Accelerate healthcare innovation with Dragon Copilot apps and agents in Microsoft Marketplace
Imagine spending less time navigating systems and more time caring for patients. AI brings data, insights, and content directly into the flow of work. Dragon Copilot streamlines clinical documentation and routine tasks, so clinicians spend less time navigating systems and more time focused on patient care. By simplifying physician and nursing charting, notes, flowsheets, and radiology reporting, it reduces rework and cognitive burden, helping care teams work more efficiently and confidently throughout the day. Microsoft Marketplace now supports Dragon Copilot AI Apps and Agents, creating a scalable path for partners to bring innovation directly into clinical workflows. Dragon Copilot unifies intelligence and context at the point of care, while Microsoft Marketplace is how partner innovation becomes part of that experience, enabling AI apps and agents and to operate within the trusted procurement, security, and governance frameworks customers already rely on. Partners building AI apps and agents for Dragon Copilot can use Microsoft Marketplace as their commercial engine. Marketplace supports multiple enterprise‑ready sales motions, whether selling direct, or through partners for channel-led sales. This gives partners flexibility in how they go to market and meet customers where they prefer to buy. With support for per‑user and flat‑rate pricing, flexible billing options, and contract terms ranging from monthly to multi‑year, Marketplace enables partners to align their commercial model to customer procurement needs while scaling globally through trusted Microsoft purchasing, security, and global commerce. Dragon Copilot offer types in Microsoft Marketplace Microsoft Marketplace will support a family of Dragon Copilot offer types designed for partner solutions across clinical roles and workflows. The first offer type - Dragon Copilot Physician Apps and Agents - is now available, enabling partners to publish solutions that surface directly within physician workflows. Each offer type defines the intended user, where the solution appears in Dragon Copilot, and how customers discover, purchase, and deploy it, making offer selection a strategic decision that shapes monetization and the end-to-end customer experience. Supported offer type Dragon Copilot Physician Apps and Agents AI solutions that will surface directly inside Dragon Copilot at the physician point of care. They deliver in‑workflow access to insights, task automation, clinical decision support, and documentation assistance without requiring clinicians to leave their existing tools. These solutions are purchased through Microsoft Marketplace and become automatically available within Dragon Copilot after deployment. (Available in the United States) Best for: AI apps or agents built specifically for physician workflows Partners delivering role‑based intelligence Scenarios where fast, in‑context action inside Dragon Copilot is critical How it works Bringing an app or agent to Dragon Copilot through Microsoft Marketplace is designed to be simple and enterprise‑ready. Marketplace provides the commercial, operational, and discovery foundation so partners can focus on building differentiated AI experiences. Healthcare organizations can also choose to build and deploy apps or agents in Dragon Copilot and expand usage through the Marketplace. Step 1: Create offer selecting the offer type Begin in Partner Center by choosing the Dragon Copilot offer type that best aligns to your solution and target clinical persona. This choice determines where your solution surfaces inside Dragon Copilot, which roles it supports, and how customers discover, purchase, and activate it. After choosing the offer type, define pricing, billing, contract terms, and sales options. Microsoft Marketplace offers flexible pricing, sales channels, anc contract lengths to meet healthcare organizations' needs. Step 2: Customer discovery After publishing, customers can discover and purchase Dragon Copilot AI apps and agents through Microsoft Marketplace and through in-product experiences like, Azure. Step 3: Customer purchase Customers purchase Dragon Copilot solutions through the Azure portal using the same streamlined experience they use for other offers on Microsoft Marketplace. Customers complete their subscription details, and billing begins once they configure their account. From there, they are redirected to the Dragon Admin Center to complete a few final configuration steps and begin using the solution. Step 4: Customer usage After purchase, customers are guided to the Dragon Admin Center, where they activate and manage their Dragon Copilot solutions. From the admin experience, customers can complete any required configuration, assign the app