AWS HealthOmics introduces VPC-connected workflows

AWS HealthOmics announces VPC-connected workflows, giving customers the ability to run bioinformatics pipelines that access AWS resources across regions and public internet resources through a customer’s Virtual Private Cloud (VPC). With this launch, life sciences customers no longer need to migrate their data and dependencies to the same AWS Region as their workflow before running analyses. AWS HealthOmics is a HIPAA-eligible service that helps accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
This launch enables life sciences customers to develop and test bioinformatics workflows more quickly. Customers can design workflows that access publicly-hosted data sets as well as AWS resources in different regions without making changes to the workflow code or migrating data between regions. Customers can use new Configuration APIs to specify a VPC configured to access public internet resources to which HealthOmics can send and receive network traffic, making it easy to use different network configurations for different use cases. With Configuration APIs, you can add and remove public internet dependencies anytime. Networking settings are configured at the per-run level, allowing you to opt-in only the workflows that you want to be VPC connected. 
VPC-connected workflows are now available in all regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), Asia Pacific (Singapore), and Asia Pacific (Seoul). To learn more about connecting workflows to your VPC, see the HealthOmics documentation.
Quelle: aws.amazon.com

AWS Security Hub is now available in AWS GovCloud (US) Regions

AWS Security Hub is now available in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Security Hub is a unified cloud security solution that prioritizes critical security issues and helps you respond at scale, reduce security risks, and improve team productivity. Security Hub detects critical risks by correlating and enriching security signals from Amazon GuardDuty, Amazon Inspector, and AWS Security Hub CSPM, enabling you to quickly surface and prioritize active risks in your cloud environment. The service delivers near real-time risk analytics and advanced trends, transforming correlated security signals into actionable insights through enhanced visualizations and contextual enrichment. You can enable Security Hub for individual accounts or across your entire organization with centralized deployment and management. Capabilities include exposure findings, security-focused resource inventory, attack path visualization, and automated response workflows. The service automatically visualizes potential attack paths by showing how adversaries could chain together threats, vulnerabilities, and misconfigurations to compromise critical resources. Streamlined pricing consolidates charges across multiple AWS security services for improved cost predictability. To get started, visit the AWS Security Hub console or the AWS Security Hub product page. For the full list of AWS Regions where Security Hub is available, see the AWS Regional Services List.
Quelle: aws.amazon.com

Amazon CloudWatch now supports multi-account and region log centralization based on data source

Amazon CloudWatch centralization now supports centralizing logs based on data source name and type. CloudWatch allows customers to copy log data from multiple AWS accounts and regions into a single destination account using centralization rules. With today’s launch, customers can now define rules that target data sources by name and type, such as VPC Flow Logs, EKS Audit Logs, and CloudTrail Logs, in addition to the existing log group name-based selection.
Data source name and type are discovered automatically by CloudWatch for AWS service logs and are based on log group tags for application logs.  Now, customers can specifically target which logs they want to centralize using these parameters. For example, a central security team can create a rule that centralizes all logs from CloudTrail and VPC data sources across their entire organization without needing to know or maintain a list of individual log group names.
To get started, create or modify a centralization rule in the Amazon CloudWatch console or through the AWS CLI and AWS SDKs, and specify your data source selection criteria in the centralization rule configuration.
Data source selection criteria is available in all AWS commercial regions where CloudWatch log centralization is available. Standard CloudWatch Logs pricing applies for log ingestion, storage, and data transfer. For more information, see the CloudWatch Logs Centralization documentation.
Quelle: aws.amazon.com

AI for nuclear energy: Powering an intelligent, resilient future

The world is racing to meet a historic surge in power demand with an infrastructure pipeline built for the analog age. Driven by the exponential expansion of digital technologies and the reindustrialization of supply chains, the mandate for always-on, carbon-free power is urgent and absolute. Nuclear energy is the essential backbone for this future, but the industry remains trapped in a delivery bottleneck. Before a shovel even hits the dirt, critical projects are slowed by highly customized engineering, fragmented data, and mountains of manual regulatory review.

Drive innovation to power a secure and sustainable futureThat is where AI comes in. To break the infrastructure bottleneck and shift the industry from ambition to delivery, Microsoft is announcing an AI for nuclear collaboration with NVIDIA, to provide end-to-end tools that streamline permitting, accelerate design, and optimize operations across the industry.

This set of technologies brings disciplined engineering to the entire lifecycle of a nuclear plant—spanning site permitting, design, construction, and continuous operations. By enabling these capabilities within a connected, AI-powered foundation, we are empowering energy developers to make highly complex work repeatable, traceable, secure, and predictable—slashing development timelines and eliminating rework without sacrificing safety.

PausedThe digital foundation for nuclear at scaleThe only thing that may be more complex than building a nuclear plant is designing and permitting one. Permitting alone can take years, cost hundreds of millions of dollars, and involve an immense amount of data processing and reporting. It’s not a lack of need, knowledge, or even willingness that’s holding development back, but rather the inability to progress efficiently and consistently through rigorous permitting and development processes.

