Unified Catalog Self-Serve Analytics - Data products and other elements do not sync
Dears, I intend to create a custom interface through a PowerBI report in Fabric to distribute Purview Unified Catalog browser. I use the feature "Unified Catalog Self-Serve Analytics" to deliver the Unified Data Catalog content in a Fabric Lakehouse. However, from the 44 data products created, only 22 are delivered to the lakehouse, in the data product table I have tried in different lakehouse, same result. I would love some advice to help me configure this properly. Do you face the same issue ? Best, Antoine
How to cancel a Norton subscription Allow Uniqueness of Glossary Terms across Governance Domains
When glossary terms are created and published, there is no check for the same term name in another governance domain. Some organizations do want to enforce term uniqueness across all domains. Would it be feasible to provide an optional switch in Unified Catalog settings to turn this on?
Cross workspace lineage for Fabric Lakehouse tables in Purview
I’m currently exploring lineage capture in Microsoft Purview for Fabric Lakehouse tables that are spread across multiple workspaces, following medallion architecture (Bronze, Silver, Gold in separate Fabric workspaces). While reviewing the documentation, I noticed the stated limitation around cross-workspace lineage for non-power BI assets, as mentioned here: https://learn.microsoft.com/en-us/purview/data-map-lineage-fabric Is there any update or workaround planned to support cross-workspace Fabric lineage in Purview? Is this limitation on the product roadmap or actively being worked on? Until native support is available, are there any recommended design patterns to handle lineage in this scenario?
Data Quality Error (Internal Service Error)
I am facing an issue while running the DQ scan, when i tried doing manual scan and scheduled scans both time i faced Internal Service Error. ( DataQualityInternalError Internal service error occurred .Please retry or contact Microsoft support ) Data Profiling is running successfully but for none of the asset, DQ is working. After the lineage patch which MS had fixed, they had introduced Custom SQL option to create a rule, and after that only i am facing this issue. Is anyone else also facing the same? I tried with different data sources (ADLS, and Synapse) its same for both. If anyone has an idea, do share it here, it will be helpful.
C# MIP SDK v1.17.x - AccessViolationException on creation of MIPContext in 64-bit console app
I first logged this on https://stackoverflow.com/questions/79746967/accessviolationexception-when-creating-mipcontext-after-upgrade-to-v1-17 and the responses there have indicated I should raise with Microsoft a a likely bug, but I don't see a clear route to reporting other than here so any response would be appreciated, even if just to direct me to the appropriate reporting location. I've built a simple console app that demonstrates this issue that I'm happy to provide but we're seeing an issue with the 1.17.x version of the C# MIP SDK where an AccessViolationException is being thrown when trying to create an MIP context object. This is for a .Net Framework 4.8 console app built in 64-bit configuration, deployed to a Windows Server 2016 with the latest VC++ redistributable (14.44.35211) installed (both x86 and x64 versions), though we've seen the same on Windows Server 2019 and 2022. When the same app is built in 32-bit and deployed to the same environment the exception doesn't occur. The following code is what I've used to repro the issue: MIP.Initialize(MipComponent.File); var appInfo = new ApplicationInfo { ApplicationId = string.Empty, ApplicationName = string.Empty, ApplicationVersion = string.Empty }; var diagnosticConfiguration = new DiagnosticConfiguration { IsMinimalTelemetryEnabled = true }; var mipConfiguration = new MipConfiguration(appInfo, "mip_data", LogLevel.Info, false, CacheStorageType.InMemory) { DiagnosticOverride = diagnosticConfiguration }; //Expect BadInputException here due to empty properties of appInfo //When built as part of a 64-bit console app this causes AccessViolationException instead MIP.CreateMipContext(mipConfiguration); The AccessViolationException crashes the console app, with the following logged in the Windows Event Log: Framework Version: v4.0.30319 Description: The process was terminated due to an unhandled exception. Exception Info: System.AccessViolationException at Microsoft.InformationProtection.Internal.SdkWrapperPINVOKE.MipContext_Create__SWIG_1(System.Runtime.InteropServices.HandleRef) at Microsoft.InformationProtection.Internal.MipContext.Create(Microsoft.InformationProtection.Internal.MipConfiguration) at Microsoft.InformationProtection.Utils.MIPHelper.CreateMipContext(Microsoft.InformationProtection.MipConfiguration) The issue doesn't occur with the latest 1.16 version (1.16.149) of the SDK but does appear to be in all versions of the 1.17 release. Library: C# MIP SDK v1.17.x Target App: .Net Framework 4.8 console app Deployed OS: Windows Server 2016, 2019 and 2022 (With .Net Framework 4.8 and latest VC++ redist installed)AI Security Ideogram: Practical Controls and Accelerated Response with Microsoft
