Governed vs. Public AI: Why Banks Are Bringing Models In-House
Regulated finance is moving sensitive AI work onto private, governed infrastructure. Here's a simple way to decide which data goes where.
Same model, different data, completely different risk.
📰 THE NEWS
Banks are increasingly running large language models in private environments (on-premises, private cloud or in-country sovereign cloud) so prompts and customer data never reach a shared public endpoint. Gartner forecasts sovereign cloud infrastructure spending of $80bn in 2026, up 35.6%, with regulated industries a key driver. Go.AI, which sells private AI to banks, raised an $85m Series A in September. In India, the RBI's FREE-AI framework (August 2025) and its June 2026 draft model-risk guidance treat AI as models that need an inventory, validation and version control.
🔄 THE PIVOT
The story isn't "public AI is banned". It's tiering: public tools for public data, governed enterprise AI for internal data, private deployment for regulated customer data. A model that's fine in one tier is a breach in another.
✅ ACTIONABLE TIP: a 3-tier AI data policy for finance
1. GREEN (public or anonymised data such as market research and policy drafting): approved public tools.
2. AMBER (internal financials, forecasts, contracts): enterprise plans with no-training terms, SSO, logging and admin controls.
3. RED (customer PII, payroll, inside information): private or on-prem deployment only, with model versioning and validation.
Then publish a one-page "which tool for which data" guide. Shadow AI grows wherever the policy is unclear.
💬 Does your finance team have a written rule on which data can go into which AI tool?
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Sources: Fintech Futures (citing Gartner); AY Automate; RDP GPU Mart on RBI FREE-AI and draft MRM guidance.