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City of Raleigh from insights to action: A traffic digital twin, live in six weeks

DanNarloch — Tue, 04 Aug 2026 19:06:05 GMT

Raleigh is one of the fastest-growing cities in the United States, and its residents feel that growth every morning, in traffic. Easing congestion has become a priority the whole city can measure.

Understanding how traffic actually moves has traditionally meant sending transportation engineers out to observe intersections and analyze them by hand, a slow and resource-intensive process for a city serving 500,000 residents and 19 million annual visitors. Raleigh saw a chance to approach it differently.

Working with Esri, NVIDIA, and Microsoft, the city built a real-time digital twin of its traffic, a virtual environment where teams can watch conditions unfold and understand what a change would do before making it. Built on Azure, it combines video from existing traffic cameras with AI analytics and geospatial intelligence to give engineers a live view of the busiest intersections in the city.

Raleigh moved from concept to pilot in six weeks, using infrastructure it already owned. "This has not required a single modification to our on-the-streets assets," says James Alberque, GIS and Emerging Technology Manager at the City of Raleigh. The cameras were already on the poles, and the cloud foundation was already in place.

With that foundation in place, Raleigh estimates that optimizing signal timing along one busy corridor could save commuters $9.7 million in time and travel costs. That figure is a projection, not audited savings. It rests on delays cut by seconds per signal, changes most drivers would never notice, spread across thousands of daily trips.

The work is also changing what a small team can cover. Two engineers now have a live view of the network that once took field visits to assemble, creating room to improve safety, tune operations, and look at services beyond traffic.

For governments looking to improve quality of life without new capital, Raleigh's experience carries a useful lesson. Transformation doesn't always mean starting over. Often it starts with reimagining the data and infrastructure already in the ground.

Read the full story to see how Raleigh built it in six weeks, and what two engineers can now cover.

What’s new in Microsoft 365 Copilot for U.S. Government Customers

elisabeth_jones — Thu, 06 Aug 2026 22:46:35 GMT

Many government organizations are evaluating ways to expand the use of AI while maintaining appropriate security, compliance, and administrative controls. Microsoft 365 Copilot helps employees work with information they are authorized to access across Microsoft 365. Administration and compliance capabilities help IT and security teams manage adoption across these experiences.

Since our April update, new capabilities across U.S. Government clouds have expanded what employees can do with Microsoft 365 Copilot and how IT, security, and compliance teams can apply data protection, governance, and adoption controls to their AI workloads.

Microsoft 365 Copilot: Help employees work with organizational knowledge

Microsoft 365 Copilot helps employees work with the information they are authorized to access across Microsoft 365 and connected enterprise sources.

Researcher (now available in GCC-High) supports complex, multi-step research across accessible work content and other configured sources, producing a structured, source-cited report. Analysts can use it to compare evidence, identify patterns, and prepare decision-ready briefs with less manual synthesis. Learn more about Researcher

Microsoft 365 Copilot connectors (now available in DoD) extend Copilot and Microsoft Search to external line-of-business content by indexing it in Microsoft Graph. This lets employees search for and reason over more of the enterprise knowledge they need without leaving the Microsoft 365 experience. Learn more about Microsoft 365 Copilot Connectors.

Shared controls: Govern AI and agents at scale

As adoption grows, IT, security, and compliance teams need controls that help them manage how employees use Microsoft 365 Copilot and how organizations govern agents and AI experiences.

Microsoft Purview Data Loss Prevention for prompts (now available across GCC, GCC-High, and DoD) gives security and compliance teams a real-time control to help safeguard prompts containing sensitive data. Organizations can configure DLP policies to prevent Microsoft 365 Copilot and agents built in Copilot Studio and published to Microsoft 365 Copilot from processing a prompt with a Sensitive Information Type (SIT), as well as from grounding responses on sensitive files or on web search, helping reduce data leakage and oversharing risks. Learn more about Microsoft Purview DLP for Microsoft 365 Copilot and Copilot Chat.

Agent admin controls (now available across GCC, GCC-High, and DoD) give IT teams centralized options to review, approve, assign, block, or remove agents and manage access by user or group. Learn more about agent administration in Microsoft 365.

Copilot Chat usage reports in Microsoft 365 admin center (now available in GCC, GCC-High, and DoD) help admins understand how employees are adopting and using Copilot Chat. Inform your licensing strategy by identifying power users of Copilot Chat. View total active users, Copilot Chat usage by Microsoft 365 app, and view user level activity data (anonymized by default).

Continuing updates for U.S. Government customers

Together, these releases expand research, knowledge access, reporting, and compliance capabilities available across U.S. Government cloud environments.

Microsoft will continue investing in Microsoft 365 Copilot to help U.S. Government organizations evaluate, adopt, and manage AI experiences in alignment with their requirements.

Reproducing an AI Action Plan Analysis with the Microsoft Discovery App

TimMeyers — Sat, 01 Aug 2026 14:00:00 GMT

Setting the scene

Just over a year ago, I published a three-part series on this blog about using generative AI to analyze the roughly 10,000 public responses (10,067 to be exact) to the OSTP/NITRD Request for Information on the AI Action Plan:

The goal of that series was not to produce a report. It was to give an analyst a way to collaborate with the corpus — to ask questions, surface patterns, and draw their own conclusions from 10,000+ voices that are impractical to read one by one and even harder to hold in your head all at once. Getting there took about a day of AI-assisted engineering: prompt engineering, a model bake-off, standing up cloud infrastructure in Azure, configuring knowledge-graph research packages (GraphRAG ), building a metadata transformation layer, and deploying an MCP server so a custom Copilot could talk to it. A number of technologies, a lot of plumbing, and — importantly — a lot of me doing the work end-to-end.

Fast forward a year. The Microsoft Discovery app is now in preview. I've been experimenting with it to learn how it works, and I wanted to see what would happen if I pointed it at the same corpus. Not as an analysis project — as a capability test. How far could I push it? How much could I hand off? What would it do on its own?

I loaded the corpus. I gave it one vague sentence of intent. I set it to Autopilot. I went to the movies. Three hours later I came back to a fully-graded 43-page research report.

This post is the "one year later" retro: what I still did, what the platform now does for me, and what that shift tells us about where AI-for-discovery is heading.

Case study, pre-released product, and AI disclaimers:

This document is for informational purposes only. Some information relates to pre-released product which may be substantially modified before it’s commercially released and generally available. These results reflect a specific run. Generative AI systems are probabilistic, so repeating the workflow with the same prompts and models may produce different outputs. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY AND WITH RESPECT TO THE INFORMATION PROVIDED HERE. 

What Microsoft Discovery is

Microsoft Discovery is Microsoft's extensible platform that brings together agentic orchestration, advanced reasoning, a graph-based knowledge foundation, and high-performance computing for scientific research and R&D workflows. It ships in two complementary experiences: Microsoft Discovery, the enterprise cloud service running in Azure — generally available with full governance, multi-user collaboration, and scalable compute; and the Microsoft Discovery app, a free download, local-first Windows client currently in preview that runs on your own machine with just a GitHub Copilot subscription. Both operate on the same building blocks:

Discovery Engine to orchestrate autonomous, multi-step, long-running sessions to achieve outcomes.
Bookshelf to provide a platform-native, graph-based, document-reasoning capability.
An agentic AI platform with everything you'd expect — tools, skills, agents — out of the box.

Projects you build in the app are portable up to the enterprise service when you need scale, private data, team collaboration, or compliance.

This post is about the Discovery app.

The experiment, re-run — in three steps

Here's what I actually did. It's short.

