How Supabase RLS makes secure embedded dashboards deployable in one day

All Posts

TL;DR: If your app uses Supabase Auth with RLS, you can leverage that setup to embed dashboards securely with minimal effort using Upsolve AI. Exposing user-specific data in a dashboard is straightforward in theory but notoriously difficult in practice.

Serguei Balanovich

Co-Founder & CTO, Upsolve AI

Nov 14, 2025

10 min

TL;DR: If your app uses Supabase Auth with RLS, you can leverage that setup to embed dashboards securely with minimal effort using Upsolve AI.

Exposing user-specific data in a dashboard is straightforward in theory but notoriously difficult in practice. The challenge isn’t just building the UI or running queries—it’s making sure every user sees only their own data.

The real insight here? If you’ve already configured Row-Level Security (RLS) in your database, you’ve built the foundation for secure multi-tenancy. Dashboards don’t need their own bespoke access controls—they can reuse what you’ve already built.

Why RLS matters

Security at the database layer is one of the most robust ways to enforce access controls. Unlike middleware filters or application-level checks, RLS policies are enforced directly by the database, ensuring that every query respects the security rules, regardless of how or where it’s executed.

For example, with Supabase:

  • Policies can be scoped to users, roles, or even specific data attributes.

  • Queries are implicitly filtered at the source, eliminating the risk of accidental data exposure at the application layer.

Once RLS is configured, the database becomes the source of truth for data security. This is critical when exposing sensitive data, like in customer-facing dashboards, where the consequences of misconfiguration can be severe.

The Insight: Dashboards as an extension of RLS

If you’re using Supabase Auth and have RLS in place, embedding a secure dashboard doesn’t require extra effort to rebuild access controls. You can simply extend your existing policies to your dashboard.

Here’s how it works with Upsolve AI:

  1. Use supabase.auth.getSession() to retrieve the session token from Supabase.

  2. Pass the token to the <UpsolveDashboard /> React component as a dbAuthToken={} prop.

That’s it. Your dashboard queries the database directly and respects the same RLS policies already in place for your app. There’s no duplication of logic, and the database continues to handle access control.

Why this is cool

This approach combines simplicity with security. By reusing RLS, you avoid the overhead of building a separate security layer for dashboards. And because RLS is enforced at the database level, you don’t have to worry about middleware bugs or client-side missteps exposing the wrong data.

We’ve seen this enable teams to go from concept to production with embedded dashboards in under a day—not because they cut corners, but because they could rely on the database to enforce security.

If you’re working with Supabase Auth and RLS and want to experiment with this setup, we’re currently testing it with more teams. Feel free to share thoughts here or email us at team@upsolve.ai.

Key Takeaways

  • Hire once: Add an employee in Payroll and they’re synced to Time automatically.

  • A named manager, clear escalation paths with time commitments.

  • Reconcile faster: Payment deposits and fees auto‑post to your GL.

  • Hire once: Add an employee in Payroll and they’re synced to Time automatically.

  • A named manager, clear escalation paths with time commitments.

  • Reconcile faster: Payment deposits and fees auto‑post to your GL.

Pros

  • Hire once: Add an employee in Payroll and they’re synced to Time automatically.

  • A named manager, clear escalation paths with time commitments.

  • Reconcile faster: Payment deposits and fees auto‑post to your GL.

Cons

  • Hire once: Add an employee in Payroll and they’re synced to Time automatically.

  • A named manager, clear escalation paths with time commitments.

  • Reconcile faster: Payment deposits and fees auto‑post to your GL.

Try Upsolve for Embedded Dashboards & AI Insights

Embed dashboards and AI insights directly into your product, with no heavy engineering required.

Fast setup

Built for SaaS products

30‑day free trial

See Upsolve in Action

Launch customizable dashboards and AI‑powered insights inside your app, fast and with minimal engineering effort. No code.

Share this post

Latest Articles

a machine that is sitting in a room

Ka Ling Wu

Dec 5, 2025

How to QA an agent when the ground truth changes daily

The Testing Problem Nobody Prepared You For: Software QA is built on a simple premise: correct behavior is stable. You write a test, it passes, and if the test fails tomorrow, you know something broke. This doesn't work for data analytics agents.

Read More

a machine that is sitting in a room

Ka Ling Wu

Dec 5, 2025

How to QA an agent when the ground truth changes daily

The Testing Problem Nobody Prepared You For: Software QA is built on a simple premise: correct behavior is stable. You write a test, it passes, and if the test fails tomorrow, you know something broke. This doesn't work for data analytics agents.

Read More

grayscale photo of binoculars on the ground

Ka Ling Wu

Dec 1, 2025

The agent development stack nobody talks about: observable tools, not just observable agents

Why Your Agent Observability Stack Is Incomplete: Every AI engineering team knows they need observability. They instrument their LLM calls, track token usage, log prompts and completions.

Read More

grayscale photo of binoculars on the ground

Ka Ling Wu

Dec 1, 2025

The agent development stack nobody talks about: observable tools, not just observable agents

Why Your Agent Observability Stack Is Incomplete: Every AI engineering team knows they need observability. They instrument their LLM calls, track token usage, log prompts and completions.

Read More

A wooden block spelling data on a table

Ka Ling Wu

Nov 28, 2025

Why Git-Style versioning breaks for data analytics agents

The Problem Everyone Underestimates: When engineering teams build their first AI agent, they typically think it's a harder version of building a microservice. Add some LLM calls, implement retry logic, maybe throw in RAG, ship it. They're wrong, but they don't know it yet.

Read More

A wooden block spelling data on a table

Ka Ling Wu

Nov 28, 2025

Why Git-Style versioning breaks for data analytics agents

The Problem Everyone Underestimates: When engineering teams build their first AI agent, they typically think it's a harder version of building a microservice. Add some LLM calls, implement retry logic, maybe throw in RAG, ship it. They're wrong, but they don't know it yet.

Read More

Rigid data no more.

Upsolve let's your customers "chat to their data" without leaving your platform. Quicker clarity for your users, better engagement for you.

Start Here

Subscribe to our newsletter

By signing up, you agree to receive awesome emails and updates.

Rigid data no more.

Upsolve let's your customers "chat to their data" without leaving your platform. Quicker clarity for your users, better engagement for you.

Start Here

Subscribe to our newsletter

By signing up, you agree to receive awesome emails and updates.

Rigid data no more.

Upsolve let's your customers "chat to their data" without leaving your platform. Quicker clarity for your users, better engagement for you.

Start Here

Subscribe to our newsletter

By signing up, you agree to receive awesome emails and updates.