Detect and auto-fix exposed DB tables with AI-guided RLS policies

Many engineering and security teams repeatedly discover accidentally exposed database tables—especially as applications move to hosted Postgres/MySQL and serverless databases—leading to data leaks, audit failures, and expensive incident response. This is a widespread operational problem: roughly 25 million software teams could be in scope for tooling that prevents these misconfigurations, a market we estimate at $12.0B (25M teams × $480/year), and it disproportionately impacts teams without dedicated DB security expertise or strong CI guarded by policy-as-code. You could build a service that continuously scans cloud DBs for exposure risks, generates context-aware Row-Level Security (RLS) policies and accompanying unit tests using LLMs, surfaces suggested changes as policy-as-code PRs, and optionally applies safe auto-fixes behind human-in-the-loop approvals and canary rollouts. Key product elements would be provider-native integrations (Neon, Supabase, PlanetScale, RDS), verifiable test suites and audit logs, and a conservative default of “suggest-and-PR” with opt-in auto-remediation to minimize production risk. This market is attractive now because cloud-native DB usage, policy-as-code adoption, and LLM-driven developer tooling are converging—reflected in a market score of 90/100 and revenue potential of 82/100—so buyers are primed for automation that reduces audit overhead and breach risk. Competition is medium: many vendors offer DB scanning or policy linting, but few provide end-to-end, test-backed RLS generation plus safe auto-fix and full auditability; strengths of this idea are clear ROI on reduced audit time and incident prevention, while the main challenges are avoiding false positives, ensuring fixes don’t break applications, and earning trust through strong testing, explainability, and provider integrations.