Step 1 — Load the data

I created a new Discovery app workspace called aip-for-fun. Ingesting documents is as simple as picking a source — files, a folder, a URL, or the open workspace — from the app's Ingest Documents dialog:

I loaded the AI Action Plan RFI itself into one bookshelf and the 10,067 public responses into another — keeping them separate seemed like a good idea; the RFI is the question and the responses are the answers. Total indexing time: about 15 minutes.

For contrast, in 2025 just cracking those PDFs open and preparing them for GraphRAG indexing was hours of engineering — an Azure Function to convert PDFs to Markdown, another to trigger on new files, all wired through Blob Storage. The Discovery app makes it as simple as pointing it at the folder and letting it go.

Step 2 — Initialize the project

I selected the out-of-the-box project-setup-orchestrator agent in Discovery Chat and gave it one line:

"I want a report analyzing the datasets in the bookshelves."

That's it. That's the whole prompt. I was intentionally vague — I wanted to see what Discovery could figure out on its own.

The orchestrator went to work: it inspected the workspace, found the bookshelves, asked me a clarifying question or two, and drafted a Purpose statement for me to confirm.

From that one sentence and a look at the bookshelves, it produced:

A Purpose statement:

"Systematically analyze the U.S. public's response to the OSTP/NITRD Request for Information on the AI Action Plan (EO 14179, Jan 2025), drawing on a corpus of ~10,067 submissions to characterize who responded, what they asked for, and what those signals mean for AI policy."

Six proposed Outcomes with grading rubrics. I reviewed them, liked them, and added four more where I wanted deeper cuts on specific dimensions. Discovery wrote the rubrics for those too — including a higher-bar rubric for the international competitiveness outcome using a 0–4 Likert scale across four weighted questions. Discovery did the drafting; I did the reviewing and validating.

#	Outcome	Type	Proposed by
1	Respondent Landscape Report	knowledge	Discovery
2	Policy Demand Map	knowledge	Discovery
3	Public Sentiment Analysis	knowledge	Discovery
4	Policy Recommendations Brief	artifact	Discovery
5	Consensus Themes Across Stakeholder Groups	knowledge	Discovery
6	Longitudinal Corpus Comparison	knowledge	Discovery
7	Creative Economy Impact Assessment	knowledge	Tim
8	Sector-by-Sector Policy Divergence	knowledge	Tim
9	Energy and Infrastructure Signal Report	knowledge	Tim
10	International Competitiveness Framing Analysis	knowledge	Tim

From that Purpose and those Outcomes, Discovery generated a hierarchical task graph — 27 tasks across 5 milestones, from source acquisition through respondent characterization, policy demand mapping, sentiment, specialized domains, and synthesis.

Step 3 — Autopilot Go

I switched the chat to Autopilot and typed:

"Work DX-1 to completion."

(A quick aside: Discovery numbers every task in the graph with a unique DX-N ID. DX-1 is always the top-level task — completing it means completing everything under it. You can see the numbering in the task tree screenshot below.)

After project setup, this was essentially a one-shot prompt. I left. I went to see a movie. Three hours later I came back to a task tree that looked like this — every node green:

The task tree above shows six milestones and 32 tasks, not the five and twenty-seven Discovery started with. That's because after Milestone 5 produced the first end-to-end report draft, Discovery graded that draft against the project's ten Outcomes, saw room to strengthen it, and — on its own — added a sixth milestone with five additional tasks to expand depth, regenerate charts, embed local images, and rebuild the PDF. The autopilot run didn't just execute the plan; it revised the plan when the artifact didn't clear the bar.

And behind DX-1: an 831-line, 43-page PDF report with a branded title page, a table of contents, nine narrative sections, three appendices, a bibliography, 18 embedded charts, and rubric grades against every one of the ten Outcomes.

For scale: across the whole project — including setup, the autopilot run, and later polish — there were 34 user prompts, 225 assistant turns, and 386 tool calls. But "34 user prompts" is misleading: I personally typed maybe four or five of them. The rest were Discovery's own agents prompting themselves and each other as they worked through the task graph autonomously. (Full disclosure: I didn't capture token counts on this run — instrumenting that end-to-end is something I'm still working on.)

A look at what came back

The metrics above tell you how the report was produced. What the report actually is is at least as interesting. Here it is.

The report opens with a table of contents, an executive summary, and a set of action-ready conclusions — before it ever gets to the deeper sections. This is the polish layer Discovery added in Milestone 6: not just content, but the kind of structural framing an analyst would expect from a professional research deliverable.

Behind those front-matter pages, the report goes deep. A few examples of what the autopilot run produced:

Respondent landscape. Section 1 characterizes who actually filed — separating the thousands of individual submissions (often a single page or less) from the several hundred organizational filings (typically 8–10 pages, sometimes 40+). This asymmetry matters: it means the same corpus contains both a broad public-sentiment signal and a much smaller set of dense, high-effort policy arguments — and the report treats them differently rather than averaging them together. (This was also one of the first things that jumped out at me when I analyzed the corpus by hand last year — Discovery landed on the same distinction on its own.)

Public sentiment, quantified. Section 2.2 doesn't just say "people are worried" — it breaks tone down by respondent group, showing where concern outweighs enthusiasm and vice versa. It's the kind of chart I'd have had to prompt-engineer and hand-plot last year.

The policy demand map. Section 3 is the ten-theme demand map — a ranked view of what respondents are actually asking the federal government to do, from federal preemption of state AI laws to R&D investment, workforce policy, energy and infrastructure, and international standards leadership.

Sector-by-sector depth. Section 4 breaks the corpus down by sector — creative economy, education, healthcare, financial services, and more — with each subsection surfacing that community's specific policy asks. Education is a representative example: a concise, bulleted list of what educators, districts, and higher-ed institutions are asking for.

The punchline. Section 9.1 closes the report with a decision-ready recommendation table of twelve grounded policy actions, each with its own evidence base and implementation pathway. Alongside it, the report also lays out a recommended implementation sequence across several time horizons — immediate administrative action (0–6 months), near-term regulatory action (6–18 months), and legislative action (18–36 months) — so a reader can see not just what to do, but in what order.

Not a chatbot answering questions one at a time. A brief a policy analyst can read on a plane.

I want to be clear about the framing of this experiment: I was testing what the Discovery app can do on its own. Were I actually doing this analysis for a customer, I'd have worked with Discovery a step at a time — reviewing, redirecting, going deeper in specific areas, catching things the autonomous run might have oversimplified. Autopilot is not the recommended mode for high-stakes work. It's a remarkable demonstration of how much the platform can do when you let it run.

Parting thoughts

Last year I was a data scientist and a software engineer. I was writing summarization prompts, hand-tuning model choices, standing up Azure Functions and Cosmos DB, debugging failed function invocations, wiring MCP servers. I was building the machine AND doing the analysis.

This year I stated my intent, reviewed what the platform proposed, added the outcomes it missed, set it going — and came back to graded artifacts.

Over the last twelve to eighteen months, the software engineering industry has been living through exactly this shift: from writing the code to directing agents that write the code. What's exciting about Microsoft Discovery is that it brings that same shift to the scientific, research, and analysis communities — anyone who follows the scientific method, working with a corpus of research and data, generating new insights and evidence, and trying to reason their way to a defensible conclusion.

The corpus for my experiment was public comments on AI policy. But the same shape of work applies whether you're diving into unstructured public feedback, designing a new quantum chip, or investigating anaerobic genes and bioreactor optimization for advanced biomanufacturing.

If you want to try it, download the Discovery app. After logging in with a GitHub Copilot account, all you really need to bring is a question you'd like to explore, a hypothesis you'd like to test, or an artifact you'd like to produce — or all three. Discovery is ready to help with the rest.