or agent to the appropriate users or roles, and control how it is made available within Dragon Copilot workflows. The Dragon Admin Center provides a centralized place for customers to enable partner solutions, manage access, and ensure deployments align with their security, governance, and operational policies so AI apps and agents are ready for use at the point of care with minimal setup. What this means for you As Dragon Copilot adoption accelerates, Microsoft Marketplace provides a clear, scalable foundation for partner participation. With a dedicated Dragon Copilot offer type, partners can publish AI apps, agents, through a defined commercial model, gain targeted visibility through Dragon Copilot–specific discovery, and take advantage of Microsoft go‑to‑market programs. Learn more Read through our documentation on how extensions for Dragon Copilot work and how to build your own - AI apps and agents | Microsoft Learn Check out the sample repo with sample code and more - microsoft/dragon-copilot-extension-samplesPartner Blog | Building the foundation for AI: Cloud, data, security, and AI skills for partners
Customers are moving beyond AI experimentation. They are looking for partners who can connect AI ambition to the cloud, data, security, governance, and business application capabilities required to put AI to work. That makes skilling across the Microsoft stack increasingly important. It can also make the question of where to start more difficult. This month, there is a simpler starting point. The Microsoft Partner Skilling Hub Agent can recommend training, answer skilling-related questions, and generate personalized technical skilling plans based on your role and goals. From there, you can build a focused path across Frontier Transformation, agents, Microsoft 365 Copilot, certifications, hands-on learning, and co-sell execution. The foundation for AI is broader than AI skills Frontier Transformation is the shift from targeted AI pilots to repeatable, governed AI capabilities embedded into the flow of work, business processes, and customer engagement. For partners, delivering that transformation requires more than expertise in a single AI product. It requires teams that understand how cloud infrastructure, data, security, agents, and business applications work together. That foundation matters across customer segments. For partners serving small and medium-sized businesses (SMBs), it can support you in guiding customers toward practical AI adoption while addressing security, governance, productivity, and business process needs together. This month, focus your skilling plan on five areas: validating your technical expertise, building agent platform capabilities, developing AI business application skills, earning industry-recognized certifications, and applying those skills through hands-on learning. Continue reading hereBuilding human-centric security skills for AI
AI is reshaping the workplace, and the organizations thriving in this new era know it takes more than just cutting-edge tech to succeed. A new kind of company is emerging—the so-called Frontier Firm. These organizations are building their business models around on-demand intelligence and hybrid human-AI collaboration. They understand that no matter how advanced technology is, it’s people—armed with the right skills, judgment, and foresight—who make the difference. Through targeted skilling and a culture of shared responsibility, Frontier Firms are ensuring their people are prepared to meet the security demands of an AI-driven world. This growing need for cross-functional security skilling is a central theme in our new e-book Skilling for Secure AI: How Frontier Firms Lead the Way, and we’ve pulled together three takeaways across all roles that preview how leading organizations are approaching this era. Let’s take a closer look at the key insights shaping secure AI skilling today: 1. Human expertise is critical when securing your organization in the age of AI AI may be transforming how to approach daily work, but it doesn’t replace the need for human oversight, judgment, or creativity. As AI tools become more powerful, so does the opportunity for human decisions to drive meaningful impact. That’s why Frontier Firms are strengthening their commitment to human expertise. These companies are equipping their people with the skills to use AI responsibly, securely, and collaboratively. For example, whether it’s reskilling IT teams to manage system permissions or upskilling data specialists to evaluate which datasets are appropriate for use, Frontier Firms are investing in targeted security skilling. By focusing on building expertise, these organizations empower their teams to move faster, innovate more confidently, and safeguard outcomes at every step. 2. Everyday use of AI makes security skilling