Fund managers are increasingly deploying large language models and other artificial intelligence (AI) tools capable of accessing and analyzing nonpublic information at unprecedented scale. This trend raises pressing questions about how existing insider trading and material nonpublic information (MNPI) handling rules apply when the entity processing restricted data is not a human analyst but an AI model operating across vast datasets. The stakes are significant. Broker-dealers and investment advisers must maintain policies and procedures reasonably designed to prevent misuse of MNPI, and managers risk regulatory scrutiny if AI tools foreseeably could use restricted data improperly – even absent actual trades. The question is not whether a machine can commit insider trading but whether people and managers have designed AI systems that foreseeably allow MNPI to influence trading or recommendations. This guest article by Skadden partners Daniel Michael and Andrea Griswold examines how the existing securities law framework applies to AI tools that access nonpublic information, identifies regulatory risks beyond traditional insider trading claims and offers practical guidance on controls fund managers can implement now – before AI-specific regulatory developments arrive. For more on other new insider trading issues, see “Managing Prediction Market Insider Trading Risks” (Aug. 13, 2026); and “Insider Trading Enforcement Moves Beyond Equities” (Jul. 16, 2026).