Fund managers are increasingly deploying large language models and other artificial intelligence tools capable of accessing and analyzing nonpublic information at unprecedented scale, raising pressing questions about how existing insider trading and material nonpublic information handling rules apply when the entity processing restricted data is not a human analyst but an AI model operating across vast datasets. Fund managers are increasingly deploying large language models and other artificial intelligence tools capable of accessing and analyzing nonpublic information at unprecedented scale, raising pressing questions about how existing insider trading and material nonpublic information handling rules apply when the entity processing restricted data is not a human analyst but an AI model operating across vast datasets. Skadden partners Daniel Michael and Andrea Griswold discuss how the existing securities law framework applies to AI tools that access nonpublic information, identify regulatory risks beyond traditional insider trading claims and offer practical guidance on controls fund managers can implement now — before AI-specific regulatory developments arrive.
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