How to Fix Database Issues in Your AI-Generated App (2026)

Your AI-built app works. Users can sign up, create records, see their data. Everything looks fine in development.

Then you launch. A user discovers they can see another user's data by changing a URL parameter. Someone deletes their account and their records stay in the database forever. Your page that lists 50 items fires 50 separate database queries instead of one, and the whole app crawls under load.

These are not edge cases. They are the default outcome of building with AI coding tools. According to Beesoul's audit data, roughly 70% of Lovable apps ship with Row Level Security disabled entirely. Damian Galarza found 69 vulnerabilities across just 15 AI-built apps. And a scan of 200+ vibe-coded sites on Reddit found an average security score of 52 out of 100.

The good news: most database problems in AI apps fall into a small number of categories, and each one has a clear fix. This guide covers the six most common failures and exactly what to do about them.

If you are working through a broader cleanup, this article is part of our complete guide to fixing AI-generated apps. For database-specific issues, you can also jump to our database issues fix page.

Why AI Tools Build Broken Databases

AI coding tools are optimized for one thing: making your app work in a demo. That means getting data on screen as fast as possible.

This creates a predictable set of shortcuts:

• Security policies get skipped. RLS adds complexity. The AI disables it so the query returns data without policy errors.
• Performance patterns get ignored. Indexes, eager loading, and query batching are invisible in a demo with 3 test records. The AI does not add them because they are not needed yet.
• Data integrity is an afterthought. Soft deletes, foreign key constraints, and cascade rules require thinking about what happens when things go wrong. AI tools think about what happens when things go right.
• Schema design stays flat. AI generates whatever schema makes the current prompt work. It does not plan for the next feature, multi-tenancy, or compliance requirements.

The result is a database that works perfectly for one user running one feature in development. It falls apart the moment real users with real data show up.

Tools like Cursor, Lovable, and Bolt all exhibit these patterns to varying degrees. The issue is not with any specific tool; it is with the nature of prompt-driven code generation applied to database architecture.

The 6 Most Common Database Failures in AI Apps

1. Disabled Row Level Security (RLS)

What it is: RLS controls which rows each user can access. Without it, any authenticated user can read, modify, or delete any row in your database.

How AI tools break it: They either disable RLS entirely or create overly permissive policies like USING (true) that allow all access. Beesoul reports this is present in roughly 70% of the Lovable apps they audit.

The risk: User A can see User B's private data. An attacker with a valid session can dump your entire database.

2. Missing Soft Deletes

What it is: Soft deletes mark records as deleted (with a deleted_at timestamp) instead of permanently removing them. This preserves data for compliance, undo functionality, and audit trails.

How AI tools break it: They use hard DELETE statements. When a user deletes something, the data is gone forever. No undo, no audit trail, no GDPR-compliant export.

The risk: A user accidentally deletes their project and you cannot recover it. You receive a data access request and the records no longer exist.

3. N+1 Query Explosions

What it is: Instead of fetching related data in a single query (or a small number of batched queries), the app fires one query per item in a list.

How AI tools break it: AI generates the simplest possible data fetching pattern. If you have a list of 50 orders and each order has a customer, the AI writes code that fetches each customer individually instead of joining them in one query.

The risk: Pages that load instantly with 5 records take 10+ seconds with 500 records. Your Supabase bill spikes. Users leave.

4. Unverified Webhooks

What it is: Webhooks from payment providers (Stripe, PayPal) need signature verification to confirm they actually came from the provider and were not spoofed by an attacker.

How AI tools break it: They set up the webhook endpoint and process the payload but skip signature verification. Anyone who knows your webhook URL can send fake payment confirmations.

The risk: An attacker sends a fake "payment successful" webhook and gets free access to your product. This is one of the most exploited vulnerabilities in AI-generated apps.

5. Missing Indexes

What it is: Database indexes speed up queries on specific columns. Without them, the database scans every row to answer a query.

How AI tools break it: AI never adds indexes. It creates the table and moves on. With 100 rows, this is invisible. With 100,000 rows, every filtered query becomes slow.

The risk: Your app gradually slows down as the database grows. By the time you notice, the fix requires a migration that may cause downtime.

6. No Multi-Tenant Isolation

What it is: If your app serves multiple organizations or workspaces, each tenant's data must be isolated so Team A cannot access Team B's records.

How AI tools break it: AI generates a single-tenant schema. All records share one table with no tenant filtering. Adding a team_id column after the fact requires migrating every table and updating every query.

The risk: Data leakage between customers. For B2B apps, this is often a deal-breaker during security reviews.

How to Diagnose Database Issues in Your AI App

Before you start fixing things, figure out what is actually broken. Here is a quick diagnostic process you can run in under 30 minutes.

Check RLS Status

In the Supabase dashboard, go to Database > Tables. Click each table and check if RLS is enabled. If the toggle is off, that table is wide open.

You can also run this SQL in the Supabase SQL editor:

SELECT tablename, rowsecurity
FROM pg_tables
WHERE schemaname = 'public';

Any row where rowsecurity is false needs immediate attention.

Check for N+1 Queries

Open your browser DevTools, go to the Network tab, and load a page that displays a list. Filter by rest or your Supabase URL. If you see many identical requests firing sequentially, you have an N+1 problem.