Overview As organizations scale generative AI, two motions must advance in lockstep: hardening the AI stack (“Security for AI”) and using AI to supercharge SecOps (“AI for Security”). This post is a practical map—covering assets, common attacks, scope, solutions, SKUs, and ownership—to help you ship AI safely and investigate faster. Why both motions matter, at the same time Security for AI (hereafter ‘ Secure AI’ ) guards prompts, models, apps, data, identities, keys, and networks; it adds governance and monitoring around GenAI workloads (including indirect prompt injection from retrieved documents and tools). Agents add complexity because one prompt can trigger multiple actions, increasing the blast radius if not constrained. AI for Security uses Security Copilot with Defender XDR, Microsoft Sentinel, Purview, Entra, and threat intelligence to summarize incidents, generate KQL, correlate signals, and recommend fixtures and betterments. Promptbooks make automations easier, while plugins provide the opportunity to use out of the box as well as custom integrations. SKU: Security Compute Units (SCU). Responsibility: Shared (customer uses; Microsoft operates). The intent of this blog is to cover Secure AI stack and approaches through matrices and mind map. This blog is not intended to cover AI for Security in detail. For AI for Security, refer Microsoft Security Copilot. The Secure AI stack at a glance At a high level, the controls align to the following three layers: AI Usage (SaaS Copilots & prompts) — Purview sensitivity labels/DLP for Copilot and Zero Trust access hardening prevent oversharing and inadvertent data leakage when users interact with GenAI. AI Application (GenAI apps, tools, connectors) — Azure AI Content Safety (Prompt Shields, cross prompt injection detection), policy mediation via API Management, and Defender for Cloud’s AI alerts reduce jailbreaks, XPIA/UPIA, and tool based exfiltration. This layer also includes GenAI agents. AI Platform & Model (foundation models, data, MLOps) — Private Link, Key Vault/Managed HSM, RBAC controlled workspaces and registries (Azure AI Foundry/AML), GitHub Advanced Security, and platform guardrails (Firewall/WAF/DDoS) harden data paths and the software supply chain end-to-end. Let’s understand the potential attacks, vulnerabilities and threats at each layer in more detail: 1) Prompt/Model protection (jailbreak, UPIA/system prompt override, leakage) Scope: GenAI applications (LLM, apps, data) → Azure AI Content Safety (Prompt Shields, content filters), grounded-ness detection, safety evaluations in Azure AI Foundry, and Defender for Cloud AI threat protection. Responsibility: Shared (Customer/Microsoft). SKU: Content Safety & Azure OpenAI consumption; Defender for Cloud – AI Threat Protection. 2) Cross-prompt Injection (XPIA) via documents & tools Strict allow-lists for tools/connectors, Content Safety XPIA detection, API Management policies, and Defender for Cloud contextual alerts reduce indirect prompt injection and data exfiltration. Responsibility: Customer (config) & Microsoft (platform signals). SKU: Content Safety, API Management, Defender for Cloud – AI Threat Protection. 3) Sensitive data loss prevention for Copilots (M365) Use Microsoft Purview (sensitivity labels, auto-labeling, DLP for Copilot) with enterprise data protection and Zero Trust access hardening to prevent PII/IP exfiltration via prompts or Graph grounding. Responsibility: Customer. SKU: M365 E5 Compliance (Purview), Copilot for Microsoft 365. 4) Identity & access for AI services Entra Conditional Access (MFA/device), ID Protection, PIM, managed identities, role based access to Azure AI Foundry/AML, and access reviews mitigate over privilege, token replay, and unauthorized finetuning. Responsibility: Customer. SKU: Entra ID P2. 5) Secrets & keys Protect against key leakage and secrets in code using Azure Key Vault/Managed HSM, rotation policies, Defender for DevOps and GitHub Advanced Security secret scanning. Responsibility: Customer. SKU: Key Vault (Std/Premium), Defender for Cloud – Defender for DevOps, GitHub Advanced Security. 