As fund managers deploy AI tools capable of analyzing nonpublic information at unprecedented scale, they face growing uncertainty about how existing insider trading and material nonpublic information (MNPI) rules apply when AI systems, rather than human analysts, process restricted data. Regulatory risk may arise not only from actual misuse of MNPI but also from inadequate controls over AI systems that could foreseeably allow restricted information to influence trading decisions or recommendations. In this guest article, Skadden partners Daniel Michael and Andrea Griswold examine 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. See “Benchmarking Fund Managers’ Adoption and Governance of Generative AI” (Nov. 19, 2025).