I've done a handful of other Discovery projects since this one. Every time, I push it a little further — follow a thread, ask a stranger question, see how far my curiosity will take me. Every time, it goes.

Where will your curiosity take you?

Empowering the Mission at the Edge with Azure Local

Kevin Mack — Wed, 22 Jul 2026 17:05:33 GMT

Azure Local extends Azure services to customer-controlled locations so mission workloads can run where they are needed—at the tactical edge, or at a forward site. This capability is especially critical in Denied, Disrupted, Intermittent, and Limited (DDIL) environments. It enables hybrid and fully disconnected operations while maintaining consistent security, policy, and lifecycle control.

This post provides a reference architecture that uses Azure Local for mission solutions at the tactical edge. This article is designed to provide a reference for those developing Mission Tactical Solutions, designed to run at the edge and how to enable a platform using Microsoft technologies.

Why is the Edge so important to Mission?

In modern defense operations, decision advantage depends on bringing compute and analytics closer to the point of collection often in environments where reach-back to centralized cloud services are intermittent, degraded, or denied. The ability to run cloud-architected solutions when connectivity to the cloud is limited is a critical requirement for these scenarios. When evaluating the broader defense landscape, several contributing factors shape this requirement and influence how solutions must operate today and evolve over time.

High Data Volume - Military forces and mission partners field more sensors designed to support the warfighter and drive decision advantage. As data volume grows, isolating signal from noise becomes even more important within strict security boundaries and constrained communications.
Increased Pressure to Deliver Value Faster - Edge capabilities are expected to deliver outcomes faster than ever before. Meeting this demand requires solutions that evolve over time while maintaining compliance and security both now and in the future.
Modern Tools and AI Model Access at the Edge - Edge deployments increasingly require AI workloads that isolate signal from noise and sustain decision advantage, even under denied or degraded connectivity.

To address these needs, Microsoft has developed adaptive cloud capabilities that extend Azure patterns to the tactical edge, and this is accomplished with the Adaptive Cloud. Adaptive Cloud and the portfolio of services provide an operating model across hybrid, edge, and IoT environments to enable an operations platform through a single consistent framework.

With the Adaptive Cloud portfolio, the goal is to provide solution teams with the option deploy and operate mission applications closer to the point of need while maintaining governance and lifecycle control.

Azure Local (including disconnected operations), and Azure Arc form a common operational layer for edge environments. Together they help standardize infrastructure, deploy modern applications (VMs and containers), and enforce governance—even when connectivity is limited—so mission systems can be operated, patched, and updated without relying on continuous reach-back.

What is Azure Local?

Azure Local is Microsoft’s on-premises, Azure-consistent platform that extends Azure infrastructure and services to customer-controlled locations so critical workloads can run where they are needed—on the edge—without sacrificing cloud-native agility or security governance. It supports modern VM and Kubernetes workloads locally while maintaining a common operational model with Azure for policy, security controls, and lifecycle management.

In tactical environments, Azure Local helps standardize and harden infrastructure across enclaves and sites, while enabling a platform that can evolve and change over time to meet mission needs.

The result is a more resilient foundation for mission applications in contested or bandwidth-constrained conditions—while reducing operational friction and accelerating fielding.

Recent Announcements:

Recent announcements describe updates to Azure Local for edge and sovereign scenarios, including:

Azure Local disconnected operations (ALDO) is generally available, extending Azure governance and policy to sites with no cloud connectivity.
Foundry Local adds support for large AI models in fully disconnected, sovereign environments.

Over the past year, several customer and partner announcements have expanded validated hardware and deployment options for Azure Local in mission environments.

Curtiss-Wright has announced Azure Local validation for PacStar Tactical Edge Servers: Azure Local validation for PacStar Tactical Edge Servers.
Microsoft has highlighted a partnership with Armada to deploy Azure Local on Armadas Galleon Modular Data Centers (MDC): Azure Local on Galleon Modular Data Centers.

Taken together, these announcements indicate that Azure Local is being validated and deployed for mission workloads at the tactical edge where connectivity, policy, and operational constraints drive the architecture.

Azure Local disconnected operations (ALDO):

Azure Local disconnected operations (ALDO) is designed for missions that cannot support connectivity back to the Azure Cloud. It enables platforms and workloads (VMs, Kubernetes, and other extended services) to run fully disconnected at remote or forward sites, while keeping local control of compute, storage, and networking—and preserving governance intent for policy and configuration when connectivity returns.

Mission Reference Architecture:

As many edge systems modernize, teams move toward container-based solutions and use orchestration platforms such as Kubernetes to run and manage those workloads. Containerization is only part of the broader solution: workloads must also be deployed to edge hardware and sustained over time, including updates, patching, and feature delivery for end users.

With Azure Kubernetes Service enabled by Arc (AKS on Arc) available on Azure Local, teams can deploy and operate containerized applications using Azure-consistent patterns while remaining resilient in disconnected or contested scenarios. Depending on how the Azure Local environment is configured can determine the length of time it can support disconnected durations. Azure Local also supports a cloud-like development experience through infrastructure as code and platform services that accelerate delivery.

Key Components of the Architecture:

Azure Local – Software-defined platform for Azure-consistent operations on approved edge hardware.
Azure Kubernetes Service enabled by Arc (AKS on Arc) – Managed Kubernetes for running containerized workloads with Azure-consistent deployment and operations.
Azure Container Registry (ACR) – Image repository for packaging and deploying container images to the edge.
Azure Arc – Extends Azure management, policy, and security to edge resources when connectivity is available—without making mission operations dependent on constant connectivity.

Cloud Connected Resilient Architecture:

This architecture can be deployed in two configurations to support scenarios where cloud connectivity every 30 days is possible or not. The connected configuration below enables the ability to extend the cloud to the tactical edge, while connectivity is available, but maintains system operations when it is not. Which is the critical element of supporting mission and tactical solutions.

How does Data Flow through this solution?

Collect data at the edge (UAV, TOC, ship, platform systems) to support time-sensitive decisions in operations. The battlefield of the future has an ever-growing array of new sensors and systems that are constantly capturing relevant information to support the warfighter.
Ingest mission data into a local processing environment (AKS enabled by Arc on Azure Local) so analytics can run close to the source, even when reach-back is limited.
Run AI models in Kubernetes pods on the AKS enabled by Arc cluster using validated on-premises edge hardware (including GPU-enabled nodes where required).
Enforce network segmentation to isolate workloads and enclaves, supporting least-privilege access and separation of mission functions.
Replicate container images to the edge using Azure Arc connected registry so updates can be staged when connected and consumed when disconnected.
Synchronize selected data products and model outputs to the enterprise when communications permit, using the preferred transport available in theater and taking advantage of existing or new backhaul investments.
Enable connectivity through deployed tactical network to support connectivity to additional assets and Forward Deployed C2 Systems.
SCP enables secure connectivity between classified tactical edge locations and the Microsoft classified cloud via Microsoft’s global network, which spans more than 175,000 miles of terrestrial and subsea fiber. This connection is used when policy and network conditions allow.
Apply role-based access control (RBAC) and Azure Policy to enforce configuration and compliance with intent from Azure Government to the local environment.
Forward logs, health, and security telemetry to the Microsoft security toolchain when connected to support visibility, triage, and continuous hardening.

What are some of the key architectural considerations?

When architecting this solution, Azure Local offers features that improve adaptability and support future evolution of the platform.

