Enterprise security teams have spent more than a decade refining data loss prevention. We built policies around file movement. We tuned alerts around email exfiltration. We monitored endpoints, USB devices, cloud storage, and outbound gateways. The assumption was consistent. Sensitive data moves in recognizable ways, through defined channels, and can be inspected before it leaves the organization.

Large language models break that assumption.

The conversation around enterprise AI risk tends to focus on outputs. Hallucinations. Bias. Toxicity. Model safety. These are real issues, especially in customer facing systems. But inside the enterprise, the more immediate risk sits on the input side. What employees are feeding into these systems matters far more than what the model generates in return.

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