data protection

5508 Topics

Why does AD group need access to underlying dataset, but direct access user doesn't?
I have a personal MS List and a personal Power BI Service dashboard. All stored in my JohnDoe personal account. If I share the dashboard with JaneDoe, then she can view the dashboard without issue, even though she doesn't have access to the underlying MS list. But if I share the dashboard with a DivisionManagement Active Directory group, then a manager who is in that group gets an error message when opening the dashboard link: Permission required. You cannot see the content of this report because you do not have permissions to the underlying dataset. Please contact the dataset owner to request access. Why does an Active Directory group need access to the underlying dataset, but a direct access user doesn't?
Blue88
3 hours ago Place Service
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Power BI Desktop crashes at “Loading information protection” when opening any report
Problem description Power BI Desktop launches successfully, but it crashes whenever I try to open a report. This happens with existing reports as well as newly created reports. For example, I can launch Power BI Desktop, create a new report, close Power BI, and then try to open that newly created report. Power BI crashes when attempting to open it. The issue first occurred after installing Power BI Desktop version 2.158.1304.0. Today, I updated to version 2.159.883.0 (October 2026, 64-bit) to see whether the update would resolve the issue, but the problem persists. I have already uninstalled and reinstalled Power BI Desktop and deleted the Power BI Desktop application folder before reinstalling. I would appreciate help diagnosing the underlying cause rather than repeating cache-cleaning steps. System information Operating system: Windows 10, version 22H2, build 19045.5854 Power BI Desktop, current version: 2.159.883.0 (October 2026, 64-bit) Power BI Desktop, version when the issue first appeared: 2.158.1304.0 Microsoft Edge: Version 155.0.4283.60 (64-bit) Microsoft Edge WebView2 Runtime: It does not appear in Windows Installed Apps. I have not yet confirmed whether it is installed or properly registered. Troubleshooting performed Restarted the computer. Deleted the Power BI Desktop application folder before reinstalling. Uninstalled and reinstalled Power BI Desktop. Updated Power BI Desktop from version 2.158.1304.0 to 2.159.883.0; the issue persists. Checked Windows Installed Apps for Microsoft Edge WebView2 Runtime; it was not listed. Reviewed the Power BI trace log. Findings from the trace log The trace shows that Power BI reaches its information protection components, including PBI.InformationProtectionManager and PBI.MipClientFactory. It also shows that the Analysis Services process starts and the modeling engine connects successfully. The log contains handled SyncAccessException messages stating that Report.AnalysisServicesDatabase was accessed synchronously while its value was being set asynchronously. However, I could not identify a definitive fatal exception in the trace that explains the crash. Questions What could cause Power BI Desktop to launch normally but crash whenever any report is opened, including a newly created report? Could this be related to a regression introduced in version 2.158.1304.0, given that updating to 2.159.883.0 did not resolve it? Could a missing or incorrectly registered Microsoft Edge WebView2 Runtime cause this behavior, and how can I verify its installation? If WebView2 is installed correctly, which additional diagnostic logs or tests should I perform to identify the cause? I can provide the Power BI trace log and additional diagnostic information if needed. Thank you.
ccn_ecommerce
4 hours ago Place Desktop
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Right-sizing Fabric capacity with 11 models and 47 dataflows: where do you stop?
