Codebase data leakage detection and prevention for enterprise software teams
A system that monitors LLM API usage and prevents inadvertent exposure of proprietary code, architecture, and internal documentation through third-party AI services.
The problem
Enterprise engineering teams unknowingly leak sensitive internal APIs, function signatures, database schemas, and architectural details to public LLMs (ChatGPT, Claude, etc.) when developers use them for debugging and coding assistance, creating IP and security risk with no visibility or control.
Who has it: Mid-market to large software companies (200–5,000 engineers) with proprietary codebases, regulated compliance requirements (healthcare, fintech, defense), and centralized security/engineering leadership.
Why now: LLM adoption by developers is accelerating; major breaches (Samsung, Apple, Dropbox engineers) exposed internal code; enterprises now require proof of data non-retention and egress controls; regulatory pressure (SOC2, FedRAMP) is tightening.
Where this came from
2 public sources behind this idea.
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