fundamental across the organization With AI extending into more roles, understanding where human expertise intersects with security risk is becoming essential across the organization. For instance, business leaders can use AI to analyze performance data and line-of-business users can leverage AI to generate content. Each of these AI interactions underscores the need for widespread skilling that helps employees make informed, secure, and ethical choices when working with AI. That’s why security skilling can’t be siloed. Frontier Firms are addressing this shift by making security skilling a shared, organization-wide priority. They’re focused on making secure habits a familiar and consistent part of everyday work. By doing so, they’re building a workforce that understands how to use AI tools effectively and securely, reinforcing security as a core part of productivity. 3. Continuous learning is how to stay agile in a changing world Building security skilling across the organization is an important step, but lasting impact comes from making that learning continuous and adaptive. That’s why Microsoft offers resources like Microsoft Learn for Organizations, designed to help teams build and sustain skills across roles. As the landscape evolves, organizations need to weave security skilling into their culture, so every employee feels empowered to grow their capabilities alongside the technologies they use. Frontier Firms understand this nuance and are developing ongoing strategies to build future-ready security resilience across their workforce. From hosting annual hackathons to creating quarterly forums where teams can exchange ideas, Frontier Firms empower employees to adapt, respond, and lead through change. Showcasing expertise in action: Microsoft Credentials for Security As organizations work to stay ahead in AI, proving relevant skills has become increasingly important. Employers want candidates who can show they know how to use secure AI, while job seekers aim to stand out in a highly competitive market. Microsoft, with over thirty years of experience in credentialing, offers a broad range of Certifications tailored to security roles such as Security Operations Analyst and Cybersecurity Architect, as well as Applied Skills credentials for practical scenarios involving tools like Microsoft Sentinel or Microsoft Defender. Explore Microsoft Credentials for Security. Empowering people is the strategy for securing AI By building skilling strategies that reach across functions, Frontier Firms are prioritizing trust, accountability, and long-term resilience. Their success reinforces that equipping people with the right skills to securely use AI can give organizations a lasting competitive edge. Looking to identify and contextualize AI and security learning opportunities across your organization? Explore our new e-book, Skilling for Secure AI: How Frontier Firms Lead the Way—and be sure to check out the readiness tool at the end to help assess how prepared your teams are for today’s security needs.
How valuable would a Nonprofit Check Plus API be for validating nonprofit status?
Would a Nonprofit Check Plus API make it easier for an organization to verify a nonprofit before making a donation or starting a partnership? I would like to know why people specifically prefer to use it and the advantages if any. Are there alternatives to the same means?
What is the due diligence for verifying nonprofits?
I know that before donating, funding, or partnering with a nonprofit, a few minutes of due diligence can prevent costly mistakes. But what steps can one take to verify that a nonprofit is legitimate, financially transparent, and actually doing what it claims? Are there AI and API tools one can use for this?Accelerate connectors development using AI agent in Microsoft Sentinel
Today, we’re excited to announce the public preview of a Sentinel connector builder agent, via VS code extension, that helps developers build Microsoft Sentinel codeless connectors faster with low-code and AI-assisted prompts. This new capability brings guided workflows directly into the tooling developers already use, helping accelerate time to value as the Sentinel ecosystem continues to grow. Learn more at Create custom connectors using Sentinel connector AI agent Why this matters As the Microsoft Sentinel ecosystem continues to expand, developers are increasingly tasked with delivering high‑quality, production‑ready connectors at a faster pace, often while working across different cloud platforms and development environments. Building these integrations involves coordinating schemas, configuration artifacts, Azure deployment concepts, and validation steps that provide flexibility and control, but can span multiple tools and workflows. As connector development scales across more partners and scenarios, there is a clear opportunity to better integrate these capabilities into the developer environments teams already rely on. The new Sentinel connector builder agent, using GitHub Copilot in the Sentinel VS code extension, brings more of the connector development lifecycle -- authoring, validation, testing, and deployment into a single, cohesive workflow. By consolidating these common steps, it helps developers move more easily from design to validation and deployment without disrupting established processes. Read the full announcement here: Accelerate connectors development using AI agent in Microsoft Sentinel Original Publication: Microsoft Security Community Blog, March 30th, 2026
