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Today we’re launching groups and access controls in Basedash. Basedash is a BI tool the whole company touches — so who can see what really matters. Now you control it precisely.

Create groups for the way your company actually works, grant each group access to exactly what it needs, and give every group its own AI context so the assistant behaves differently for different people.

Why we built it

Access and governance are the difference between a BI tool a few analysts use and one the whole company — plus the people outside it — can be trusted with. The moment you connect real data and point an AI analyst at it, the question stops being “can we answer this?” and becomes “who should be allowed to ask?”

Groups and access controls answer that question. You decide who is in each group, what each group can reach, and how the AI talks to them.

Group your users

Bundle people into groups that match how you work — your data team, growth, leadership, or an external client you share dashboards with. Each group is a single place to manage everything those users can do.

The Groups page in Basedash settings, listing groups like Data team, Growth, and a new External stakeholders group

Add a person to a group and they inherit its access immediately. Remove them and it’s revoked just as fast.

Grant access to anything

This is the core of it: a group’s access covers everything in Basedash, not just dashboards. Toggle on exactly what a group should reach and leave the rest off.

The access editor for a group, with toggles for data sources, dashboards, MCP servers, automations, and chats

You can scope a group to specific:

  • Data sources — choose which databases and warehouses a group can query
  • Dashboards — share the right reports, and only those
  • Chats — control who joins which conversations
  • Automations — decide who receives each scheduled report
  • MCP servers — grant tool access on a per-group basis

So your external stakeholders can see their own client dashboard and nothing else, while your data team keeps full access to everything.

Steer the AI per group

Permissions decide what a group can reach. AI context decides how the assistant talks to them. Every group gets its own context that’s applied to every answer automatically.

A group's AI context instructions on the left, and the assistant answering scoped to the client's own data on the right

Tell the assistant something like “you’re talking to an external client, only reference their own account data, and keep explanations non-technical,” and every response for that group follows it — on top of the row-level security that already applies to every question. Same product, different behavior depending on who’s asking.

Who gets what

Because groups map to real teams and audiences, access falls out naturally — each group gets exactly the slice of Basedash it needs.

Six example groups and the access each one gets, from Data team to external Clients

How it works

Group, grant, steer — the three steps of access controls in Basedash

It’s three steps: group your users, grant the access each group needs, and steer how the AI answers for them. Set it once and it applies everywhere — chats, dashboards, automations, and the MCP server alike.

Getting started

Groups and access controls are available today for all Basedash users:

  1. Sign up for Basedash (or log in) and connect your data sources
  2. Open Settings → Groups and create a group for a team or audience
  3. Grant the group access to the data sources, dashboards, chats, and automations it needs
  4. Add an AI context so the assistant answers the way that group should hear it

What’s next

A BI tool is only as useful as it is trusted. Now the right people see the right data — and the AI knows who it’s talking to.

Try groups and access controls today — control exactly who can access your data.

Written by

Max Musing avatar

Max Musing

Founder and CEO of Basedash

Max Musing is the founder and CEO of Basedash, an AI-native business intelligence platform designed to help teams explore analytics and build dashboards without writing SQL. His work focuses on applying large language models to structured data systems, improving query reliability, and building governed analytics workflows for production environments.

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