Check for Hard Deletes

Search your codebase for .delete() calls. If you find them operating on user data without a corresponding deleted_at column on that table, you have hard deletes.

Check Webhook Verification

Search for your webhook handler (usually at /api/webhooks/stripe or similar). Look for stripe.webhooks.constructEvent() or equivalent signature verification. If the handler just parses req.body directly, it is unverified.

Fixing Each Problem: Step by Step

Fix 1: Enable and Configure RLS

For every table in your Supabase project:

-- Enable RLS
ALTER TABLE your_table ENABLE ROW LEVEL SECURITY;

-- Add a policy for authenticated users to access their own rows
CREATE POLICY "Users access own data" ON your_table
FOR ALL USING (auth.uid() = user_id);

If your table does not have a user_id column, you need to add one and backfill it. For tables that should be publicly readable (like blog posts), use a specific SELECT policy:

CREATE POLICY "Public read" ON posts
FOR SELECT USING (true);

CREATE POLICY "Owner write" ON posts
FOR ALL USING (auth.uid() = author_id);

Fix 2: Add Soft Deletes

Add a deleted_at column to every table that stores user data:

ALTER TABLE your_table ADD COLUMN deleted_at TIMESTAMPTZ DEFAULT NULL;

Then update your application code to set deleted_at = NOW() instead of deleting rows. Update all queries to filter out deleted records:

SELECT * FROM your_table WHERE deleted_at IS NULL;

Fix 3: Eliminate N+1 Queries

Replace individual fetches with joins. In Supabase, use the select syntax to include related data:

// Before (N+1): fetches each customer separately
const orders = await supabase.from('orders').select('*')
for (const order of orders.data) {
  const customer = await supabase.from('customers').select('*').eq('id', order.customer_id)
}

// After (single query with join)
const orders = await supabase
  .from('orders')
  .select('*, customers(*)')

Fix 4: Verify Webhooks

For Stripe, add signature verification to your webhook handler:

const sig = req.headers['stripe-signature']
const event = stripe.webhooks.constructEvent(req.body, sig, process.env.STRIPE_WEBHOOK_SECRET)

Never process a webhook payload without verifying its signature first.

Fix 5: Add Indexes

Identify columns you frequently filter, sort, or join on:

CREATE INDEX idx_orders_user_id ON orders(user_id);
CREATE INDEX idx_orders_created_at ON orders(created_at);
CREATE INDEX idx_orders_status ON orders(status);

A good rule of thumb: any column that appears in a WHERE, ORDER BY, or JOIN clause should have an index.

Fix 6: Add Tenant Isolation

If you need multi-tenant support, add a team_id column to every tenant-scoped table and update your RLS policies:

ALTER TABLE your_table ADD COLUMN team_id UUID REFERENCES teams(id);

CREATE POLICY "Team members access" ON your_table
FOR ALL USING (
  team_id IN (
    SELECT team_id FROM team_members WHERE user_id = auth.uid()
  )
);

When to Call a Professional

You can handle most of the fixes above yourself, especially with Cursor helping you write the migration SQL. But some situations warrant professional help:

• You have production users and cannot afford downtime. Migrations on live databases require careful planning.
• Your schema needs a full redesign. If the AI-generated structure is fundamentally wrong (flat where it should be relational, single-tenant where it should be multi-tenant), a professional can design the migration path.
• You are preparing for a funding round. Technical due diligence will check your database security. An audit report from an agency like Beesoul or Varyence carries weight with investors.

Our agency directory lists verified teams that specialize in full-stack rescue for AI-built apps. Most offer a discovery call to scope the work before committing.

Prevention: Prompts That Produce Better Databases

The best fix is not needing one. When you prompt your AI tool to build database features, include explicit constraints:

For Cursor or Claude Code:

"Create a new table called projects with columns for name, description, user_id (references auth.users), created_at, updated_at, and deleted_at. Enable RLS. Add a policy so users can only access their own projects. Add an index on user_id and created_at."

For Lovable:

"When you create database tables, always enable Row Level Security, add a deleted_at column for soft deletes, and create indexes on foreign key columns and any column used in filters."

Being explicit about these requirements in your prompts prevents most of the issues covered in this article. For a deeper look at prompt strategies and code quality, see our vibe code audit guide.

FAQ

What is the single most important database fix for AI apps? Enable Row Level Security on every table. This one change prevents the most damaging class of vulnerability: unauthorized data access between users.

Do these issues affect all AI coding tools equally? The patterns are consistent across Cursor, Lovable, and Bolt, but the severity varies. Tools that use Supabase tend to have more RLS issues. Tools that generate backend APIs tend to have more webhook and query performance issues.

How long does it take to fix a typical AI app's database issues? For a small app (5-10 tables), expect 2-4 hours if you know what you are doing, or a full day if you are learning as you go. Larger apps with production data may take a week or more.

Will fixing these issues break my existing app? Enabling RLS will break any query that does not match a policy. Always test in a staging environment first. Soft deletes require updating all queries to filter deleted_at IS NULL, which is straightforward but touches many files.

Should I fix database issues before or after fixing authentication? Fix authentication first. RLS depends on auth.uid(), so if your auth is broken, your RLS policies will not work correctly either.

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Related

• How to Fix Your AI-Generated App (Complete Guide)
• Vibe Code Audit: Secure Your AI App Before Launch
• From AI MVP to Production: The Complete Transition Guide