Organizations face growing challenges protecting sensitive data because they often lack visibility into how information moves across endpoints, cloud applications, browsers, and generative AI tools. Valuable assets such as source code, customer records, and intellectual property are constantly transferred and transformed, increasing the risk of insider threats, data theft, and accidental exposure. Traditional Data Loss Prevention (DLP) solutions frequently rely on static rules and pattern matching, generating large numbers of false positives while missing real security incidents. As a result, data breaches are often discovered too late to prevent damage. Cyberhaven and Torq address this challenge by combining deep data visibility with automated security workflows. Cyberhaven tracks sensiti

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