6) Network isolation & egress control Use Private Link for Azure OpenAI and data stores, Azure Firewall Premium (TLS inspection, FQDN allow-lists), WAF, and DDoS Protection to prevent endpoint enumeration, SSRF via plugins, and exfiltration. Responsibility: Customer. SKU: Private Link, Firewall Premium, WAF, DDoS Protection. 7) Training data pipeline hardening Combine Purview classification/lineage, private storage endpoints & encryption, human-in-the-loop review, dataset validation, and safety evaluations pre/post finetuning. Responsibility: Customer. SKU: Purview (E5 Compliance / Purview), Azure Storage (consumption). 8) Model registry & artifacts Use Azure AI Foundry/AML workspaces with RBAC, approval gates, versioning, private registries, and signed inferencing images to prevent tampering and unauthorized promotion. Responsibility: Customer. SKU: AML; Azure AI Foundry (consumption). 9) Supply chain & CI/CD for AI apps GitHub Advanced Security (CodeQL, Dependabot, secret scanning), Defender for DevOps, branch protection, environment approvals, and policy-as-code guardrails protect pipelines and prompt flows. Responsibility: Customer. SKU: GitHub Advanced Security; Defender for Cloud – Defender for DevOps. 10) Governance & risk management Microsoft Purview AI Hub, Compliance Manager assessments, Purview DSPM for AI, usage discovery and policy enforcement govern “shadow AI” and ensure compliant data use. Responsibility: Customer. SKU: Purview (E5 Compliance/addons); Compliance Manager. 11) Monitoring, detection & incident Defender for Cloud ingests Content Safety signals for AI alerts; Defender XDR and Microsoft Sentinel consolidate incidents and enable KQL hunting and automation. Responsibility: Shared. SKU: Defender for Cloud; Sentinel (consumption); Defender XDR (E5/E5 Security). 12) Existing landing zone baseline Adopt Azure Landing Zones with AI-ready design, Microsoft Cloud Security Benchmark policies, Azure Policy guardrails, and platform automation. Responsibility: Customer (with Microsoft guidance). SKU: Guidance + Azure Policy (included); Defender for Cloud CSPM. Mapping attacks to controls This heatmap ties common attack themes (prompt injection, cross-prompt injection, sensitive data loss, identity & keys, network egress, training data, registries, supply chain, governance, monitoring, and landing zone) to the primary Microsoft controls you’ll deploy. Use it to drive backlog prioritization. Quick decision table (assets → attacks → scope → solution) Use this as a guide during design reviews and backlog planning. The rows below are a condensed extract of the broader map in your workbook. Asset Class Possible Attack Scope Solution Data Sensitive info disclosure / Risky AI usage Microsoft AI Purview DSPM for AI; Purview DSPM for AI + IRM Unknown interactions for enterprise AI apps Microsoft AI Purview DSPM for AI Unethical behavior in AI apps Microsoft AI Purview DSPM for AI + Comms Compliance Sensitive info disclosure / Risky AI usage Non-Microsoft AI Purview DSPM for AI + IRM Unknown interactions for enterprise AI apps Non-Microsoft AI Purview DSPM for AI Unethical behavior in AI apps Non-Microsoft AI Purview DSPM for AI + Comms Compliance Models (MaaS) Supply-chain attacks (ML registry / DevOps of AI) OpenAI LLM OOTB built-in; Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time Secure registries/workspaces compromise OpenAI LLM OOTB built-in Secure models running inside containers OpenAI LLM OOTB built-in Training data poisoning OpenAI LLM OOTB built-in Model theft OpenAI LLM OOTB built-in Prompt injection (XPIA) OpenAI LLM OOTB built-in; Azure AI Foundry – Content Safety / Prompt Shield Crescendo OpenAI LLM OOTB built-in Jailbreak OpenAI LLM OOTB built-in Supply-chain attacks (ML registry / DevOps of AI) Non-OpenAI LLM Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time Secure registries/workspaces compromise Non-OpenAI LLM Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time Secure models running inside containers Non-OpenAI LLM Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time Training data poisoning Non-OpenAI LLM Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time Model theft Non-OpenAI LLM Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time Prompt injection (XPIA) Non-OpenAI LLM Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time Crescendo Non-OpenAI LLM Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time Jailbreak Non-OpenAI LLM Azure AI Foundry – Content Safety / Prompt Shield; Defender for AI – Run-time GenAI Applications (SaaS) Jailbreak Microsoft Copilot SaaS OOTB built-in Prompt injection (XPIA) Microsoft Copilot SaaS OOTB built-in Wallet abuse Microsoft Copilot SaaS OOTB built-in Credential theft Microsoft Copilot SaaS OOTB built-in Data leak / exfiltration Microsoft Copilot SaaS OOTB built-in Insecure plugin design Microsoft Copilot