Engineers can spend thousands of hours drafting, cross-referencing, formatting, searching, reviewing, and reworking materials. They have to identify and fix inconsistencies across tens of thousands of pages. It is little wonder that plants have been notorious for construction delays and cost overruns.

To break this infrastructure bottleneck, we need to move away from highly customized engineering towards repeatable, reference-based delivery—while maintaining regulatory standards and engineering accountability.

With AI, we can identify tiny documentation inconsistencies and resolve them quickly. By unifying data and simulation across the lifecycle, we ensure complex work remains:

Traceable: Every engineering decision is digitally linked to the evidence and regulations that back it up.Audit-Ready: The system keeps a perfect “paper trail,” ensuring that regulators can verify safety instantly.Secure: High-level intelligence is applied within a governed, protected environment.Predictable: High-fidelity simulations map time and cost, catching delays before they happen in the real world.This isn’t just about speed; it’s about trust. Engineers and regulators are freed to focus on what matters most: building a safe, secure, high-capacity, carbon-free power source that’s on-time and on-budget.

Here is how AI and Digital Twins can carry a project from the initial phases to efficient operations:

Design and engineering: Digital Twins and high-fidelity simulations enable faster iteration. Engineers can reuse proven patterns and instantly see how a tiny design change impacts the entire model, creating a validated plan before breaking ground.Licensing and permitting: Generative AI handles the heavy lifting of document drafting and gap analysis. It unifies all project information, ensuring comprehensive applications aligned with historical permits. This allows expert regulators to focus their time on safety judgments rather than reconciling thousands of pages of text.Construction and delivery: While traditional 3D models only map physical space, 4D (time scheduling) and 5D (cost tracking) simulations can virtually construct the plant before shovels hit the dirt. AI and Digital Twins allow developers to track physical progress against the digital plan in real-time, catching potential delays and preventing the schedule collisions that lead to expensive rework.Operations and maintenance: AI-powered sensors and operational digital twins detect anomalies early, ensuring higher uptime and predictive maintenance that keeps the grid stable with human operators firmly in control.By unifying data, traceability, and simulation across phases, AI accelerates design validation with high-fidelity 3D models and Digital Twins, improves licensing consistency through AI-assisted document workflows, and connects design assumptions to operational performance—giving operators, regulators, and stakeholders clearer, continuous visibility.

Accelerating delivery: How Aalo Atomics, Idaho National Labs, and Southern Nuclear are deploying AI for nuclearThe proof is in the progress. Our collaboration is already changing the pace of nuclear delivery.

Aalo AtomicsAalo Atomics has reduced the time-intensive permitting process by 92% using the Microsoft Generative AI for Permitting solution, saving an estimated $80 million a year. For Aalo, the value of the Microsoft and NVIDIA collaboration isn’t just speed—it’s confidence.

“Two things matter most: enterprise-scale complexity and mission-critical reliability. We’re deploying something complex at a scale only a company like Microsoft really understands. There’s no room for anything less than proven reliability.”

—Yasir Arafat, Chief Technology Officer, Aalo Atomics

PausedSouthern NuclearSouthern Nuclear has developed and deployed agents using Microsoft Copilot across its fleet, including engineering and licensing, to improve consistency, reuse knowledge faster, and support better decision-making in key workstreams.

Idaho National LaboratoryWhen it comes to the public sector and specifically United States Federal, Idaho National Laboratory (INL) has become an early adopter of AI for nuclear technology. By using the AI capabilities to automate the assembly of complex engineering and safety analysis reports, INL is streamlining the review process and creating standard methodologies for regulators to adopt these tools safely, further speeding deployment.

Expanding the ecosystem: How Everstar and Atomic Canyon are operationalizing AI for nuclear on Microsoft AzureMicrosoft is actively expanding this secure ecosystem. Everstar—an NVIDIA Inception startup—brings domain-specific AI for nuclear to Azure to modernize how the industry manages project workflows and governed data pipelines.

“The nuclear industry has been bottlenecked by documentation burden and regulatory complexity for decades. This partnership means our customers get the secure, scalable cloud deployments they demand. It’s a significant step toward making nuclear power fast, safe, and unstoppable.”

—Kevin Kong, Chief Executive Officer, Everstar

We are also excited to highlight Atomic Canyon, whose Neutron platform is now available in the Microsoft Marketplace, allowing nuclear developers to deploy these capabilities with consistency and control through trusted procurement pathways.

Progress at the pace this moment requiresAI is enabling the energy industry to deliver more power, faster, and safely. This Microsoft and NVIDIA collaboration provides the path to do exactly that for advanced developers, owners, and operators. By turning fragmented, high-variance workflows into governed, auditable systems, we can compress timelines without compromising rigor. By unifying data, simulation, and evidence across design, permitting, construction, and operations, we are accelerating the deployment of firm, carbon-free power while strengthening regulatory confidence and operational resilience.

The AI for nuclear operations collaboration brings together NVIDIA Omniverse, NVIDIA Earth 2, NVIDIA CUDA-X, NVIDIA AI Enterprise, PhysicsNeMo, Isaac Sim, and Metropolis with Microsoft Generative AI for Permitting Solution Accelerator and Microsoft Planetary Computer to create a comprehensive, AI-powered digital ecosystem for nuclear energy on Azure.

