When building your own AI-powered software product handling client financial data, how do you ensure data privacy and security?

The approach included bringing on a group of trusted contractors with software/data backgrounds once commercialization was planned, rather than doing all engineering personally. Real testing is in place using Supabase and Row Level Security (RLS) as a major component. The system is read-only and cannot originate transfers or send/receive money — a deliberate 'choice editing' approach to remove the possibility of major issues like wire fraud, which is common in real estate. GitHub Actions are used for a testing suite, and Cloudflare monitoring and proxy servers are part of the enterprise-level security stack. The product was built with a conscious effort to go well beyond 'vibe coding' and function as a real full-stack software product given the sensitivity of financial data and the trust required to get users to switch products.