SaaS Responsibility: Provider/Creator Example 1: Microsoft plugin: responsibility to secure lies with Microsoft Example 2: 3rd party custom plugin: responsibility to secure lies with the 3rd party provider. Example 3: customer-created plugin: responsibility to secure lies with the plugin creator. Shadow AI Microsoft Copilot SaaS or non-Microsoft SaaS gen AI APPS: Purview DSPM for AI (endpoints where browser extension is installed) + Defender for Cloud Apps AGENTS: Entra agent ID (preview) + Purview DSPM for AI Jailbreak Non-Microsoft GenAI SaaS SaaS provider Prompt injection (XPIA) Non-Microsoft GenAI SaaS SaaS provider Wallet abuse Non-Microsoft GenAI SaaS SaaS provider Credential theft Non-Microsoft GenAI SaaS SaaS provider Data leak / exfiltration Non-Microsoft GenAI SaaS Purview DSPM for AI Insecure plugin design Non-Microsoft GenAI SaaS SaaS provider Shadow AI Microsoft Copilot SaaS or non-Microsoft SaaS GenAI APPS: Purview DSPM for AI (endpoints where browser extension is installed) + Defender for Cloud Apps AGENTS: Entra agent ID (preview) + Purview DSPM for AI Agents (Memory) Memory injection Microsoft PaaS (Azure AI Foundry) agents Defender for AI – Run-time* Memory exfiltration Microsoft PaaS (Azure AI Foundry) agents Defender for AI – Run-time* Memory injection Microsoft Copilot Studio agents Defender for AI – Run-time* Memory exfiltration Microsoft Copilot Studio agents Defender for AI – Run-time* Memory injection Non-Microsoft PaaS agents Defender for AI – Run-time* Memory exfiltration Non-Microsoft PaaS agents Defender for AI – Run-time* Identity Tool misuse / Privilege escalation Enterprise Entra for AI / Entra Agent ID – GSA Gateway Token theft & replay attacks Enterprise Entra for AI / Entra Agent ID – GSA Gateway Agent sprawl & orphaned agents Enterprise Entra for AI / Entra Agent ID – GSA Gateway AI agent autonomy Enterprise Entra for AI / Entra Agent ID – GSA Gateway Credential exposure Enterprise Entra for AI / Entra Agent ID – GSA Gateway PaaS General AI platform attacks Azure AI Foundry (Private Preview) Defender for AI General AI platform attacks Amazon Bedrock Defender for AI* (AI-SPM GA, Workload protection is on roadmap) General AI platform attacks Google Vertex AI Defender for AI* (AI-SPM GA, Workload protection is on roadmap) Network / Protocols (MCP) Protocol-level exploits (unspecified) Custom / Enterprise Defender for AI * *roadmap OOTB = Out of the box (built-in) This table consolidates the mind map into a concise reference showing each asset class, the threats/attacks, whether they are scoped to Microsoft or non-Microsoft ecosystems, and the recommended solutions mentioned in the diagram. Here is a mind map corresponding to the table above, for easier visualization: Mind map as of 30 Sep 2025 (to be updated in case there are technology enhancements or changes by Microsoft) OWASP-style risks in SaaS & custom GenAI apps—what’s covered Your map calls out seven high frequency risks in LLM apps (e.g., jailbreaks, cross prompt injection, wallet abuse, credential theft, data exfiltration, insecure plugin design, and shadow LLM apps/plugins). For Security Copilot (SaaS), mitigations are built-in/OOTB; for non-Microsoft AI apps, pair Azure AI Foundry (Content Safety, Prompt Shields) with Defender for AI (runtime), AISPM via MDCSPM (build-time), and Defender for Cloud Apps to govern unsanctioned use. What to deploy first (a pragmatic order of operations) Land the platform: Existing landing zone with Private Link to models/data, Azure Policy guardrails, and Defender for Cloud CSPM. Lock down identity & secrets: Entra Conditional Access/PIM and Key Vault + secret scanning in code and pipelines. Protect usage: Purview labels/DLP for Copilot; Content Safety shields and XPIA detection for custom apps; APIM policy mediation. Govern & monitor: Purview AI Hub and Compliance Manager assessments; Defender for Cloud AI alerts into Defender XDR/Sentinel with KQL hunting & playbooks. Scale SecOps with AI: Light up Copilot for Security across XDR/Sentinel workflows and Threat Intelligence/EASM. The below table shows the different AI Apps and the respective pricing SKU. There exists a calculator to estimate costs for your different AI Apps, Pricing - Microsoft Purview | Microsoft Azure. Contact your respective Microsoft Account teams to understand the mapping of the above SKUs to dollar value. Conclusion: Microsoft’s two-pronged strategy—Security for AI and AI for Security—empowers organizations to safely scale generative AI while strengthening incident response and governance across the stack. By deploying layered controls and leveraging integrated solutions, enterprises can confidently innovate with AI while minimizing risk and ensuring compliance.