Microsoft, NVIDIA, and Aalo Atomics will be presenting this AI-lead industry perspective at CERAWeek 2026 in a session entitled “A Digital Age for Nuclear: Aalo Atomics, NVIDIA, and Microsoft.”

Discover moreReady to move from ambition to delivery? See how the Microsoft and NVIDIA nuclear for AI collaboration can drive change within your organization.

Contact us to learn more.
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Quelle: Azure

Microsoft named a Leader in 2026 Gartner® Magic Quadrant™ for Integration Platform as a Service

Enterprise digital transformation is entering a new phase. The challenge is no longer just connecting systems. It is about making those systems intelligent, able to reason, respond, and act in real time across the business.

As AI moves from experimentation into production, a clear pattern is emerging. Models and agents do not create value on their own. Value is created when AI can reliably access enterprise data, invoke APIs, trigger workflows, and operate within the guardrails of security, compliance, and governance. This makes integration essential to realizing AI value.

We are proud to share that Microsoft has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Integration Platform as a Service, marking the eighth consecutive year of recognition. We believe this reflects both the strength of Azure Integration Services today and our conviction that integration must evolve to meet the demands of the AI era.

Read the full 2026 Gartner® Magic Quadrant™ for Integration Platform as a Service report

From integration to intelligent operations

For years, integration platforms have helped organizations connect applications and synchronize data. AI is changing what organizations expect from these platforms.

AI systems do not operate in isolation. They depend on APIs to take action, events to respond in real time, workflows to orchestrate decisions, and governance to ensure trust. Without strong integration, AI remains isolated.

This shift fundamentally redefines the role of integration.

Azure Integration Services provides a unified platform to connect applications, data, APIs, and events while operationalizing AI across the enterprise. This allows organizations to move beyond point-to-point connectivity and build systems that coordinate actions in real time.

Unify systems with the AI-powered Azure Integration Services

The rise of agentic workflows

As integration evolves, workflows are evolving with it.

Static, predefined automations are giving way to adaptive processes that combine APIs, real-time data, and AI-powered decisioning. This is driving the rise of agentic workflows, where AI agents and deterministic logic operate together within orchestrated systems.

With Azure Logic Apps, organizations can design workflows that incorporate AI agents alongside business rules. These workflows are context-aware, responsive, and continuously improving.

They can invoke models, integrate human approvals, react to real-time signals, and execute across distributed systems. The result is a shift from traditional automation to intelligent operations that adapt as conditions change.

AI at scale demands governance by design

As AI systems gain the ability to act, governance becomes non-negotiable.

AI can access sensitive data, call downstream systems, and trigger business actions at speed. Without strong controls, this introduces real risk across security, compliance, cost, and trust.

Azure Integration Services addresses this by embedding governance into how AI interacts with the enterprise. With AI Gateway capabilities in Azure API Management, organizations can define and enforce how AI systems access APIs, models, and data. This includes applying policies, managing usage, enforcing access controls, and ensuring AI-powered interactions comply with regulatory and organizational requirements.

This approach allows organizations to scale AI confidently while maintaining control.

From experimentation to real-world impact

Organizations are already using these capabilities to drive measurable outcomes.

In cybersecurity, Cyderes processes more than 10,000 security alerts each day. By combining AI-powered analysis with automated, integrated workflows, the team has reduced noise and transformed how investigations are handled. Investigation cycles are now five times faster, enabling analysts to focus on high-value signals while keeping pace with increasingly sophisticated, AI-powered cyberthreats.

In life sciences, Vertex Pharmaceuticals addressed the challenge of knowledge fragmented across dozens of systems, including ServiceNow, internal documentation, and training platforms. By orchestrating AI within integrated workflows, they built a solution that can search, summarize, and route information seamlessly across tools like Microsoft Teams and Outlook. Tasks that once took hours are now completed in minutes, improving productivity while maintaining compliance and supporting global teams.

Organizations are also applying these patterns to govern AI at scale. Access Group, for example, uses Azure API Management to govern how AI systems interact with enterprise APIs and services. By introducing centralized policies, access controls, and observability, they can securely expose capabilities to AI applications while maintaining control over usage, cost, and compliance. This approach ensures that AI-powered interactions remain consistent, auditable, and aligned with business requirements.

These examples reflect a broader shift. Integration is no longer just about connecting systems. It is enabling new ways of working, where AI is embedded directly into business processes and governed as part of the enterprise platform.

Looking ahead

Integration will play a central role as organizations scale their use of AI. As organizations adopt AI agents, event-driven architectures, and real-time decisioning, the ability to orchestrate and govern these interactions becomes increasingly important.

We are honored to be recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Integration Platform as a Service and look forward to helping customers build what comes next.

Ready to explore further?

Download your complimentary copy of the 2026 Gartner® Magic Quadrant™ for Integration Platform as a Service to learn why Microsoft was named a Leader.

Explore how Azure Integration Services helps organizations operationalize AI across applications, data, and workflows.

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Quelle: Azure