics, and data analytics workloads.
Amazon Elastic Container Service (Amazon ECS) service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. You can choose proactive scaling by using predictive scaling (automatic) and scheduled scaling […]
Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x). Faster service auto scaling also enables you to reduce baseline capacity and lower compute costs while maintaining service reliability and performance as workload demand fluctuates. Amazon ECS service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. With today's launch, target tracking policies for CPU and memory utilization now support 20-second metric resolution, in addition to the default 60-second resolution, for faster scaling signal detection. To get started, use the AWS Console, CLI, CloudFormation, or AWS SDKs to configure 20-second resolution for CPU or memory utilization metrics when creating or updating your ECS service, then configure a target tracking policy selecting the corresponding high-resolution predefined metric. This feature is available in all AWS commercial and AWS GovCloud (US) Regions, across all ECS compute options: AWS Fargate, Amazon ECS Managed Instances, and Amazon EC2. High-resolution metrics are subject to standard CloudWatch charges; for a pricing example, see Amazon CloudWatch pricing. To learn more, see our documentation and the launch blog post.
Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances deliver up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6. You can use G7 instances for AI inference workloads such as language translation, video and image analysis, speech recognition, and recommender systems. Additionally, G7 instances also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics, and game streaming, as well as data analytics workloads such as large-scale data processing pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU, custom Intel Xeon 6 processors, and up to 700 Gbps of Elastic Fabric Adapter (EFA) networking bandwidth. You can start using Amazon EC2 G7 instances today in two AWS Regions: US East (Ohio) and US West (Oregon). You can purchase G7 instances as On-Demand Instances, as part of Savings Plans, or Spot Instances. To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit this blog post and the G7 instance page.
Starting today, Nested virtualization is now available on additional Intel platforms and additional Regions. Nested virtualization is now available on C7i,R7i, M7i, C8id,R8id, M8id, C7i-flex, M7i-flex, I7i, C8i-flex,R8i-flex, M8i-flex,and X8i, in addition to already available support on C8i, M8i and R8i instances. This capability is also now available in US GovCloud (US-East) and US GovCloud (US-West), in addition to existing support in all commercial regions. With nested virtualization capabilities, customers can create nested environments by running KVM or Hyper-V on virtual EC2 instances. Customers can leverage this capability for use cases such as running emulators for mobile applications, simulating in-vehicle hardware for automobiles, and running Windows Subsystem for Linux on Windows workstations. To learn more see documentation .
Amazon Connect Customer now supports the ability to interrupt an agent with a contact, overriding their usual routing configuration in case of urgent or time-sensitive work. For example, an agent may be waiting for a time-sensitive callback on their personal extension, while taking customer service calls in the meantime. When that urgent call comes in, it can now ring the agent even if the agent is currently already on another call, so the agent can decide whether to put the first caller on hold to pick up the callback as well. You can also use this feature to directly assign certain contacts to a specific agent even though that agent has set themselves to a custom status where they normally could not be offered queued contacts. For example, you may want to ensure that a specific agent cannot take customer service calls while in “Back Office Work” but still allow calls to their personal extension to ring through, improving efficiency for urgent contacts. This feature is available in all AWS regions where Amazon Connect Customer is offered. To learn more about this feature, see the Amazon Connect Customer Administrator Guide. To learn more about Amazon Connect Customer, the AWS cloud-based contact center, please visit the Amazon Connect Customer website.
AWS Compute Optimizer now includes improved visibility into IOPS and throughput spikes when deliverings Amazon EBS volume rightsizing recommendations. Compute Optimizer analyzes two additional Amazon CloudWatch metrics, VolumeIOPSExceededCheck and VolumeThroughputExceededCheck, which report whether your workload consistently attempted to drive IOPS or throughput beyond your volume's provisioned performance in any given minute. By factoring in these signals, Compute Optimizer helps you make rightsizing decisions to balance cost with performance for workloads that experience bursts of high IOPS or throughput. This enhancement is available in all AWS Regions where AWS Compute Optimizer is available, except the AWS GovCloud (US) Regions, and the China Regions. The underlying CloudWatch metrics are available at no additional charge for all EBS volumes attached to Nitro-based EC2 instances, excluding standard and Multi-Attach enabled volumes. To get started, go to AWS Compute Optimizer in the AWS Management Console. To learn more, visit the AWS Compute Optimizer User Guide.
