HLK test failing for a KMDF driver with error “A lower driver failed IRP_MN_QUERY_STOP_DEVICE”
Subject: HLK DF‑PNP Rebalance Fail Restart Device: E: target widens to all volumes; QueryStop fails even without my driver installed. Hi all, I’m trying to pass all HLK tests for my driver. Driver: KMDF PnP volume upper‑filter (attached via UpperFilters to the Volume/Disk class) OS: Windows Server 2016 Datacenter, build 14393 HLK: 10.1.14393.8069 Test: DF - PNP Rebalance Fail Restart Device (Reliability) Target volume: E: (NTFS) Storage: PERC H330 Mini (DELL) (HBA) Observed behavior The DQ initially targets E:, but the test later widens to all STORAGE\Volume devnodes: WDTF_TARGETS : INFO : - Query("IsDevice AND (DriverBinaryNames!='i8042prt.sys') AND (Class!=hdc) AND (Class!=scsiadapter) AND (DsmDevice!=TRUE) AND (IsDevice AND (DriverBinaryNames!='i8042prt.sys') AND (Class!=hdc) AND (Class!=scsiadapter) AND (DsmDevice!=TRUE) AND (DeviceID='STORAGE\VOLUME{01FEB6F1-3717-11F1-9707-806E6F6E6963}#0000000001000000'))") WDTF_TARGETS : INFO : Target: Volume (E:) STORAGE\VOLUME{01FEB6.. WDTF_SUPPORT : INFO : - ClearSetupAPILogs() WDTF_TARGETS : INFO : - Query("IsDevice AND (SemiUniqueTargetHardwareIdentifier='STORAGE\Volume')") WDTF_TARGETS : INFO : Target: Volume STORAGE\VOLUME{01FEB6.. WDTF_TARGETS : INFO : Target: Volume STORAGE\VOLUME{01FEB6.. WDTF_TARGETS : INFO : Target: Volume (C:) STORAGE\VOLUME{01FEB6.. WDTF_TARGETS : INFO : Target: Volume (E:) STORAGE\VOLUME{01FEB6.. Failures report A lower driver failed IRP_MN_QUERY_STOP_DEVICE and PNP.RestartDevice() Win32=1 - Incorrect function. For each of the 4 target volumes, it shows this error, DTF_PNP : INFO : - EDTTryStopDeviceFailRestart() WDTF_PNP : INFO : Target: Volume (C:) STORAGE\VOLUME{01FEB6EF-3717-11F1-9707-806E6F6E6963}#000000001F500000 WDTF_PNP : INFO : Result: A lower driver failed IRP_MN_QUERY_STOP_DEVICE. Rebalance tests cannot proceed... WDTF_PNP : INFO : - EDTTryStopDeviceFailRestart() WDTF_PNP : INFO : Target: Volume (E:) STORAGE\VOLUME{01FEB6F1-3717-11F1-9707-806E6F6E6963}#0000000001000000 WDTF_PNP : INFO : Result: A lower driver failed IRP_MN_QUERY_STOP_DEVICE. Rebalance tests cannot proceed... WDTF_PNP : ERROR : PNP.RestartDevice() Win32=1 - Incorrect function. WDTF_PNP : ERROR : PNP.RestartDevice() Win32=1 - Incorrect function. Q1. I have tried multiple variations of the DQ query, and each successfully targets the E volume only. Why does this test later widen to all volumes even when DQ targets only E:? How can I force it to stay on E: ? Q2. I also ran this on a physical server client and a VM client, but faced the same issue both times. I understand the system/boot volume may veto QueryStop, but why does each volume report ‘a lower driver failed IRP_MN_QUERY_STOP_DEVICE’ in this test? Q3. I uninstalled my driver completely and still see the same failures when the test is running with only inbox drivers (e.g., msdmfilt.sys, volsnap.sys, volume.sys) listed by Driver Verifier for target E in the logs. That makes me suspect setup/config rather than my driver. Why would only these drivers fail the test as well, what am I missing here? WDTF_DRIVER_VERIFIER : INFO : - EnableOnAllDriversOfDevices() WDTF_DRIVER_VERIFIER : INFO : Target: Volume (E:) STORAGE\VOLUME{01FEB6F1-3717-11F1-9707-806E6F6E6963}#0000000001000000 WDTF_DRIVER_VERIFIER : INFO : Driver: msdmfilt.sys WDTF_DRIVER_VERIFIER : INFO : Driver: volsnap.sys WDTF_DRIVER_VERIFIER : INFO : Driver: volume.sys Thanks for any guidance!Extend Microsoft Purview data protection to AWS Bedrock agents for cross-cloud AI governance
Organizations are moving fast with AI, and many of those AI workloads are not staying in one cloud. A team might use Microsoft 365 and Microsoft Purview for governance and in addition to Microsoft Foundry they may still choose to run an AI agent on AWS Bedrock or on the Google Cloud Platform. The technical challenge is straightforward: how do you keep one consistent set of data security, governance, and compliance controls when the agent itself runs outside Microsoft Azure? This is where Microsoft Purview becomes the central policy engine for your data estate. In this post, we show why that matters and then walk through a practical example: an expense approval agent running on Amazon Bedrock, protected by Microsoft Purview Data Loss Prevention (DLP) policies. ExpenseApprovalAgent" details of the Agent blade Why Purview should be the central policy engine Most organizations do not want separate policy stacks for every cloud, every model endpoint, and every app team. That leads to duplicated controls, inconsistent enforcement, and audit gaps. The better