Hi all, I'd like to ask how you'd approach a Power BI and Fabric environment that has grown past what the capacity can comfortably handle, because I'm not sure we found the best way through it. The environment has been growing since 2021 and now has 11 complex semantic models (sales, production, job costing and finance) fed by 47 dataflows. Sources are an Odoo ERP hosted on AWS, Google Sheets and Google Drive, with ingestion through Power Automate. Over time the symptoms piled up: slow refreshes, heavy load on the server, and Fabric consumption that was higher than it should have been. On the governance side there were shared logins, weak role-based controls and unstructured workspaces. We started with an audit of every model, dataflow, governance setting and the Fabric usage. Then we consolidated fragmented models to cut redundancy, redesigned the dataflows, and moved to incremental refresh with query folding where possible. For governance we added RBAC, a Dev/Test/Prod workspace structure, sensitivity labels and lineage tracking, plus a certified semantic model and quarterly audits. After the optimization, we were able to bring capacity down from 64 CU to 16 CU, and in some cases 8 CU. What I'm curious about is how you decide where to stop. How do you know how far you can go on right-sizing before refresh windows or interactive queries start to suffer? Do you look at the Capacity Metrics app over a few weeks, or do you test by dropping the SKU and watching? And how much do you rely on smoothing and bursting? With 47 dataflows, would you keep them as dataflows, or move some of the heavier logic into a Lakehouse or Warehouse? And with sources like Google Sheets and an ERP, how do you confirm query folding is actually happening and not silently falling back? On model consolidation, did you go for fewer, bigger models or a central certified model with thin reports? What did that do to refresh times and to your DAX? Last one: for the Dev/Test/Prod setup, are you using deployment pipelines, or something else? Has anyone found a way to keep dataset and dataflow parameters from breaking between stages? Would love to hear how you've handled this.
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kiara-mehra35
2 days ago Place Service
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How are teams handling AI-driven automation around Power BI Embedded applications?
I’m exploring patterns for adding AI-driven automation around Power BI Embedded applications. For example: Power BI Embedded → user interaction → backend/API event → AI analysis → business action I’m particularly interested in how teams are handling: event-driven actions from embedded reports calling backend APIs securely using AI to summarize or interpret report context triggering workflows based on report interactions authentication with service principals or managed identities keeping the architecture maintainable in production Are there recommended patterns for combining Power BI Embedded, Fabric APIs, and AI services without putting too much logic into the client application?
codeautomation
2 days ago Place Developer
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Need to implement - 1 semantic data model - 2 reports (1 with rls and 1 without rls)
Hi, I have created a common semantic data model. On this data model we are creating 2 power bi reports. Report 1 - needs to have RLS. Report 2 - Should be without RLS. We initially thought we will built a common semantic data model and then tie Report 1 with an RLS excel file. But we are unable to filter rows from common semantic data model in Report 1. Now, we are thinking of putting RLS in common semantics data model, which makes Report 1 as expected, but then we are unable to bypass RLS in Report 2. Can anyone please help me with this issue? How can i design the data model and report to acheive my goal?
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Arti173
3 days ago Place Service
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Fabric Data Agent Access issue
I have created a fabric data agent using semantic model of a published report, It is working fine but I'm not able to share it with others I published the agent in M365 copilot and share with my teams of 100 people only 4 or 5 people can go to M365 copilot and search the agent and added the agent to their agent tab others are not even seeing the agent but if I share the agent link from M365 copilot then they see the agent but agent is not answering the questions but it's perfectly answering for me and other 4 to 5 people who see the agent when I surfed it said it said there are 2 level of access to get answer from the agent 1. agent level access 2. underlying data level access I have workspace contributor access for workspace, read, write, reshare permission for the agent. most of them share the same access but there are not able to see the only difference I found is I'm having premium per user license and most of other have pro license so I doubted it but internet says license is not an issue can anyone pls help me with this issue
gkl
3 days ago Place Service
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Moving finance Excel reporting to Power BI: how would you handle versioning and RLS?
Hi all, I’d like to ask how you’d handle a finance reporting setup where the source is still Excel, because I’m not sure we landed on the best design. The sources are multiple Excel files with budget, cost and revenue data, maintained separately for different regions. The problems were the usual ones: combining the files led to duplication and inconsistencies, several versions of the same file were circulating, and reconciling everything took a lot of manual effort. Budget vs. actuals and profitability trends were hard to track, and sensitive financial data was being shared without any role-based access. For the fix, we set up predefined formats, validation checks and version control on the Excel inputs, then used Power Query for the ETL, with dataflows handling integration and versions. The financial datasets are stored in SharePoint/Cloud behind role-based access, with the Data Gateway for connectivity. In Power BI we used hybrid data modeling with automated refreshes, row-level security, deployment pipelines and audit logs for governance. The reports cover variance analysis, KPIs and trend lines across regions. So here’s what I keep wondering. When the inputs are still Excel files owned by finance teams, how strictly do you enforce a template at the source? Do you reject files that fail validation, or clean them up downstream in Power Query? For version control, are you relying on SharePoint versioning, dataflow versions or something else? And how do you stop someone from still emailing around a “final_v3” file next to the governed report? On the model side, we went with hybrid modeling. How do you decide what to import and what to leave in DirectQuery or a composite model, especially for budget vs. actuals? And with RLS across regions, how do you manage the roles as the number of regions and stakeholders grows? Do you map roles through security groups, or maintain a mapping table in the model? Last one: deployment pipelines work fine for the reports, but has anyone found a clean way to handle dataflow and parameter changes between stages without breaking the gateway connections? Would love to hear how you’d approach it.
