Browse Azure News (138)

Kristen Womack reviews October’s Azure Developer CLI releases, covering deployment previews, Container Apps express support, environment value removal, and extension-aware pipelines. The recap also explains stable extension APIs, breaking Go API changes, reliability fixes, and new templates for deploying semantic search and streaming AI applications to Azure.

spurthikommajos explains how Microsoft Fabric Eventstream’s preview support for workspace-level private connectivity lets custom applications publish events without public internet access. The article covers Azure private endpoints, workspace-scoped network restrictions, setup prerequisites, and supported analytics destinations, including important capacity requirements and ingestion limitations.

spurthikommajos introduces preview support for variable libraries in Microsoft Fabric Eventstream. Connection and item references let teams reuse eventstream definitions across development, staging, and production, while each workspace supplies its own resource values. The article explains configuration steps, deployment prerequisites, and how references remain intact through Git integration and deployment pipelines.

Microsoft Threat Intelligence examines how North Korean threat actors use AI to accelerate reconnaissance, identity deception, malware development, and operational scaling. The report distinguishes observed activity from suspected AI use, explains why operations remain human-directed, and provides Microsoft Defender detections and practical guidance for protecting identities, endpoints, and software repositories.

Salim Chawro introduces Hardware Audit Logging for Azure Cloud HSM, which records supported management operations directly from the hardware. Signed audit data and certificates enable independent verification, while Azure Monitor Log Analytics delivery supports investigations and monitoring. The feature is rolling out across public regions and is automatically enabled on new resources where available.

Satya Nadella outlines GitHub updates announced at Universe, including Copilot Fusion performance and cost claims, a unified Copilot harness in Foundry, and Copilot Projects for coordinating multiple agents across repositories. The post also notes plans to expand GitHub capacity and strengthen its architecture.

Lynn Dai introduces Trusted Publishing for Visual Studio Marketplace, enabling extension releases from GitHub Actions and Azure Pipelines without long-lived personal access tokens. The article explains federated identity setup, GitHub policy activation, Azure Pipelines service connections, and how to migrate existing publishing workflows while verifying access before removing stored secrets.

Jeff Hollan, Viswajeeet Balaji, and Anthony Chu introduce the Microsoft Copilot harness public preview in Foundry Agent Service and generally available resilience for hosted agents. They demonstrate reusable skills and declarative deployment, explain recovery responsibilities and preview limitations, and outline consumption-based runtime billing beginning December 1, 2026.

Jay Parikh outlines GitHub’s Universe 2026 announcements, including multi-repository Copilot projects, model orchestration, deeper code review, and enterprise access billed through AI credits. He also describes GitHub’s infrastructure expansion, redesigned Git architecture, and ongoing migration to a multi-region Azure deployment.

Sandeep Sen summarizes October's Azure SDK releases, including authentication changes in Azure Core for .NET, managed identity improvements for Python and Java, and the first stable Cosmos DB libraries for Rust. The update also lists new stable management libraries and initial previews for AI and provisioning libraries.

Rena Liu introduces Azure AI Speech LLM 09-2026, adding 25 languages and faster multilingual transcription. The announcement covers recognition improvements, locale selection, phrase lists, and REST API configuration, with automatic service-side deployment requiring no customer action.

Sverre Brandsberg Dahl describes how SLB agile reservoir modeling connects with Azure Data Manager for Energy to run reservoir ensembles at cloud scale. The architecture combines parallel simulation, shared Azure Files storage, OSDU-aligned data services, and Microsoft Entra ID access controls to keep modeling workflows connected to governed corporate data.

Cynthia Zanoni and Pooja Yarabothu explain how Microsoft’s PostgreSQL extension brings database exploration, SQL editing, and troubleshooting into VS Code. They show how GitHub Copilot can help interpret schemas and query plans, while emphasizing result verification and careful review of performance recommendations before making changes.

Josef Sin explains what Azure DevOps users should check following the PHP2Shell disclosures. Azure DevOps Services and Server do not use PHP to handle customer requests, but pipeline workloads, agent hosts, containers, and deployed applications may require runtime patches. The guidance distinguishes Microsoft-managed updates from fixes customers must apply themselves.

Microsoft Security Research, Mahesh Mandava, Srinivasan Govindarajan, Sagar Patil and Suriyaraj Natarajan break down the LiteLLM 1.107.0 supply-chain compromise, showing how Python startup hooks enabled credential theft without importing LiteLLM, what data was targeted, and how to investigate and recover using Microsoft Defender detections and hunting queries.

Eleanor White explains how GitHub is moving its Git storage to Azure, focusing on the last ultra-busy repositories blocked by cross-region latency. The post breaks down per-reference write quorums and a “virtual replica” design that makes Azure Blob Storage part of the durability path so reads and writes can scale independently.

jovanpop-msft introduces preview support for calling Fabric User Data Functions from T-SQL in Fabric Data Warehouse. The integration extends warehouse queries with custom Python logic for data enrichment, external API calls, and secret retrieval, with an example that calculates and stores IPv6 network prefixes for downstream analytics.

Alistair Speirs outlines how to design “sovereign AI” architectures that keep sensitive workloads under the right operational, regulatory, and security controls, while still enabling AI capabilities from edge to cloud. The post connects Azure Local, confidential computing, and Microsoft Foundry/Foundry Local into a single, verifiable foundation.

Bruno Capuano shows how to use Bring Your Own Model (BYOK) with the GitHub Copilot SDK in a small .NET 10 console app, routing inference to Microsoft Foundry or Azure OpenAI while keeping the Copilot runtime. The walkthrough covers ProviderConfig, endpoint normalization, and Entra ID vs API-key authentication.