Today, AWS announced the availability of all-MiniLM-L12-v2 in Amazon SageMaker JumpStart, expanding the portfolio of models available to AWS customers. This model from Sentence Transformers maps sentences and paragraphs to a 384-dimensional dense vector space, enabling customers to build high-quality semantic search, text clustering, and sentence similarity applications on AWS infrastructure. all-MiniLM-L12-v2 excels at encoding sentences and short paragraphs into dense vector representations that capture semantic meaning, making it ideal for information retrieval, semantic search systems, document clustering, duplicate detection, and paraphrase identification. Its compact architecture delivers fast inference while maintaining strong embedding quality, well suited for production workloads that require efficient text representations at scale. With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Today, AWS announced the availability of Ministral-3-14B-Instruct-2512 in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Mistral AI delivers frontier-class multimodal capabilities in a compact 14B-parameter architecture optimized for edge deployment, enabling customers to build advanced AI assistants, agentic systems, and vision-enabled applications on AWS infrastructure. Ministral-3-14B-Instruct excels at analyzing images and providing insights based on visual content in addition to text, agentic capabilities with native function calling and JSON output, and multilingual understanding across dozens of languages including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic. With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Amazon Web Services (AWS) is excited to release the Spring 2026 System and Organization Controls (SOC) 1 and 2 reports in machine-readable OSCAL format alongside the PDF version of the reports. The reports cover 188 services over the 12-month period from April 1, 2025 to March 31, 2026, giving customers a full year of assurance. […]
Amazon SageMaker AI's new observability capability allows customers to operate production generative AI inference workloads with confidence by providing comprehensive visibility into token performance, GPU health, inference component placement, and autoscaling behavior. It takes away the manual work of searching CloudWatch for per-endpoint metrics, correlating latency spikes with GPU saturation or KV cache exhaustion and diagnosing why scaling operations are slow. This capability tracks inference performance metrics in real-time, including Time to First Token, inter-token latency, queue depth, and tokens per second, and surfaces them alongside infrastructure health so customers can identify and resolve issues in minutes rather than hours. SageMaker AI detailed observability transforms how customers monitor and optimize their inference fleet. The new pre-built SageMaker AI Insights dashboard in Amazon CloudWatch gives customers token latency, GPU utilization, inference component copy counts, scaling events, and cold start breakdowns in a single view with OpenTelemetry native metrics published automatically, no instrumentation required. This allows teams to quickly diagnose TTFT degradation, verify availability zone compliance, and tune autoscaling policies. Customers who have standardized on observability tools like Grafana can connect directly using the regional PromQL endpoint and import a pre-configured dashboard template. This capability helps customers self-serve operational issues and maximize the performance of their AI investments. SageMaker AI Inference observability is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Canada (Central), South America (São Paulo), Europe (Ireland), Europe (Frankfurt), Europe (London), Europe (Stockholm), Europe (Zurich), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and Asia Pacific (Ja
Customers that use Amazon Simple Notification Service (Amazon SNS) in the Asia Pacific (Seoul) Region can now send text messages (SMS) to subscribers in more than 200 countries and territories. Amazon SNS is a fully managed pub/sub messaging service that enables message delivery to multiple endpoints including AWS Lambda, Amazon SQS, Amazon Data Firehose, mobile devices, and email. With this launch, customers using SNS in the Asia Pacific (Seoul) Region can subscribe phone numbers to SNS topics and broadcast SMS messages via AWS End User Messaging. To learn more about sending SMS messages with SNS, visit Mobile text messaging with Amazon SNS. For the list of supported countries and regions, visit Supported countries and regions.