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kiara-mehra35
6 days ago Place Service
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Building AI-Powered Features Around Power BI Embedded Analytics
Hi everyone, I am exploring ways developers are extending Power BI solutions with AI-powered capabilities. Many applications today need more than dashboards — they need intelligent features that can help users understand data, generate insights, and automate decision-making processes. I am interested in understanding how developers are approaching this architecture. Some questions: - How are you integrating AI capabilities with Power BI Embedded applications? - Are you using Power BI APIs together with external AI services for generating insights? - What are the recommended approaches for allowing AI systems to query and understand Power BI datasets securely? - How do you handle permissions and data access when adding AI features on top of business intelligence platforms? Would love to hear about real-world implementations and best practices from developers working with Power BI integrations. Thanks!
codeautomation
6 days ago Place Developer
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Onelake Catalog Semantic Model - RLS Issue
Outline: My report is based on the OneLake Catalog semantic model. I published my report in an app and created an audience group specific to that report. My report includes enrollment information for all Rice University schools, and the ask is to apply RLS at the school level, so each school can view only its own enrollment. I have a Dim_School in my upstream, with a distinct of all the schools, and there are fact tables: one for course details and the other for program details, joined to this Dim_School. As a next step for applying RLS, steps followed: 1. I created a "User School Mapping" table, and assigned 2 users and their corresponding schools for testing User School Mapping = DATATABLE ( "User Email", STRING, "School", STRING, { {"****@rice.edu","Engineering and Computing" }, {"*****@rice.edu","Engineering and Computing" } } ) 2. Then created a School_Access_Role with DAX rule, so only the user having restriction will be restricted to the corresponding school, the rest should be able to view all the schools. This way I don't have to join the Dim_School and the User School Mapping VAR CurrentUser = LOWER(TRIM(USERPRINCIPALNAME())) VAR HasRestriction = CONTAINS( 'User School Mapping', 'User School Mapping'[User Email], CurrentUser ) RETURN IF ( NOT(HasRestriction), TRUE(), CONTAINS( 'User School Mapping', 'User School Mapping'[User Email], CurrentUser, 'User School Mapping'[School], Dim_School[SCHOOL] ) ) 3. Identified the Sematic model from the workspace, selected security, and added the users to it, then saved it. 4. Asked the user with RLS applied to test the report, but the user gets a message saying "It's loading, but nothing happens". What else could I have done here? Experts seeking your guidance, please advise.
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RajiKarthik
8 days ago Place Desktop
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AI-driven analytics over Power BI Semantic Models – architecture discussion
Hi Fabric community, I would like to share an architecture experience and get feedback from the community. We have implemented an AI-assisted analytics layer over Power BI Semantic Models. The solution allows business users to ask questions in natural language and perform analysis directly against Power BI models. The architecture includes: Semantic model metadata understanding Query planning DAX generation and execution Query validation and governance controls Our implementation uses a dedicated intelligence layer around Power BI rather than an MCP-based approach. With the recent interest in Power BI MCP and Fabric MCP, I would like to discuss: How do you see custom AI analytics layers compared with MCP-based integrations? What are the recommended architectural patterns for enterprise AI agents working with Power BI Semantic Models? I would be happy to share more details about our implementation experience.
Moitkarimi
9 days ago Place Desktop
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3likes
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