Building Microsoft Sentinel Connectors in Minutes with the Sentinel Connector Builder Agent
Overview We previously announced the public preview of the Microsoft Sentinel connector builder agent via VS code extension, that helps developers build Microsoft Sentinel codeless connectors faster with low-code and AI-assisted prompts. This post walks through a hands-on lab using a mock Network Log API to demonstrate how the Sentinel connector builder agent simplifies building Codeless Connector Framework (CCF) pull connectors. Instead of manually creating ingestion infrastructure and configuration files, you’ll use a guided, conversational workflow in VS Code to generate connector artifacts, test them against a live API, and deploy them into Microsoft Sentinel. The lab focuses on the end-to-end experience ranging from API setup to validated connector deployment so you can see how quickly a working integration can be produced. For additional guidance beyond this lab, refer to our MS Learn documentation. The Lab Environment This lab is built around a mock Network Log API hosted as an Azure Function App. The purpose of the lab environment is to give us a live API that we can use to build, validate, and test the Sentinel CCF connector builder agent against end to end. The API exposes 50 synthetic network activity records that look and behave like a real product data source, including web traffic, DNS requests, blocked remote access attempts, malware command-and-control blocks, VPN activity, and other common network events. That makes it a useful stand-in for the type of telemetry many teams want to onboard into Microsoft Sentinel. The API is intentionally shaped like the kind of source a customer might expose for telemetry retrieval. It uses API key authentication through the X-API-Key header, returns paginated results through a nextLink model, and provides a predictable response structure that the builder agent can map into a pull connector configuration. The repo contains everything needed for the walkthrough. There is an ARM template to deploy the Function App, reference documentation for the API, and a sample connector package showing the generated polling config, table schema, DCR, and connector definition. The end goal of the lab is straightforward: use the builder agent to generate a CCF pull connector that ingests this API into the custom NetworkLogAPIGetNetworkLogs_CL table in Sentinel. Follow the full walkthrough here: Building Microsoft Sentinel Connectors in Minutes with the Sentinel Connector Builder Agent Original Publication: Microsoft Sentinel Blog, August 11th, 2026A Practical, Technical Guide to Bringing AI Into Everyday Nonprofit Workflows
Nonprofits face increasing pressure to improve efficiency, strengthen reporting, and communicate more frequently—often with limited staff and resources. Microsoft Copilot for Microsoft 365 embeds AI directly into familiar tools like Word, Excel, Outlook, Teams, and PowerPoint, allowing nonprofits to automate knowledge work without adopting entirely new systems or hiring specialized AI teams. How Copilot Works Under the Hood Copilot is built on three core components: 1. Large Language Models (LLMs) These AI models generate, summarize, and transform text and other content. 2. Microsoft Graph Microsoft Graph connects Copilot to your organization’s data—including: Emails Files (SharePoint, OneDrive) Meetings and calendars Teams chats This provides context-aware responses based on your organization’s existing content. 3. Microsoft 365 Apps Copilot is embedded directly inside: Word Excel Outlook Teams PowerPoint Together, these components allow Copilot to generate insights and content grounded in your organization’s data. [learn.microsoft.com] 📌 Important: Copilot does not create new data silos. It respects existing permissions, so users only see data they are already authorized to access. 