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Building & Product

How do I keep customer data safe in an AI-built internal tool?

The single biggest failure in AI-built tools is a database with no row-level security: one scan found 170 of 1,645 Lovable apps leaking names, emails, and financial records because the AI generated the tables but never configured access rules. Turn on RLS for every Supabase table, keep API keys server-side (never in the browser), and put the tool behind a real login even if it is 'just internal'. Then run one adversarial pass, asking the AI itself or a scanner to attack what it built, before any customer data goes in.

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18 resources.

More in Databases & Internal Tools

Why use AI to build databases and internal dashboards instead of buying SaaS? 18 → How do founders build CRMs, trackers, and admin panels with AI tools? 20 → What's the fastest path from spreadsheet chaos to a working internal tool? 18 → Which AI app builder should I use for internal tools: Lovable, Bolt, Replit, v0, Retool, or Glide? 18 → What database should sit under my AI-built internal tool: Supabase, Airtable, Postgres, or a spreadsheet? 18 → How can my team ask questions of our data in plain English instead of writing SQL? 18 → How do founders automate data entry and keep tools in sync using AI agents (n8n, Zapier, Make)? 18 → When does an AI-built internal tool break down, and when should I bring in a professional developer? 18 → What does building and running internal tools with AI actually cost compared to SaaS subscriptions? 18 → How do I get a live dashboard of my business metrics with AI instead of wrestling with BI tools? 18 →

Also in Starting Up

The same ground, over in Build the product, our Starting Up track.

What actually counts as an MVP, and what's the smallest one I can get away with? 20 → Which no-code tool should I use to build my first product? 20 →
eChai Partner Brands The partnership →