How an AI agent can convert Supabase policy context into an RLS review brief with scenarios, risks, and validation checks
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Supabase context, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent convert Supabase policy context into an RLS review brief with scenarios, risks, and validation checks?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
What this agent helps you do
A Supabase row level security review agent focuses on policy behavior and user scenarios. It helps teams identify risky access assumptions, missing tests, and changes that require careful approval.
When to use this workflow
Use it before launching new roles, changing table access, debugging authorization bugs, or reviewing customer-impacting data flows.
How Supabase gives the agent context
Connect Supabase and specify the project, tables, policies, roles, and user scenarios. Ask the agent to prefer read-only checks and mark policy changes for approval.
Example starter prompt
Review Supabase RLS policies for this feature. Summarize intended access, risky scenarios, validation checks, missing context, and approval-ready policy changes.Suggested workflow steps
The agent maps policies to scenarios, identifies gaps, prepares safe validation checks, and documents assumptions.
Inventory the Supabase tables, enabled RLS state, policies, commands, roles, authenticated claims, helper functions, and relevant views. Write both allowed and denied scenarios for anonymous users, record owners, collaborators, service roles, and any organization boundary in scope.
Test operations with representative fixtures and the actual query shape. A policy that protects a single-row lookup may still behave differently for inserts, updates, deletes, or collection queries. Never test with production credentials or expose another user’s rows.
Questions this workflow answers
Do our data-access policies allow every intended user action and block the cross-account cases they should?
The agent converts policy code into a scenario matrix. It records each table, whether RLS is enabled, command, role, using and with check behavior, helper functions, views, and required claims. Scenarios name the actor, tenant or organization boundary, record ownership, operation, and expected result. Anonymous, owner, collaborator, administrator, and service paths remain distinct.
Both allowed and denied tests matter. An owner may be able to select one row but fail an update because the new value violates a check. A collaborator may read a collection yet see another organization’s record through a view or helper function. Inserts, updates, deletes, single-row reads, and filtered collections use the actual application query shape with representative test fixtures.
The agent flags policies that depend on missing claims, recursive helpers, broad roles, or assumptions the client can alter. It does not “test” production with real user credentials or use a service role as evidence that end-user access works. Any fixture that represents another account is synthetic and isolated.
The review package includes policy summaries, scenario results, gaps, safe reproduction steps, and proposed migration or application changes. Each proposal explains which allowed or denied case it changes and the regression tests needed. Security and engineering owners approve policy updates and deployment; no policy is disabled or rewritten during analysis.
Expected handoff
The output should include a policy summary, scenario table, risks, and validation checklist. Pair with GitHub when app code or migrations are involved.
The scenario table should include anonymous users, ordinary members, owners or admins, cross-organization users, deleted memberships, and service operations where relevant. For each select, insert, update, and delete path, the agent can state the row shape, session claims, expected result, and policy clause that decides it. A policy that permits the happy path may still leak rows through a join or block updates because the new row no longer satisfies a check. Testing allowed and denied cases together shows whether access boundaries survive realistic data changes.