Azure Local and Infrastructure-as-code – Infrastructure-as-code at the edge strengthens security control through policy enforcement while preserving flexibility as new services become available.
Enable Remote Monitoring and Management Plane – Through Azure Arc, Azure Local is able to be remotely managed and monitored when connectivity is available, including pushing updates without needing on-site system administration support.
Cloud Development Mirrored on the Edge Device – Platform-as-a-Service offerings on Azure Local let developers build in the cloud using the same approach used at the edge, improving scale and test coverage during development.
Azure Local Provides Scalable Solutions for the Edge – With the right partner hardware, Azure Local can be sized for mission environmental needs, making it possible to scale deployments across configurations and sites.
Enabling GitOps and Registry Mirroring for Application Deployment – Azure Arc combined with Azure Local helps standardize containerized application delivery through GitOps workflows and registry replication, so updates can be staged when connected and consumed when disconnected.

What are the advantages of this architecture?

Power AI at the tactical edge – Run GPU-enabled, containerized models locally with consistent platform services.
Extend cloud operations patterns to the edge – Use AKS and infrastructure-as-code to deliver repeatable deployments, lower-touch sustainment, and faster updates across sites.
Platform that evolves with mission needs - Abstracting the underlying hardware helps teams adapt and extend solutions over time.

What are the key integrations?

AKS management (onsite or remote from cloud) – Use Azure Arc-enabled Kubernetes for centralized management when connected and resilience when disconnected.
Registry replication – Use Azure Arc Connected Registry to replicate images from cloud registries to the tactical edge.
Policy enforcement – Apply Azure Policy locally to drive compliance and configuration consistency, even while disconnected.
Logging + security integration – Integrate with Azure Arc and Microsoft Defender to support monitoring and security posture across dispersed edge deployments.

Fully Cloud Disconnected Architecture:

This architecture can be deployed in scenarios where there is no ability to connect back to the cloud to support management and operations. The disconnected configuration below enables the ability to leverage cloud deployment patterns at the tactical edge, and enables an enhanced platform and technologies despite never connecting to the cloud.

The primary difference between the architectures being that the solution can be configured to support never being connected to the cloud. In which case, the solution would only connect to the tactical network. In this scenario, by design there is no option for remote management via arc, but the capabilities of Policy Enforcement, Infrastructure-as-code, and hardware platform abstraction are all still available.

Adaptive Cloud and Innovation at the Edge:

Azure Local and Microsoft technologies bring cloud capabilities to the tactical edge while maintaining resiliency amid uncertain connectivity and changing mission needs.

For additional background on Microsoft’s Defense and mission offerings, see the Defense and Intelligence Solutions site: Defense and Intelligence Solutions | Microsoft AI

For questions about this architecture or edge deployment scenarios, you can contact the mission team at mission@microsoft.com.

1:1 Teams Chat between commercial and DOD - 504 error

AKG_Advisors — Tue, 14 Jul 2026 15:08:42 GMT

Hi. Wondering if anyone can tell me what setting I ought to look for to figure out what happened. I used to be able to chat 1:1 with government from commercial and DoD via skype interop. I went through a domain change, and now when I try to chat, I get "message failed to send" or I get a 504/1008 error.

Doesn't make sense to me.

Would appreciate any guidance/help on what to look for.

Microsoft support hasn't been helpful. Almost all public documentation says, "turn on cross cloud". But that's not so much the issue.

新盛公司游戏网址

开户腾龙 — Sun, 12 Jul 2026 07:21:54 GMT

腾龙公司负责人薇：《 T L 9 1 9 98》

第一步：打开浏览器输入网址《T L 0 6 8 .《 v i p》点击注册。

第二步：在注册页面，找到“注册会员”按钮并点击注册。

第三步：进入注册界面后，填写必要的个人信息，如用户名、手机号码、Q等。

第四步：设置安全且易记的登录密码。

第五步：提示注册成功，就可以下载APP登录游戏。

腾龙公司游戏网址

龙腾乐娱 — Sun, 12 Jul 2026 07:13:08 GMT

维《T L 9 1 9 9 8》公司开户罔纸《T L 0 6 8 . v i p 》

腾龙公司是扎根于缅甸果敢自治区的多元化企业秉持诚信创新共赢的理念，

经过多年发展业务以涵盖房地产基建旅游物流运输以及农产品加工等多个领域，

在房地产领域公司凭借专业团队，打造出多个现代化住宅与商业化项目，

推动果敢老街城市化和公共设施建设，助力当地基础设施升级，

旅游板块投资建设高端酒店和绿发展度假村，位游客带来舒适体验，

带动区域旅游发展，物流运输上建立高效物流网络，

为企业和居民提供便捷服务农产品加工中引入新建技术，

提升农产品附加值增加农民收入不仅如此公司始终将社会责任抗在肩头。

积极投身公益事业在教育医疗和环境保护等领域大力支持，

为果敢地区发展贡献力量，为公司将持续创新拓展业务版图，

促进区域繁荣在新时代书写更多辉煌篇章下期科普腾龙公司现状带大家更多了解缅甸果敢老街

Microsoft Prompt Gallery for Gov Clouds

sara_clark_NH — Wed, 08 Jul 2026 13:55:04 GMT

Has anyone found a way to access Microsoft's prompt gallery with a gov cloud? Any web access link requires you to log in and brings you to the commercial endpoint. I don't see a way to access it from inside Office using the Copilot panel. We are GCCH.

Also has anyone built an internal prompt gallery for your business to use and share prompts? Did you create this in a SharePoint list? Would love to hear other's ideas and even some examples of what is working great for them as far as what you are capturing and what is your repository.

Planner Premium in GCCH

sara_clark_NH — Wed, 08 Jul 2026 13:46:57 GMT

Hi all,

We are looking to do more with Planner Premium and are frustrated at the lack of tools given to gov clouds to support the conversion of Planner basic plans to Planner Premium. We don't have the magic button to add the premium views like commercial. I then attempted to build a flow to do this based on a basic plan exported to Excel and we don't have the action within Power Automate to interact with Premium plans. It only allows me to select the basic Planners, the Premium ones don't show up in the drop-down to select. Has any one else figured out a way to do this other then manually recreating your tasks from one plan to another?

UK Public Sector IT Professionals Needed - MSc Research Participation Request

GreenResearcher — Mon, 29 Jun 2026 21:39:21 GMT

Hello,

I am an MSc Cybersecurity student at the University of South Wales conducting research on Microsoft 365 security compliance in UK public sector organisations.

My dissertation investigates the gap between the default security configuration of Microsoft 365 and the outcomes required by the NCSC Cyber Assessment Framework v4.0. I have developed a practical compliance roadmap and I am looking for experienced IT professionals to review it and complete a short anonymous questionnaire.

What is involved:
Reading a compliance roadmap document — approximately 20 to 30 minutes
Completing a short anonymous questionnaire — approximately 12 minutes
Submitting responses within seven days

Who I am looking for:
IT managers, cloud architects, systems administrators or CISOs
Working in or contracted to UK local government, NHS, education or public sector cloud security
Minimum three years of Microsoft 365 experience

Everything is completely anonymous. No names or identifying information are collected. This research has received ethical approval from the University of South Wales.

If you are interested please reply to this post or send me a direct message.

Thank you.

Microsoft SPARK: Powering America’s Genesis Mission for Scientific Discovery

bryanlopez — Fri, 26 Jun 2026 15:38:01 GMT

The Genesis Mission: A National Commitment to Discovery

The U.S. Department of Energy announced the Genesis Mission as a historic national effort to use the power of artificial intelligence to accelerate scientific discovery, strengthen national security, and drive energy innovation. The Genesis Mission brings together the DOE’s 17 National Laboratories, advanced experimental facilities, large-scale scientific datasets, and next-generation computing resources into a more integrated scientific discovery platform. The goal is to double the productivity and impact of American research and innovation within the decade by embedding AI directly into the scientific process.

