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FinTech

A Consumer Finance Platform Gave Every Team AI Access to Enterprise Data - With Zero Credentials Stored on Any End-User Machine.

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OVERVIEW

When Every Team Wants AI Access to Company Data, But Security Can't Allow It.

A high-growth consumer finance and wealth management platform had a problem most data teams don't talk about publicly: the only way to give AI assistants access to enterprise systems was to hand out live credentials to individual laptops. No central control. No audit trail. No safe path forward.

The data team knew what needed to happen. The architecture made it structurally impossible. What followed was a ground-up engineering effort to solve a problem that most organizations either avoid or work around badly - giving every team governed, natural-language access to enterprise data, without a single credential ever leaving the data team's control.

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CHALLENGES

Three Gaps That Made Governed AI Access Architecturally Blocked

The platform's existing infrastructure had no path for AI clients to reach enterprise systems without expanding the attack surface. Every option involved a trade-off between access and security - until it didn't.

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No governed path to enterprise systems meant the only option was distributing individual OAuth credentials to laptops, with no central way to revoke them.

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AI tools had unrestricted reach: destructive operations, sensitive data, and no policy boundary limiting what they could invoke.

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The Snowflake semantic layer had no version control - AI could return confident, well-formed answers that were simply wrong, with no way to catch them before they reached business users.

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No audit trail existed for AI-initiated data access. Nothing logged. Nothing traceable.