👉 Learn more: Microsoft 365 Copilot overview Requirements to Enable Copilot To use Copilot in Microsoft 365, organizations generally need: A supported Microsoft 365 plan (e.g., Business Standard, Business Premium, E3, or E5) A Copilot add-on license Proper data stored in Microsoft 365 (e.g., OneDrive, SharePoint, Teams) 📌 Copilot’s effectiveness depends heavily on how well your data is organized and accessible. Technical Use Cases for Nonprofits 1. Grant Writing & Reporting Automation Copilot can: Summarize program outcomes from documents (Word, SharePoint) Generate draft grant narratives Rewrite content to align with funder tone Extract insights from structured data (Excel, reports) ⚠️ Clarification: In many standard Microsoft 365 Copilot scenarios, Copilot does not directly query Power BI datasets. Instead, it relies on data embedded in documents, emails, or exported reports. However, newer integrations (e.g., Microsoft Fabric and Copilot Power BI integration) allow Copilot to access and answer questions using Power BI reports and semantic models, depending on licensing, environment, and configuration. Technical Advantage Copilot uses Microsoft Graph to pull relevant context from your organization’s documents, reducing manual copy-paste work. How to Use Copilot in Word for Grant Writing Open Word Select Copilot icon to open the Copilot pane Choose Draft with Copilot (or start typing a prompt) Enter a prompt such as: “Draft a 2-page grant narrative using the attached program summary and last year’s outcomes.” (Optional) Reference or attach relevant files from OneDrive or SharePoint Click Generate Review the draft Refine using Copilot commands such as: Rewrite (improve clarity) Expand (add detail) Adjust tone (formal, persuasive, etc.) 2. Outlook + Copilot for Donor Communications Copilot can: Draft personalized donor emails Summarize long email threads Rewrite messages for tone and clarity Suggest follow-ups Technical Note Copilot can use: Previous email threads Attached documents Calendar context to generate more relevant responses. How to Use Copilot in Outlook Open a new email Click Copilot icon in the tool bar Select Draft with Copilot Enter a prompt such as: “Write a warm thank-you email to a donor who contributed $500 to our youth program.” Click Generate Review the drafted email Edit directly or use Copilot to: Rewrite Adjust tone (formal, friendly, etc.) Change length (shorter or longer) Select Keep it, then send when ready 3. Teams Meeting Summaries Copilot in Teams can: Generate meeting summaries Identify decisions and key points Extract action items Suggest follow-ups 📌 Copilot works from meeting transcripts and chat logs. How to Use Copilot in Teams Start or join a Teams meeting Enable Transcription (recommended for full Copilot functionality) After the meeting, open the meeting chat or calendar event Select the Recap tab Click Copilot Select or enter a prompt (e.g., "Recap the meeting") Review: key discussion points Decision Action items 4. PowerPoint Storytelling Copilot can transform content into presentations: Word documents → slide decks Meeting summaries → presentations Reports → visual narratives Technical Advantage Copilot uses semantic understanding to: Structure slides Generate speaker notes Suggest layouts and visuals How to Use Copilot in PowerPoint Open PowerPoint Select the Copilot icon to open the Copilot pane Provide a prompt or upload a document Copilot generates slides and notes Refine using design and layout suggestions Security & Compliance Copilot inherits Microsoft 365’s enterprise-grade security model, including: Role-based access control (RBAC) Data residency and compliance controls Existing permissions enforcement Zero Trust principles 📌 Important clarification: Microsoft states that customer data is not used to train foundation models. Data remains within your organization’s tenant boundary. 👉 Learn more: Data, Privacy, and Security for Microsoft 365 Copilot | Microsoft Learn Important Implementation Considerations 1. Data Readiness Copilot’s quality depends on your data: Organized SharePoint libraries Consistent file naming Structured documents 2. Access Control Ensure proper permissions before rollout: Avoid overexposure of sensitive data Audit SharePoint and Teams access 3. Human Oversight Copilot generates drafts—not final outputs. Always review grant narratives Validate donor messaging Confirm factual accuracy Final Thought Microsoft Copilot is not a replacement for nonprofit expertise—it is a force multiplier. By embedding AI into everyday tools, nonprofits can: Reduce administrative workload Accelerate writing and reporting Improve internal communication Focus more time on mission-driven work When implemented thoughtfully—with strong data practices, governance, and human oversight—Copilot can help organizations move faster, communicate more effectively, and make better-informed decisions. Ultimately, the goal is not just efficiency, but greater impact—freeing teams to spend less time on repetitive tasks and more time advancing the mission they serve.