This initiative builds on the Removing Barriers to American Leadership in Artificial Intelligence Executive Order. It also advances America’s AI Action Plan, a directive to reduce dependence on foreign adversaries and unleash the full strength of America’s scientific enterprise. DOE officials describe Genesis as building a “national AI platform for scientific discovery,” where labs can combine traditional simulation with generative and agentic AI for fields ranging from fusion and grid optimization to climate modeling and materials science.

Introducing SPARK: Microsoft’s Answer to the Mission

Microsoft’s Scientific Partnership Advancing Research & Knowledge (SPARK) is a mission-aligned program designed to help DOE National Laboratories accelerate AI-for-science outcomes for the Genesis Mission. SPARK combines disciplined program execution, hands-on technical delivery, and significant investments in AI and scientific computing services — so high-value pilots can be selected quickly, delivered predictably, and scaled into secure, compliant, repeatable production workflows.

SPARK alignment focuses on 5 key pillars:

1. A Dedicated Program Management Office

Microsoft is standing up a dedicated PMO to serve as the front door for Genesis Mission collaboration and the coordinating hub for the full breadth of resources Microsoft brings to bear. The PMO will orchestrate engagement across Microsoft program, technical, research, engineering, security, compliance, partner, and field teams — ensuring DOE and the National Laboratories have a clear, consistent path to access Microsoft expertise, align priorities, remove blockers, and move high-value use cases from intake through delivery. This includes project intake and selection, sprint and checkpoint cadence, cross-team resource coordination, capacity planning, and an operational interface with DOE for alignment, reporting, and sustained partnership.

2. An AI-for-Science Center of Excellence

Operating under the PMO, Microsoft’s COE is an integrated delivery team helping DOE labs move AI-for-science use cases from concept to secure, compliant, scalable implementation — spanning solution design, data and AI engineering, HPC-enabled training and inference, and production workflow automation. The COE provides enablement through office hours, hackathons, and targeted training, with emphasis on responsible AI and reproducible research pipelines. It also maintains reach-back to Microsoft research and engineering teams to help labs evaluate and leverage emerging capabilities.

3. Substantial Azure Cloud Service Credits

Microsoft is making available significant Azure cloud service consumption credits to support DOE-sponsored research initiatives. Credits will be allocated to projects that use Microsoft technology to accelerate science, with preference for rapid adoption of AI services and joint DOE–Microsoft researcher participation.

4. Professional and Technical Services Investment

Microsoft is making available dedicated investment funds to support professional and technical services for DOE National Laboratories — helping labs adopt and operationalize Microsoft AI-for-science capabilities. These services can be delivered by Microsoft teams or Microsoft-approved partners.

5. Joint Research & Development

Microsoft and DOE labs will jointly select a focused set of Genesis-aligned challenge problems for co-development — combining Microsoft research expertise with DOE scientific leadership and facilities. Collaboration may include joint experimental and computational work, scientific validation using DOE lab capabilities, and joint AI-for-science model training and evaluation. Milestones and success criteria will be co-defined; publications, IP, and data use will follow mutually agreed terms.

"An undertaking the magnitude of the Genesis Mission only works when partnership and discipline match the energy behind it. The SPARK PMO's role is to keep Microsoft and DOE moving as one team — capitalizing on every bit of momentum to deliver tangible outcomes quickly to turn our shared ambition into mission-scale science."

- Clementine Wall, Director, SPARK Program Management Office

The Technology Behind SPARK

SPARK is powered by the full breadth of Microsoft’s enterprise cloud and AI portfolio — a proven, secure, and FedRAMP-authorized technology stack built for the demands of government science. Key capabilities include:

Azure Cloud Infrastructure — Hyperscale compute, storage, and networking purpose-built for high-performance scientific workloads, with government-grade security and compliance accreditations spanning unclassified to Secret environments.

Azure AI Foundry & AI Models — A unified platform for building, deploying, and governing AI and machine learning models at and agents scale, giving DOE researchers access to frontier AI capabilities within a controlled, auditable environment.

Scientific Computing — GPU and CPU resources optimized for simulation, model training, and inference workloads that complement DOE’s on-premises HPC systems at the National Laboratories.

Cybersecurity Tools — Microsoft Defender, Sentinel, and Entra ID provide layered, Zero Trust security across the research environment, protecting American science by meeting NIST 800-53 standards and supporting continuous compliance monitoring.

Data Lakes & Analytics — Azure Data Lake and Fabric enable labs to ingest, store, and analyze massive scientific datasets across disciplines — breaking down silos and creating a unified data foundation for AI-driven discovery.

Microsoft Discovery — Building on these foundational components is Microsoft Discovery — Microsoft’s integrated science platform that brings together AI models, simulation, data, and experimental workflows in a single governed environment. Microsoft Discovery is designed to match how modern science operates: across labs, disciplines, and data types.

Advanced cognition capabilities enabling autonomous workflow orchestration: AI-driven planning, execution, and adaptation of complex scientific workflows under researcher direction. Together, this technology stack gives DOE scientists the tools to move faster — from hypothesis to discovery — without compromising the security, reproducibility, or governance standards that mission-critical research demands.

Why This Matters

Under its Genesis Mission agreements, Microsoft is effectively becoming part of the federal R&D stack — providing hyperscale compute, advanced models, and tooling that runs alongside DOE’s own high-performance computing systems at national labs like Argonne and Los Alamos. The program is explicitly framed as a way to boost scientific productivity and reduce reliance on foreign technology as AI demand continues to grow.

Ultimately, SPARK is designed to turn Genesis Mission priorities into measurable scientific outcomes — combining disciplined execution, hands-on delivery, and targeted investment to move the most promising use cases from selection to mission-scale impact across the DOE and National Labs.

America’s scientific leadership is not just an academic achievement. It is a national security imperative and an economic engine. With SPARK, Microsoft is committed to making sure that leadership is not just maintained — but accelerated.

For more information, reach out to your Microsoft Account Team or email genesismission@microsoft.com

Bryan Lopez is a Senior Director for Federal Strategic Science Missions at Microsoft and functions as the leader for the Genesis Mission program and partnership at Microsoft.

Calling APIs using private Certificate Authorities from Logic Apps

JohnScott — Tue, 05 May 2026 23:13:56 GMT

A colleague reached out to me to help with a customer's Logic App issue. They were trying to make what looked like a simple HTTP request to a Jira API but were getting back an SSL error. The root of the error was that the customer was securing the API endpoint with a certificate generated from a private Certificate Authority in their environment. Because this certificate was not signed by VeriSign, GoDaddy or other public certificate authority, and the Logic App HTTP Connector was failing as it did not trust the issuer.

The fix for this is simple as it turns out but required a bit of reading the manual. First, go to Certificates and load the root CA and any intermediate CAs you might be using to the store.

Then, to get the Logic App to load these certs, go to Environment Variables and add a new setting WEBSITE_LOAD_ROOT_CERTIFICATES and place the thumbprints of the added root and intermediate CAs as a comma delimited string (hat tip to Glen from our consulting team for figuring that one out). When you save the change, the service will restart and will now trust these private certificates.

It should be noted that Logic Apps Standard is a flavor of Azure App Service, so this fix would also work for a regular App Service or a Function App as well.

I hope this helps you out!

Logic Apps, Windows auth, the On-Premises Data Gateway and you

JohnScott — Tue, 05 May 2026 22:16:12 GMT

I was working with a customer recently who had a somewhat unusual situation, unusual in that I have become accustomed to most government customers having an Express Route or site-to-site VPN connection to connect their on-premises network to their Azure environment. However, this customer had a need to connect to an on-premises SQL instance via the on-premises data gateway (OPDG). The OPDG is installed as a Windows service on a machine inside the on-premises network and, when registered in Azure, allows Azure resources to connect to services on premises (more here).