Nandini Muralidharan announces the general availability of the Browser Automation Tool in Microsoft Foundry, explaining how agents can use managed cloud browsers (via Playwright Workspaces) to automate multi-step web workflows across public sites and private apps behind an Azure virtual network, with governance and human-in-the-loop controls.

sbaynes interviews Mercedes-AMG PETRONAS F1 lead race strategist Joey McMillan on how the team uses Microsoft Azure—especially Azure Batch—to run large-scale race simulations quickly enough to influence live, race-day strategy decisions under changing weather and track conditions.

Michael Scovetta summarizes Microsoft and NVIDIA’s guidance on managing risk when deploying open-weight AI models, focusing on how risk depends on the model, use case, deployment architecture, and safeguards, and how to connect risk categories to evaluations and controls.

Nick Brady’s September 2026 roundup covers Microsoft Foundry updates across models, agents, observability, and tooling—highlighting GPT-6 family GA, Routines and Tool Search GA, Insights preview, Model Router preview controls, and Foundry Local 2.1 bring-your-own-model support with ONNX Runtime GenAI.

Mike Hulme explains how Azure is evolving its cloud-native platform to better support AI workloads and AI agents, focusing on coordinated compute/storage/networking, production-ready open-source foundations (notably Kubernetes), cost governance with FinOps, and “agentic” operations to improve reliability and efficiency at scale.

Lim Ai Leen reports on Bar-Ilan University’s pilot of an AI agent in Moodle that helps students search, summarize, and quiz themselves on course materials using natural language, built with Azure AI Services and Microsoft Copilot Studio and run in the university’s Azure environment.

Eduard van Valkenburg explains how Microsoft Agent Framework for Python models and uses vector stores for embeddings, vector search, and agent context. The post shows how to define typed records, index and query data, expose vector search as an agent tool, and keep conversation history searchable via context providers across multiple database integrations.

Sandeep Sen announces the stable Azure MCP Server 3.0 release and explains what’s new for bringing Azure operational tooling into MCP-compatible AI agent workflows, including Terraform and Advisor tooling, more predictable client integration, and security-focused defaults for resource creation.

Jakub Oleksy reports on GitHub’s September 2026 reliability work and incidents, including database pressure reductions, overload controls, added headroom for GitHub Actions, expanded failure containment for pull requests, and progress moving production workloads and traffic to Azure.

Joe Filcik and Colin Murphy explain how Azure Content Understanding uses grounding and field-level confidence to make document extraction results inspectable and actionable, then show how to calibrate confidence thresholds (per document type and per field) to route work in human-in-the-loop review workflows.

Microsoft Threat Intelligence and Microsoft Digital Crimes Unit detail how a Chinese-language phishing-as-a-service ecosystem (Storm-2990 and Storm-3119) monetizes compromised Microsoft 365 and Azure for Education tenants, then provide practical mitigation guidance and Microsoft Defender detections to help organizations reduce phishing-driven account takeover risk.

sbaynes outlines how Microsoft is scaling Azure’s AI infrastructure end-to-end, covering custom silicon (Maia and Cobalt), networking for large AI clusters, and datacenter power and cooling redesigns. The post also highlights hardware-rooted security work like Azure Integrated HSM and the open-source Tessera Integrated HSM initiative.

sbaynes breaks down the Azure Maia 300 system architecture and the design choices Microsoft made to optimize cloud-scale LLM inference, focusing on dataflow, memory locality, low-precision formats, and rack-to-superpod networking to improve tokens-per-watt and tokens-per-dollar.

mk_sunitha introduces Fabric App functions (preview), a TypeScript-based serverless capability for Fabric Apps that lets builders run secure server-side logic, use application authentication to access downstream resources, and manage secrets safely. The post also shows how to scaffold, debug, and deploy functions, including using GitHub Copilot in VS Code.

Mark Russinovich and Saurabh Dighe announce Project Tessera, an open-source evolution of Azure Integrated HSM, and explain how its integrated HSM model and protocols aim to improve key protection, interoperability, and post-quantum readiness—plus how the same firmware stack can extend into confidential computing environments.

Chu Lahlou explains how Azure Content Understanding prebuilt analyzers go beyond a schema by packaging domain knowledge with grounding and confidence signals, so document extraction results are safer to automate. The post also shares benchmark results for Advanced Contextualization in Content Understanding 2.0 Preview, covering quality, modeled cost, and latency.

sbaynes announces Microsoft’s plan to scale the Azure Maia accelerator system to multi-gigawatt deployments starting in 2027, outlining how Maia 200 is already running production inference and what architectural changes in Maia 300 target long-context, latency-sensitive, and agentic workloads.

Tamir Dresher explains where Durable Task (and Durable Task Scheduler) actually helps in production: coordinating long-running workflows that need durable waits, replay, and recovery. Using five concrete scenarios—including Azure tenant provisioning and an AI-assisted incident workflow—he shows what durability solves and what your app still must own.

Tsuyoshi Ushio introduces Dynamic Workflows for Azure Functions Hosted Skills, a preview feature that lets an LLM generate a typed DAG plan once and then run it durably via Durable Functions. The post explains the runtime model, safety checks, and when this approach reduces token usage and latency for event-driven apps.

Chu Lahlou compares Azure Document Intelligence (ADI) and Azure Content Understanding (ACU) for real-world document processing, focusing on when to use purpose-trained document models versus generative, schema-driven extraction. The guide covers OCR/layout overlap, custom extraction trade-offs, RAG-ready output, multimodal analysis, and a practical evaluation checklist.

Rani Borkar explains how Microsoft applies agentic AI across Azure’s hardware and infrastructure lifecycle—from demand planning and supply chain through datacenter fleet operations—to shorten decision cycles, improve reliability, and build a closed-loop learning system that keeps human judgment in control.