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We've already seen real cases where private conversations with language models were indexed by search engines, where proprietary company information showed up in responses to other organizations, and where source code generated by AI carried licensing conflicts or quietly introduced security vulnerabilities.
When you send sensitive data to an external, API-based AI model, you are extending trust far beyond your security perimeter, beyond your audit controls, beyond your data governance policies, and often beyond your ability to verify what actually happens to that data.
From a compliance perspective, that's not innovation. That's exposure.
By David William SilvaWe've already seen real cases where private conversations with language models were indexed by search engines, where proprietary company information showed up in responses to other organizations, and where source code generated by AI carried licensing conflicts or quietly introduced security vulnerabilities.
When you send sensitive data to an external, API-based AI model, you are extending trust far beyond your security perimeter, beyond your audit controls, beyond your data governance policies, and often beyond your ability to verify what actually happens to that data.
From a compliance perspective, that's not innovation. That's exposure.