I did not have any direct experience with this tool but worked with the customer to get it installed on their network, which included getting it to work with their proxy (thanks to the larger Microsoft team for the assist and thanks to the customer for their patience figuring this out). We got the gateway registered in Azure and were ready to test it out.

The customer is using Logic Apps and the SQL Connector to connect back to their on-premises SQL instance. The initial attempt to connect to SQL using SQL credentials was successful (Yay!!!) but the customer wanted to use Windows Authentication (which is a best practice). However, when trying to establish the connection with Windows Authentication, they were getting the error "status 401 - Credentials are missing or not valid...The credentials provided for the SQL source are invalid."

Doing a little sleuthing in the Security logs on the SQL server, we could see that the authentication attempt being made, but with null credentials. Of course! We're passing our Windows credentials to the OPDG, but we haven't configured the gateway service account with delegation permissions to pass them on to SQL!

To configure the on-premises data gateway account for delegation, do the following:

Create a Service Principal Name (SPN) using the setspn command line tool for the OPDG service account:
setspn -S gateway\gatewayMachineName domainname\gatewaySvcAcctName
Open Active Directory Users and Computers (ADUC) and locate the OPDG service account
Open the Delegation tab and select "Trust this user for delegation to specified services only" and "Use Kerberos Only" then add the services for which you want the OPDG to have delegation, e.g. SQL Server.

Once this was done, we could create the Logic Apps SQL Connection successfully. A good check is to see if you can enumerate the list of tables in the database to which you are querying.

As it turns out, there is fairly good documentation on this topic under the Power BI documentation, but searching for this under the context of Logic Apps didn't yield much success! In any case, I hope this helps someone else in the future!

Join us at Microsoft 365 Copilot Discovery event in Huntsville, AL! - UPDATED LOCATION AND DATES!

Jake Kappus — Tue, 12 May 2026 19:28:08 GMT

Same mission. Tighter format. More signal, less noise.

We've redesigned the Microsoft 365 Copilot Expo & Discovery into something more valuable: two focused, small-group sessions where you can ask real questions, see real demos, and leave with something you can actually use!

The Updated Format:

Two action packed sessions!

Session 1	10:00 AM – 12:00 PM
Session 2	1:00 PM – 3:00 PM

Each session runs two hours and includes:

Mythbusting AI
We take the most common assumptions about AI head-on. What's true, what's overblown, and what actually matters for your work
Featured Presentation
A focused look at how Microsoft 365 Copilot applies in the government clouds.
BONUS! Self-Guided Demo Room
Step in, step out, explore at your own pace. Real tools. Real scenarios!

The Date and New Venue:

📅 Date: Tuesday, May 19, 2026

NEW LOCATION!
📍 UAH Invention to Innovation Center (I2C), 850 Ben Graves Dr NW, Huntsville, AL 35816

Who can attend this event?

All U.S. Federal Government employees, U.S. Department of War personnel, Defense Industrial Base and Defense Contractors.

Register now → https://aka.ms/milehuntsville
Questions? → eMail: mileeventsteam@microsoft.com

Three days. Six experiences. The launchpad your Copilot rollout has been waiting for.

Architecture Risk Brief: Silent Data Integrity Failures in Distributed Criminal Justice Systems

deepeshd87 — Fri, 24 Apr 2026 17:18:12 GMT

Why Modernized Public Safety Environments Need Stronger Data Integrity Controls

In criminal justice information services systems, the most dangerous failures are often the ones you cannot see.

A system may appear fully operational—dashboards green, services responsive, transactions flowing—while critical data is incomplete, inconsistent, or out of sync across connected platforms. In these environments, the absence of alerts does not necessarily mean the absence of problems. Instead, it can signal that data integrity issues are developing silently beneath normal system behavior.

As agencies modernize criminal justice information services (CJIS) systems, adopt cloud platforms, and expand data sharing across jurisdictions, the challenge is not only keeping systems online; it is ensuring the data moving between them remains accurate, consistent, and trustworthy.

Why This Risk Is Growing

Criminal justice agencies are going through rapid modernization, and with that comes a level of complexity that simply didn’t exist in earlier, more isolated systems. In many environments, legacy applications are still running alongside newer cloud-based platforms, which creates gaps in how data is processed and interpreted. At the same time, transaction volumes have increased significantly, and under heavy load it’s not uncommon to see partial commits, retry behavior, or subtle inconsistencies that are hard to detect.

There’s also a growing expectation for near real-time synchronization across systems, even when those systems weren’t originally designed to stay perfectly in sync. As more agencies begin sharing data across jurisdictions, the number of integration points increases, and each one introduces its own risk. None of these changes are inherently problematic, but together they create conditions where data integrity issues can develop quietly without triggering any obvious system failures.

These changes improve capability but also create new failure modes that traditional monitoring does not detect. System uptime alone is no longer a reliable indicator of operational health. The CJIS Security Policy reinforces this requirement by mandating that criminal justice information (CJI) remain accurate, complete, and protected from unauthorized alteration throughout its lifecycle.

What Silent Data Integrity Failures Look Like

Silent failures almost never show up as outages. Most of the time, everything looks fine on the surface—systems are up, jobs are running, dashboards are green. The problems usually come to light much later, often when someone is preparing for an audit, reconciling data between agencies, or digging into a case where something just doesn’t add up.

In one scenario, a transaction completed successfully in the source system but never made it to a downstream platform. There were no errors, no retries flagged—just missing data. In another case, records looked perfectly valid within each system, but when compared across environments, they didn’t match. These kinds of discrepancies tend to surface during reporting or compliance checks, not during normal operations.

That’s what makes them difficult to catch. From an operational standpoint, everything appears healthy. There are no alerts or obvious failures, but underneath that, the data has slowly drifted out of sync.

Database Corruption: The Most Silent Failure of All

Beyond synchronization gaps, database corruption represents an even more dangerous and often invisible threat. Corruption can arise from:

Storage subsystem issues
Hardware degradation
Incomplete writes under high load
Failover anomalies
Legacy-to-cloud interactions

Low-severity corruption may go unnoticed for weeks but eventually impacts multiple agency systems. Because corruption directly threatens the accuracy and integrity of CJI, it poses a significant CJIS compliance risk.

My Implementation: Automated Corruption Alerts

To deal with this, I implemented a simple automated alerting system that monitors corruption indicators and notifies me as soon as something looks off. Instead of waiting for issues to surface during audits or downstream failures, this provides an early signal that something isn’t right.

In practice, it means I can react quickly, investigate the issue before it spreads, and avoid situations where bad data propagates into other systems. In CJIS environments, even a single corrupted record can have real consequences, so early visibility makes a meaningful difference.

Flow Diagram to Detect Integrity

Root Causes of Silent Data Drift

In most cases, these data integrity issues don’t come from obvious failures—they build up during normal day-to-day operations. In high-volume systems, retries and partial commits under load can leave data in an inconsistent state without triggering any errors. During modernization or cloud migrations, subtle differences in schema behavior or transformation logic can cause data to drift between systems over time.

Another common gap is monitoring. Most setups track uptime and performance, but very few validate whether the data itself remains consistent across platforms. And once data moves across multiple systems and integrations, each handoff becomes a potential point where something can go slightly wrong. None of these issues stand out individually, but together they create conditions where inconsistencies quietly accumulate.

Next Steps for Agencies

Criminal justice organizations don’t need to overhaul their entire technology stack to strengthen data integrity. Instead, they can take practical, incremental steps that build resilience into existing systems while preparing for future modernization.

Establish a Baseline for Data Integrity Map where data originates, how it moves, and where it is stored across multiple agency systems.