model is to separate where workloads run from where policy decisions are made. That is the value proposition for Microsoft Purview in cross-cloud AI scenarios. Purview gives you: A consistent policy layer for sensitive information types such as credit card numbers, Social Security numbers, financial data, and other regulated content. A governance plane that can extend beyond Microsoft-hosted workloads into multi-cloud environments. A compliance framework with auditability, policy traceability, and a familiar operational model for security and compliance teams. A way to apply data-aware controls to AI interactions, not just to storage locations. In practical terms, that means the same organization that already trusts Purview to govern Exchange, SharePoint, Teams, and Copilot can use Purview to govern prompts and responses in a Bedrock-based agent as well. The key architectural shift is this: your app does not need to invent its own data policy engine. It can call Purview at the points where risk exists. What this Bedrock agent demonstrates The sample solution in this blog is a cross-cloud AI pattern: The frontend is a single-page browser-based chat app. Users authenticate with Microsoft Entra ID via MSAL. The backend runs in AWS Lambda. The model is Amazon Bedrock using Nova 2 Lite. Microsoft Purview evaluates prompts and model responses for DLP policy violations. This matters because it proves a broader point: Microsoft Purview can govern AI interactions even when the model and compute are not running in Azure. The core architecture As shown above the end-to-end flow follows this pattern: A user signs in through Microsoft Entra ID from the frontend. The frontend sends the user's access token and prompt to an API endpoint in AWS. The Lambda function exchanges that token using the On-Behalf-Of flow so Purview can evaluate under the signed-in user's identity. Purview scans the full prompt for sensitive information before the model is called. If the prompt is allowed, the Lambda function sends the request to Amazon Bedrock. Purview scans the model response before it is returned to the user. The frontend shows the result along with a Purview evaluation badge. That gives you two strong governance controls: In-line data loss prevention enforcement, which can block risky requests before they ever reach the model. Response-time enforcement, which can stop sensitive data from being returned even if a model generates it. The implementation also uses the user's identity for policy evaluation. That is important because governance decisions should reflect who is asking, not just what application is running. Why this pattern is useful for security, governance, and compliance teams There are three reasons this pattern is worth paying attention to. First, it aligns policy with risk rather than with hosting location. The compute might run in Lambda and the model might be in Bedrock, but Purview still remains the policy decision point. Second, it improves operational clarity. Security teams do not have to learn a different governance toolchain for each AI stack. They can keep using Purview concepts, policy models, and audit workflows. Third, it supports real-world adoption. Most large enterprises are hybrid and multi-cloud already. A governance pattern that only works for one vendor's runtime is not enough. Policy definition in Purview Two polices are needed to enforce DLP-a collection policy for Enterprise AI Apps and a DLP policy Collection policy 2. DLP policy Follow the steps outlined here to create the DLP policy for Enterprise AI Apps. Sample provided: purview-api-samples/DLPforCustomAIApps at main · microsoft/purview-api-samples To replicate this scenario, follow this link to the official GitHub repo: purview-api-samples/AWSBedrock at main · microsoft/purview-api-samples Once deployed, you will have: An AWS Lambda function that calls Amazon Bedrock. A browser frontend that authenticates with Microsoft Entra ID. Microsoft Purview evaluating both prompts and responses. A demo flow where safe prompts succeed and sensitive prompts are blocked. With the App and agent deployed, now comes the moment when the architectural value becomes clear. The model runtime is AWS Bedrock, but the policy decision is still coming from Microsoft Purview. Below screenshot shows the prompt containing sensitive information being blocked based on the policy evaluation by Purview. Minimal code integration requirements using the SDK Below is the code needed to perform the integration between Purview and Bedrock to perform the in and outbound inspection of content destined to and from the Bedrock model. Results