Java MIP SDK 1.17.154: commitAsync() TemplateNotFoundError (C# OK; Java fails Win & Ubuntu)
TL;DR Java SDK 1.17.154: calling setLabel() then commitAsync() fails with TemplateNotFoundError (TemplateId=2ea3c830-...). Same label/code works on Java 1.16.x and C# 1.17.154. Policy cache cleared, templates/labels verified, token/tenant checked—issue persists. Environment SDK (Java): 1.16.x (OK), 1.17.154 (FAIL) SDK (C#): 1.17.154 (OK) OS (Java): Windows 10/11 (win32 build), Ubuntu 20.04 / 22.04 / 24.04 Java: OpenJDK 17.0.16 (x64) Service/Tenant: Microsoft Purview Information Protection Auth: (e.g., user delegated token / app-only token) Code Snippet (Java) // Label apply options LabelingOptions labelingOptions = new LabelingOptions(); labelingOptions.setAssignmentMethod(AssignmentMethod.PRIVILEGED); labelingOptions.setDowngradeJustified(true); labelingOptions.setJustificationMessage("Label Apply"); // Get label Label label = fileEngine.getLabelById(labelId); // Apply label (no explicit template handling) fileHandler.setLabel(label, labelingOptions, new ProtectionSettings()); // Commit File workFile = new File(domainFolder, UidUtil.makeUid()); CompletableFuture<Boolean> commitFuture = fileHandler.commitAsync(workFile.getAbsolutePath()); commitFuture.get(); // <-- Throws TemplateNotFoundError on 1.17.154 Stack trace excerpt: Caused by: com.microsoft.informationprotection.internal.gen.Error: TemplateNotFoundError: Could not find template with id: 2ea3c830-5a0e-4eea-b48b-c72186d453c0, BadInputError.Code=General, CorrelationId=42ffaad4-3a0f-4986-ba9d-b5a79c5fd076 (ProtectionEngine), CorrelationId=16819f70-e419-473f-9895-c756f3dd5e4b (FileHandler) at com.microsoft.informationprotection.internal.gen.SdkWrapperJNI.SwigDirector_FileHandler_Observer_OnCommitFailure(SdkWrapperJNI.java:2688) Expected Behavior setLabel() should apply the label (and its protection) and commit successfully, as it does in Java 1.16 and C# 1.17.154. Actual Behavior commitAsync() fails with TemplateNotFoundError for the GUID referenced by the label’s ApplyProtectionAction. What I’ve Tried Policy/cache refresh: Deleted %LOCALAPPDATA%\Microsoft\MSIP\ / ~/.mip/, reloaded engine. Template/label verification: Confirmed existence and publish scope in Purview portal & via PowerShell/Graph. Label actions check: policyEngine.getLabelActions(labelId) shows an ApplyProtectionAction with that GUID. Token/tenant sanity check: Correct scopes and same tenant. Rollback test: Java 1.16 works; C# 1.17.154 works. Questions Any breaking change in Java 1.17 regarding how protection templates are resolved during setLabel()? Is this a known issue specific to Java SDK 1.17.154 (win32 & Ubuntu 20/22/24 builds)? Should we now explicitly use ProtectionDescriptor / SetProtection() in Java? Can someone review the service logs using the CorrelationIds above? Happy to provide additional logs, PowerShell/Graph queries, or action dumps if needed. Thanks!Fabric Warehouse is not extracting metadata like tables and its columns in Purview
Hello Team, Good Evening..! We noticed that the metadata in Datawarehouse is not extracting Schema(Tab) which contains Tables and its columns We scanned Lakehouse using Service prinicple where metadata extraction is done as expected but it is not the same in Data Warehouse after the scan completes. Are there any other options to get that metadata extracted under warehouse ? Please guide us the best possible methods to handle this situation. Regards, Naresh Inaganti