Implement Routine Cross-System Validation Use Azure Data Factory, Azure SQL Data Sync, and Log Analytics queries to automate comparisons between operational and reporting systems.

Monitor for Corruption and Synchronization Failures Enable corruption detection and configure automated notifications—similar to the low-to-critical corruption alerts I implemented.

Treat Failover and Migration as Integrity Events Use Azure SQL Failover Groups and ADF pipelines to verify data consistency before and after transitions.

Strengthen Governance and Documentation Use Microsoft Purview to track lineage, schema changes, and data ownership.

Build a Culture of Data Integrity Encourage teams to treat data correctness as a shared responsibility across the organization.

Final Thoughts

Criminal justice information systems have made significant progress in availability, scalability, and security. But as these systems become more distributed and interconnected, data integrity—including corruption detection—is emerging as one of the most critical and least visible operational risks.

The challenge is no longer simply ensuring systems stay online. It is ensuring that the data moving through them remains correct, consistent, and trustworthy across every system, agency, and workflow that depends on it.

In environments where data directly impacts investigations, reporting, and compliance decisions, integrity must be engineered, validated, and continuously enforced with the same rigor applied to system availability and security.

B2B SPO: GCCH tenant members as guests on commercial Entra

Kristin_L_365 — Fri, 24 Apr 2026 15:35:39 GMT

Has anyone successfully enabled GCC guest access to a Commercial SharePoint Online site?

We support customers on GCCH tenants and are migrating an on‑prem SharePoint workload to Commercial M365 SPO. Inviting GCC users as guests fails with token/Token ID errors. Entra sign‑in succeeds, but SharePoint token issuance fails. CTAP is configured on both GCC (outbound) and Commercial (inbound).

Microsoft support indicated it may not be possible (“Entra won’t pass OIDC to SPO”), but Microsoft documentation suggests B2B for SharePoint works across US Gov and Commercial.

If you’ve made this work (or confirmed it can’t), I’d appreciate any practical guidance or gotchas. Thanks!

Strengthening Microsoft 365 and Copilot in Government Clouds with In-Product User Feedback

jacobcamp — Mon, 13 Apr 2026 15:00:00 GMT

Delivering secure, reliable, mission-aligned productivity experiences in government clouds requires ongoing collaboration between Microsoft and the public sector organizations we serve. As organizations deploy Microsoft 365 apps and Copilot, tenant administrators need a way to understand how these experiences perform in real-world environments and pass that feedback on to Microsoft.

In-Product Feedback enables IT leaders to capture end-user signals inside Microsoft 365 and Copilot while keeping controls aligned to their compliance posture. In GCC, GCC High, and DoD, In-Product Feedback helps IT leaders spot mission-impacting experience issues, capture environment-specific needs, and share actionable signals with Microsoft.

Feedback Options for Government Clouds

Tenant administrators can choose between two configurations. The table below summarizes the key differences to help you decide what is appropriate for your environment and governance model.

Decision area	Restricted Feedback *(Default)*	Verbatim Feedback *(Admin-Enabled)*
What users can submit	Thumbs up / thumbs down and optional Microsoft-defined categories. No written comments.	Thumbs up / thumbs down and optional written comments. Categories available where applicable.
Where feedback is collected	Copilot experiences only.	Microsoft 365 and Copilot experiences.
Admin action required	None. Available by default.	Enable in Microsoft 365 feedback policy settings.
Operational use	Identify broad experience trends across your organization(s).	Identify and help understand mission-impacting usability or workflow opportunities.
Engineering influence	Supports aggregate experience analysis.	Supports engineering understanding of environment-specific feature behavior.
Why choose it	Low-friction signal at scale, with structured inputs aligned to tighter governance requirements.	Richer scenario context to help accelerate triage and improvement of specific product or feature challenges.

Value for Tenant Admins and IT Leaders

In-Product Feedback provides:

Early visibility into user experience trends and mission-workflow issues
A clear signal path to share environment-specific issues with Microsoft engineering
Real-world input to guide Copilot rollout, adoption, and support
Evidence to inform operating model, governance, and change-management decisions
Data-driven input that helps prioritize future product improvements

Choosing Restricted or Verbatim Feedback enables tenant admins to adjust the signal they receive and the data they share based on their governance, compliance, and support model.

How Microsoft Uses Feedback from Government Clouds

Feedback submitted by users in GCC, GCC High, and DOD environments is reviewed in aggregate to:

Detect recurring user experience challenges in sovereign cloud deployments
Prioritize quality improvements relevant to usage scenarios for these environments
Refine feature behavior across Microsoft 365 and Copilot workloads
Inform roadmap planning for GCC, GCC High and DOD environments

Microsoft uses these aggregated insights to help improve product quality in sovereign cloud deployments — triaging recurring issues, validating feature behavior, and informing product decisions across Microsoft 365 and Copilot for GCC, GCC High, and DoD.

Get Started Today

Tenant administrators can either:

Enable Verbatim Feedback in Microsoft 365 feedback policy settings to allow users to include optional written comments. Tenant administrators should consider this option when: richer scenario context is needed for Microsoft 365 and Copilot, piloting or deployed Microsoft 365 Copilot, investigating workflow-specific items, exploring deployment adjustments, or collaborating with Microsoft to explore and address capabilities or feature behavior.
Take no action to keep Restricted Feedback available using sentiment-based responses (i.e., thumbs up/thumbs down).

Choose the option that fits your governance model and together we can keep improving Microsoft 365 and Copilot.

To learn more about managing feedback policies in Microsoft 365 visit the following resources:

From Fragmentation to Resilience: Why Next Gen “Whole of State” Is Future of Pub Sec Cybersecurity

CyberCorey — Mon, 06 Apr 2026 11:00:00 GMT

Each year at the Billington State and Local Cybersecurity Summit, one message comes through clearly: the cyber threat landscape facing state and local governments is accelerating faster than traditional models of defense can keep up.

Cyber risk is no longer confined to a single agency, system, or jurisdiction. It spans emergency management, education, healthcare, critical infrastructure, and the workforce itself. At the same time, public sector leaders are being asked to modernize services, adopt AI responsibly, and do more with constrained resources.

These pressures are not isolated—and neither can the response be.

That is why Microsoft is focused on a Next Gen Whole of State approach: a state-wide, coordinated model that brings together cyber defense, risk management, and workforce development into a unified strategy—designed for scale, resilience, and trust.

Why “Whole of State” Matters Now

Many states have invested significantly in cybersecurity over the past decade. Yet most efforts remain fragmented—with agencies operating independently, duplicating tools, and competing for scarce talent.

Internal Microsoft analysis and field experience show that this model creates three persistent challenges:

Limited visibility across agencies and jurisdictions
Inconsistent security posture and response capability
Ongoing workforce shortages that slow modernization efforts

A Next Gen Whole of State program is designed to address these challenges holistically. It is a state-wide shared services model that improves efficiency, strengthens critical infrastructure defense, and accelerates AI and cyber talent development—while respecting the autonomy of individual agencies.

This is not about centralizing control. It is about coordinating outcomes.

Cybersecurity as Critical Infrastructure

At Billington, state and local leaders consistently emphasize that cybersecurity must be treated as critical infrastructure protection, not simply an IT function.

Next Gen Whole of State reflects that reality by enabling:

Shared cyber services across agencies and local governments
Proactive identification of vulnerabilities and “slow-burn” risks
Streamlined collaboration during incident response and emergencies

By aligning technology platforms, processes, and partners, states can move toward a more collective defense posture—reducing duplication while improving resilience across the entire ecosystem.

This approach supports more consistent policy enforcement, better situational awareness, and more efficient use of limited funding—priorities that resonate strongly across the state and local community.

