How to Limit Trading Risk When AI Accesses Nonpublic Info

As financial firms adopt large language models and other AI tools, a practical question arises: How should firms control AI tool access to nonpublic information to mitigate the risk of a trading, surveillance or enforcement problem? Skadden partners Daniel Michael, Andrea Griswold and Eben Colby discuss how existing insider trading principles apply to AI tools, where the mosaic theory may leave room for nonpublic data, and practical controls for AI access to material nonpublic information.