Free course: Unlocking AI for Nonprofits
With support from Microsoft, NetHope has launched a new free, CPD-certified course series - Unlocking AI for Nonprofits - designed specifically for nonprofit professionals. The series will help nonprofit teams build practical and responsible AI skills, no technical background required. The four learning pathways include: AI Basics – Learn what AI is and why it matters for nonprofits Applications of Generative AI – Explore time-saving tools for content, reporting, and data Advanced Applications: Microsoft Copilot and Beyond – Help your team adopt AI with clarity and confidence Responsible Use of AI – Understand ethics, inclusion, and organizational safeguards We’ve had more than 1,000 enrollments across the series to date – don't miss out! Courses are free and available through August 31, 2025. https://nethope.org/programs/unlocking-ai-for-nonprofits-enroll-in-our-new-ai-skills-course-for-nonprofits/?utm_medium=forum&utm_source=microsoftnonprofit&utm_campaign=msaiskillsjul&utm_content=unlockingaiContainer Network Insights Agent (CNIA): Your AI Teammate for AKS Networking Incidents
Hello Folks! If you run AKS in production, you already know the script. A pod cannot reach an external service, every dashboard says the cluster is healthy, and somebody is SSHing into a node with five browser tabs open trying to piece the story together. This session from the Microsoft Azure Infra Summit 2026 tackles that exact pain. Shaifali Garg (PM for Azure Container Networking on AKS) sits down with Jonathan Wang, an AKS operator running 30 clusters across two regions on Cilium, and they walk through what a real networking incident feels like, then introduce the Container Network Insights Agent (CNIA) live in the cluster. Why IT Pros Should Care In Jonathan’s environment, about 40% of incidents end up being networking problems. The tools all exist (kubectl, dashboards, detectors, Hubble), but the time sink is figuring out which layer the problem lives in and what to check next. CNIA goes after that gap. Here is what you actually get back: A symptom-to-classification jump in seconds, so you skip the first 30 minutes of “is this DNS, policy, node, or app?” One chat window with one evidence table, one root cause, and one copy-paste fix command, instead of jumping across five tabs Senior SRE tribal knowledge baked into the workflow, so anyone on the team can run the same investigation a principal engineer would Read-only by design, so the agent never changes anything on your cluster. You stay the human in the loop Installs as an AKS extension (no Helm chart, no YAML to babysit), and Azure handles the lifecycle In short, CNIA is not trying to replace your SRE team. It hands them back 20 or 30 minutes on every networking ticket, which adds up fast across a fleet. What CNIA Is, A Technical Overview Think of CNIA as an AI teammate that lives inside your AKS cluster as a pod. You describe what is broken in plain English, the way you would ping a senior engineer on Slack, and behind the scenes the agent does four things in order. It classifies the kind of problem (DNS, egress, policy, node, app), it pulls live evidence from your cluster, it analyzes that evidence, and it hands you back a clean report with evidence, root cause, and a copy-paste exec command. Two architectural choices stand out. First, the agent uses your own Azure OpenAI resource (bring your own), so prompts and diagnostic content stay in your tenant and your region. Microsoft does not see your diagnostic data, and nothing gets persisted externally. Second, the answer is grounded in evidence pulled from your cluster, not from the internet. Your pods, your policies, your CoreDNS, your host-level NIC and kernel counters. If the evidence is inconclusive, CNIA says so rather than fabricating a root cause. That last bit is what earns trust with senior SREs. CNIA fits inside the broader Advanced Container Networking Services (ACNS) story on AKS. ACNS gives you metrics in Azure Managed Prometheus and Grafana, stored and on-demand network logs with Hubble, and FQDN-based filtering with Cilium. CNIA sits on top, automating the triage loop across those signals so you do not have to walk through the playbook by hand every time. How It Works, Under the Hood The install is an