Power Bi Share Dashboard
To use the power bi share dashboard feature, add user emails and set view permissions. You can also publish a workspace app to distribute reports to a whole team. Most data teams start by granting broad workspace roles to speed up report delivery. When roles are too broad, users gain read rights to every underlying dataset and table. How the power bi share dashboard workflow operates Sharing a report starts inside your cloud workspace where datasets and visuals live. Admins assign workspace r
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To use the power bi share dashboard feature, add user emails and set view permissions. You can also publish a workspace app to distribute reports to a whole team. Most data teams start by granting broad workspace roles to speed up report delivery. When roles are too broad, users gain read rights to every underlying dataset and table.
How the power bi share dashboard workflow operates
Sharing a report starts inside your cloud workspace where datasets and visuals live. Admins assign workspace roles that let builders edit reports or let viewers read them. When you share a dashboard with a colleague, the platform checks two distinct layers. First, it checks if the user has permission to open the visual layout. Second, it checks if the user has read rights on the underlying semantic model.
Data teams manage two main data modes when they set up these shared views. In import mode, the reporting tool pulls a copy of the data on a schedule. Users who open the dashboard read from that cached copy inside the reporting service. In direct query mode, the tool sends live SQL queries straight to your production database. Every filter change or page click triggers a new query against the database engine.
To connect the reporting service to a database, teams often store a shared database login. The reporting service uses that single account to run queries for every dashboard viewer. Teams can also add row-level rules inside the semantic model to filter results per user. If a viewer gets build rights on a dataset, they can query tables outside the visual.
What native sharing solves and what it leaves open
Native workspace permissions do a good job of sorting report builders from report viewers. They let admins group dashboards into apps and control who opens each link. Row-level rules also help teams filter rows inside a single dataset based on user identity. These controls solve the problem of organizing reports inside the analytics portal.
Still, native sharing controls only decide who can open a report or dataset. They do not inspect the actual SQL statements that run against your production database. When a dashboard uses a shared service account, the database sees only one generic user. Your database audit log cannot tell which person clicked the report that ran a heavy query.
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This gap also affects how sensitive columns flow from your database into shared reports. If a table holds emails, phone numbers, card numbers, or national IDs, queries pull them in full. Someone who duplicates a dataset or builds a new visual can expose those raw fields. Static workspace roles cannot mask sensitive fields in flight or block a bad query.
Why database-level controls matter for shared analytics
Relying only on report-layer permissions forces teams to maintain rules in two separate places. Data engineers must build views in the database and rebuild filter logic inside each report. When a team member shares a new dashboard, they might skip those manual filter steps. Moving access checks and data masking closer to the database removes that risk.
When controls sit at the database layer, every query follows the same security rules. It does not matter if the query comes from a shared dashboard or a SQL client. Masking rules can rewrite emails, phone numbers, card numbers, and national IDs in query results. Audit logs can record every statement alongside the identity of the person or service running it.
Govern statements and responses across databases and APIs
Good config and scoped roles are needed setup. They do not check single queries in flight or mask sensitive fields in returned rows. Across Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes APIs, SSH, and internal HTTP endpoints, hoop.dev runs beside each supported service as an open-source proxy, a sidecar for your database or API.
First, hoop.dev guardrails block banned queries before they run. Then hoop.dev data masking rewrites matched sensitive values in responses in memory. The audit trail records every statement and verdict per resource. Furthermore, hoop.dev holds SOC 2 Type II and is open source on GitHub.
Frequently asked questions
Why can a user see the dashboard link but get a blank error screen?
The user has permission on the visual layer but lacks read rights on the dataset. Check the dataset permissions and make sure the data source credentials are still active.
Does sharing a dashboard let viewers query the database directly?
View-only links restrict users to the visuals on the screen. Still, granting build permissions on the dataset lets users write new reports against those tables.
How can we stop shared dashboards from exposing sensitive customer columns?
Apply dynamic data masking at the database query layer before results reach the reporting tool. This rewrites matched fields like emails or card numbers in results without breaking the dashboard.