of Purview’s verdict presented to user in the App UI Review governance evidence in Purview Data Security Posture Management Summary The bigger story here is not just that Microsoft Purview can protect an Amazon Bedrock agent. It is that organizations can centralize data security, governance, and compliance policy even while their AI architecture becomes more distributed across multiple clouds. That is the operational win. Developers keep the freedom to choose the best runtime and model platform. Security and compliance teams keep a central policy engine they already understand and trust. AI applications can be multi-cloud, but your data protection model does not have to be fragmented. Additional resources Configure Microsoft Purview - purview-sdk | Microsoft Learn Microsoft Purview Developer Platform Documentation - purview-sdk | Microsoft LearnAnnouncing Windows Server vNext Preview Build 29595
Hello Windows Server Insiders! Today we are pleased to release a new build of the next Windows Server Long-Term Servicing Channel (LTSC) Preview that contains both the Desktop Experience and Server Core installation options for Datacenter and Standard editions and Azure Edition (for VM evaluation only). Branding remains Windows Server 2025 in this preview - when reporting issues please refer to Windows Server vNext preview. Build 29531 established a new Server preview baseline build. Please perform a clean install of Build 29531 (or later) using the installation media linked below. Please note: Upgrades from Windows Server vNext preview builds older than 29531 are not supported. We encourage all Windows Server vNext preview users to perform a clean install using 29531 or later to successfully upgrade to future Windows Server vNext preview builds. While upgrades from earlier Windows Server previews (Build 26525 and older) are not technically blocked by setup.exe, a number of known issues have been identified related to upgrades necessitating the establishment of a new baseline build for our Server vNext Preview Program. The new baseline build (29531) will not be Flighted due to upgrade issues. Flighting support resumed with preview build 29550 or later. What's New Quick Machine Recovery available in Windows Server vNext Insider Previews. Quick machine recovery (QMR) is now available for Server vNext Insiders to test. This feature enables the recovery of Windows Server devices when they encounter boot critical errors that prevent them from booting. QMR can automatically search for cloud‑based remediations to recover from widespread boot failures significantly reducing the burden on IT administrators when multiple devices are impacted. This supports the goals of the Windows Resiliency Initiative by enabling applicable fixes to be delivered through trusted Windows Update to restore affected devices, helping reduce downtime and minimize manual recovery efforts across enterprise environments. This feature is currently enabled in the latest Server vNext Insider builds for customers to experience test mode. A Group Policy option to enable or disable the feature will be introduced in upcoming builds to provide additional administrative control. To simulate the quick machine recovery experience, use the following commands from an elevated command prompt: 1. Enable test mode: reagentc.exe /SetRecoveryTestmode 2. Configure Windows to boot to Windows Recovery Environment on the next boot: reagentc.exe /BootToRe 3. Reboot your device. The system goes through autoremediation of a simulated crash safely and reboots back to Windows Server. 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Known Issues Server Core Upgrades and AppCompat FOD: Enabling AppCompat FOD after reinstall may fail due to legacy 3rd-party license compatibility issues on Server Core devices. Server Core users may be unable to install the latest AppCompat FOD after upgrading to build 29574. This appears to be limited to Server Core installations with 3rd-party application licenses that fail compatibility checks after upgrade. This will be addressed in a future build. Upgrading from older builds of Windows Server vNext previews (26525 or older) are not supported. Please perform a clean install of build 29531 or later. Users may experience failures when attempting to upgrade from earlier previews (build 26525 and older). VMs may fail to upgrade or start after upgrade from older preview builds impacting live migration and failover cluster scenarios. Download Windows Server Insider Preview (microsoft.com) Flighting: The label for this flight may incorrectly reference Windows 11. 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