Amazon GameLift Servers now supports two significant container fleet improvements that enhance flexibility and inter-container communication for game server deployments. These new capabilities address common challenges faced by game developers using containerized architectures, providing greater control over container permissions and enabling seamless discovery of co-located containers on the same instance. You can now customize Linux capabilities for containers in your container group definitions, giving you finer control beyond Docker's default capability set. This is particularly valuable for game servers requiring specialized capabilities such as NET_RAW for custom networking protocols or SYS_PTRACE for attaching debuggers and profiling tools. Additionally, game servers can now call the new ListContainersNetworkInfo() server SDK action to retrieve comprehensive network information, including container name, ID, local IP address, and container group type for all containers running on the same instance. This enables automatic service discovery and simplified communication between game servers and auxiliary services like metrics collectors, logging agents, or caching systems. These improvements are available through the Amazon GameLift Servers console, AWS CLI, AWS SDK, and AWS CloudFormation. The ListContainersNetworkInfo() action is supported in server SDK 5.x for Go, C++, and C#, as well as in plugins for Unreal Engine and Unity. Both features are available in all AWS regions where Amazon GameLift Servers is supported, except China. To learn more, visit the Amazon GameLift Servers documentation.
Amazon Relational Database Service (Amazon RDS) for SQL Server now supports higher volume-level limits for General Purpose (gp3) storage. With this update, each gp3 volume can scale up to 64 TiB in size (4X the previous 16 TiB limit), up to 80,000 IOPS (5X the previous 16,000 IOPS limit), and up to 2,000 MiB/s throughput (2X the previous 1,000 MiB/s limit). With these improvements, customers can now run larger Microsoft SQL Server databases on Amazon RDS. Workloads with demanding I/O requirements such as high-throughput OLTP systems and large-scale analytical workloads can take advantage of higher IOPS and throughput on a single volume with simplified storage management, and get better performance for mission-critical SQL Server workloads. Additionally, you can configure additional storage volumes to add up to three gp3 or io2 volumes per DB instance, increasing total capacity up to 256 TiB per instance. There is no change to pricing - customers pay for storage and any additional IOPS and throughput they provision beyond the baseline default. For more information, refer to the Amazon RDS for SQL Server User Guide. See Amazon RDS for SQL Server Pricing for pricing details and regional availability.
Gemini AI-powered interface preview for cluster and Compute Engine optimization; Knowledge Catalog adds data lineage control for BigQuery and Apache Airflow.
Google SecOps Announcement Scheduled Maintenance CloudSQL will undergo a scheduled minor upgrade this Sunday, June 21, 2026. Google SecOps SOAR Announcement Release 6.3.90 is being rolled out to the first phase of regions as listed here. This release contains internal and customer bug fixes. Announcement Scheduled Maintenance CloudSQL will undergo a scheduled minor upgrade.
API Gateway Change Update to the API Gateway runtime architecture The API Gateway runtime architecture is being updated to improve its integration with Google Cloud Platform and its services. This update does not affect existing API Gateway features. However, be aware of the following differences: Status code changes for gRPC API Gateways Error New status code Previous status code Quota exceeded ResourceExhausted Unavailable Invalid API key InvalidArgument InternalError For 4xx client-side quota failures, API Gateway will now reject requests (fail closed). This applies to both gRPC and OpenAPI API Gateways. If you experience any other differences in behavior due to this update, contact Google Cloud Customer Care. Note: Rollouts of this release to production instances might take up to 4 weeks to complete across all Google Cloud zones. Your instances might not be updated until the rollout is complete. Apigee X Announcement On June 18th, 2026, we began maintenance updates of Apigee instances configured for maintenance windows. If you set a preferred window for maintenance for your instance, and your instance version is below 1-17-0-apigee-9, your instance will be updated to 1-17-0-apigee-9 within the next seven to 21 days. A notification containing the expected date of upgrade will be sent within the next two business days. Note: Instances that meet either of the following two criteria will not be updated: Your instance has a DNS misconfiguration, as described in Known Issue 445936920. Your instance uses an Apigee Java Library that has been removed, as described in Apigee release notes dated October 16, 2025. For more information on participating in scheduled maintenance windows, see Maintenance overview and Manage Apigee instance maintenance windows. Backup and DR Feature Backup vault support for Cloud SQL instances encrypted with customer-managed encryption keys (CMEK) is generally available (GA), providing immutable and indelible storage with enforced retention. For