AKS extension. Roughly 5 to 7 minutes from “az aks extension” to “you have an SRE buddy in your cluster.” One small pod runs continuously. A second helper only spins up on the node during a deep packet-drop investigation, reads host-level network counters, and is cleaned up right after. Nothing left behind. Permissions are deliberately narrow: Read-only RBAC on the cluster. The agent looks, it never changes anything A workload identity tied to your Azure OpenAI resource. No shared credentials Outbound traffic is HTTPS to your OpenAI endpoint on port 443, and nothing else. If you want to log that further through an NSG or firewall, that is supported On the safety side, CNIA layers two protections against prompt injection. The agent is scope-restricted by design, so off-topic requests get rejected straight away. In one of Jonathan’s demos, Shefali asks the agent to “delete core-dns” and to “write a script to scrape LinkedIn profiles.” Both are refused on the spot. The second layer is the read-only RBAC at the cluster level. Even if someone tricked the prompt into emitting a destructive command, the cluster itself would refuse. The pod’s execution is scoped to specific diagnostic commands. It is not an open shell. Honest tradeoffs, because you will ask: It is one cluster at a time. Multi-cluster correlation is not in scope yet It does not auto-remediate. It tells you the fix, you verify and run it It is AKS only. EKS and GKE are not supported today Session state lives in the pod in memory. If the pod restarts, you start a fresh chat (past sessions are still available in history) Heavy packet-drop investigations have been validated up to around 7 concurrent users on smaller clusters. The team is actively scaling that up Real-World Value The session includes two demos that map directly to incidents you have probably lived through. Demo 1, egress that silently dies. Pods cannot reach google.com. CoreDNS resolves it fine, example.com works from the same pod, every dashboard says healthy. CNIA classifies it as an egress connectivity problem (not DNS) and surfaces the actual culprit: a Cilium network policy named “restrict external FQDN” with a toFQDN rule that only allows example.com. Everything else gets silently dropped at the egress gate. DNS was allowed, the TCP connection was not. The fix command (a kubectl patch to add google.com to the allow list) is right there in the report. End-to-end fix in under a minute. Demo 2, the target port typo. A service is down with connection refused. Pods running, service exists, endpoints populated, no network policies. The agent goes inside the pod, looks at the actual listening sockets, and proves the mismatch: target port 8080, but nginx listens on port 80. One-digit typo in YAML that no kubectl get would surface on its own. The ROI math is straightforward. If your team handles networking incidents weekly and each one costs 20 to 30 minutes of “where do I even start,” that capacity adds up across the org. And critically, the win is not just speed. When the one engineer who knows where to look goes on leave, the rest of the team is no longer stuck calling them at home. Getting Started Three steps. That is it. Read the public docs, get an overview, scan the use cases, and understand what CNIA does and does not cover Pick a cluster (dev or staging is a great place to start) and install the AKS extension. Give it 5 to 7 minutes Run a few real network tickets through it. Compare your time-to-answer before and after. Hit thumbs-up or thumbs-down in the chat so the product team sees real signal Pricing in preview: no license fee. You pay for the Azure OpenAI tokens it uses (your tenant, your resource), plus the tiny bit of cluster compute for the pod. If you already have Azure OpenAI in your tenant, just point CNIA at it. Resources Diagnose and resolve AKS network issues with Advanced Container Networking Services Advanced Container Networking Services overview Configure Azure CNI Powered by Cilium in AKS AKS cluster extensions Deploy and configure Microsoft Entra Workload ID on an AKS cluster What is Azure OpenAI Service? Keep Learning at the Summit Catch the full Microsoft Azure Infra Summit 2026 session playlist here: https://www.youtube.com/playlist?list=PLjt5SKzX1iI8con7FJDB56G6hHqxGm7ki Cheers! Pierre Roman