Workforce Development Is a Security Imperative

Another theme that consistently surfaces at Billington is the workforce challenge.

Technology alone does not secure a state. People do.

Next Gen Whole of State explicitly integrates workforce and economic development into the security strategy. Through hands-on skilling, apprenticeships, and industry-recognized certifications, states can help build sustainable pipelines of AI and cyber talent using real-world platforms and tools.

This model supports:

Career-ready training aligned to actual state and local needs
Opportunities for students, veterans, and career changers
Long-term reduction in dependency on external resources

By investing locally, states strengthen both their security posture and their communities—an outcome public sector leaders increasingly view as inseparable.

Microsoft’s Role: Thought Leadership at Scale

Microsoft’s contribution to Next Gen Whole of State is grounded in three principles reflected across our public sector work:

Unified platforms that span identity, security, compliance, and AI
Cross-sector collaboration, connecting government, education, and partners
Responsible innovation, aligned with Zero Trust and secure-by-design practices

This enables states to move beyond isolated pilots toward enduring, state-wide programs—while positioning themselves to adapt as threats and technologies evolve.

Importantly, Whole of State also creates a framework for consistent executive engagement, allowing leaders to align strategy, funding, and outcomes around a shared vision.

Looking Ahead

The conversations happening at Billington reflect a broader shift underway across the public sector.

States that lead in the next decade will be those that:

Treat cybersecurity as a shared responsibility
Align technology, policy, and workforce strategy
Build trust through resilience, transparency, and scale

Next Gen Whole of State is not a single product or program.
It is a strategic approach to how states protect critical infrastructure, modernize services, and prepare their workforce for an AI-driven future.

And it is increasingly clear that this approach is no longer optional—it is foundational.

Join the Conversation

Microsoft continues to work with state and local leaders, educators, and partners to advance Next Gen Whole of State initiatives across the country.

To learn more or engage with the Microsoft Security community, visit the Microsoft Tech Community and continue the conversation.

Continuing our momentum: Expanding Microsoft 365 Copilot capabilities across U.S. Government clouds

elisabeth_jones — Thu, 02 Apr 2026 16:00:00 GMT

In December we announced the availability of Microsoft 365 Copilot for customers in GCC‑High—an important milestone in bringing work‑ready AI to organizations that meet the required security and compliance thresholds. Since then, we’ve continued to make steady progress expanding Copilot capabilities across GCC, GCC‑High, and DoD, building on earlier launches and deepening the value customers can access today.

With the rollout of the Analyst and Researcher agents and expanded agent creation capabilities in U.S. Government clouds, we’re marking another step forward in our commitment to delivering Copilot experiences to public sector customers—deliberately, responsibly, and ready for use from day one.

Agentic experiences, built for U.S. Government clouds

Agentic capabilities in Copilot for U.S. Government clouds now include Researcher and Analyst, along with Agent Builder and Copilot Studio publishing to create and share agents.

Researcher (rolling out now, starting with GCC) supports multi‑step research by gathering and synthesizing information across work content to produce clearer, grounded drafts—within U.S. Government cloud security and compliance boundaries. Learn how to get started with Researcher today.

Analyst (now available in GCC, GCC-High, and DoD) supports data analysis by turning information into structured insights and visualizations for decisions and briefings, including pattern detection and clear summaries of large volumes of information. Learn how to get started with Analyst today.

Agent Builder (now available in GCC and GCC-High) expands agent creation capabilities in U.S. Government clouds by enabling users to package instructions, prompts, and work knowledge into reusable agents that help ensure more consistent Copilot responses—grounded in the right organizational context and shareable across teams. Learn more about Agent Builder.

Publishing Copilot Studio Agents to Teams and Microsoft 365 (now available in GCC) extends agentic experiences to the tools users rely on every day. By enabling agencies to make vetted, purpose-built agents available directly in familiar Microsoft 365 surfaces, this capability helps scale consistent, role-relevant guidance and workflows—while keeping usage within the same security and compliance boundaries of U.S. Government clouds. Learn how to connect and configure an agent for Teams and Microsoft 365.

What else is new this quarter

Many core Microsoft 365 Copilot capabilities have also been enhanced in U.S. Government clouds in recent months, including:

Updated OpenAI models (GCC, GCC-High, DoD): Microsoft 365 Copilot for U.S. Government now leverages GPT 5.1 for Copilot Chat, GPT-5 for reasoning, and GPT-4o for image generation, supporting more accurate and contextual responses.

Microsoft 365 Copilot app enhancements: Copilot Search (GCC, GCC-High, DoD) helps users find relevant info across work content; the Create module (GCC, GCC-High) helps draft and refine images and other content.

Copilot Chat improvements (GCC, GCC-High, DoD), including enhanced language understanding for clearer, more accurate responses; Code Interpreter for in-chat data analysis and complex problem solving; and image upload with OCR to extract text for summaries, interpretation, and next steps.

Microsoft 365 Copilot Connectors (GCC, GCC-High) expand access to relevant third-party and line-of-business data sources to ground Copilot experiences in more of your organization’s knowledge. Learn more about Microsoft 365 Copilot Connectors.

In-product user feedback (GCC, GCC-High, DoD). U.S. Government users can now submit in-product feedback directly within Microsoft 365 and Copilot to help improve product experiences for mission work.

All these capabilities – agentic and otherwise – are built to support applicable regulatory and compliance requirements out of the box. U.S. Government compliance needs—including data residency, operational isolation, and restricted personnel access—are carefully considered prior to release, reducing the need for administrators to retrofit controls, disable features, or manage separate compliance workflows.

Moving forward

Delivering Copilot to U.S. Government clouds is a journey, not a single moment. Each new capability reflects a careful, staged approach to availability—helping to ensure features are compliant by design and usable as-is by government organizations.

For guidance on leveraging Microsoft 365 Copilot effectively, visit the Microsoft 365 Copilot for US Government Adoption site. Admins can also review how to manage Copilot agents in the Microsoft 365 Admin Center to help make the most of new agentic capabilities as they roll out.

We’ll continue to share updates as additional Microsoft 365 Copilot features continue to roll out across GCC, GCC‑High, and DoD. Thank you to our government customers and partners for your continued engagement and feedback. Your input helps shape how we responsibly bring AI to mission critical work.

Microsoft 365 Copilot Prompt a thon for Government is Coming to Ft. Lauderdale

Abby_Quinn — Fri, 20 Mar 2026 17:37:18 GMT

On April 16, Microsoft is bringing the Microsoft 365 Copilot Prompt‑a‑thon – Government Roadshow to Ft. Lauderdale, Florida, inviting all Government customers to experience Copilot in action through a hands‑on, in‑person session.

This government‑focused Prompt‑a‑thon is designed to help public sector organizations move beyond AI awareness and into practical, responsible use of Microsoft 365 Copilot. Attendees will learn how to apply effective prompting techniques to real‑world scenarios that reflect how government and education teams work every day.

What You’ll Experience

Participants will:

Practice using Microsoft 365 Copilot through guided, hands‑on exercises
Learn practical prompting techniques tailored to government and education workflows
Explore real scenarios relevant to policy, operations, communications, and IT teams
Leave with skills and examples they can immediately apply and share

This is an interactive working session—not a lecture—focused on learning by doing.

Who Should Attend

This event is designed for:

Federal End Users
State and Local Government professionals
Education sector customers
Commercial users
IT, program, policy, communications, and innovation leaders
Teams beginning or expanding their Copilot adoption journey

Whether you’re exploring Copilot for the first time or looking to drive broader adoption, this session is built to meet public sector needs.

Join Us in Florida

Attending in person provides the opportunity to learn alongside peers, ask questions in real time, and engage directly with Microsoft experts who support government and education customers.