Last month, a popular AI chat application exposed over 300 million messages tied to roughly 25 million users. What stands out is not just the scale of the leak but the nature of the data itself, which included full conversations, timestamps, model selections, and in many cases deeply personal or sensitive content that users clearly assumed would remain private.

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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. Traditional DLP was not designed for conversational AI.

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