From Patterns to Proof: The SEC’s New Playbook for Insider Trading Enforcement The SEC’s recent action charging 21 individuals in a wide‑ranging insider trading scheme marks an important inflection point in market abuse enforcement—both in scale and in method. While grounded in a familiar misappropriation theory, the case reflects a more fundamental shift: the Commission is now building scienter‑based antifraud cases through data analytics, pattern recognition, and network‑level investigation, rather than relying on isolated transactions or discrete tips. At its core, the case follows a recognizable model. A corporate attorney allegedly accessed confidential M&A information and transmitted it through a network of intermediaries and traders who executed transactions and, in many instances, shared profits through structured kickback arrangements. The conduct unfolded over multiple years, spanned more than a dozen transactions, and extended across a global web of participants. What distinguishes the case, however, is its construction. Rather than focusing on a single communication or trade, the SEC has assembled a cohesive narrative of repeated and coordinated conduct: the same actors trading ahead of multiple corporate events, recurring relationships among participants, and financial arrangements that reinforce deliberate collaboration. In this framework, the case is not about isolated misconduct—it is about a connected system of behavior revealed through data. From Transactional Cases to Network Enforcement This action exemplifies a broader evolution in SEC enforcement. Commission staff, including experts focus almost exclusively on insider trading, are increasingly able to identify anomalous trading activity across issuers, accounts, and time horizons, leveraging sophisticated data analytics and surveillance capabilities. Patterns that may appear fragmented at the participant level—trading in different securities, across accounts, or through intermediaries—can now be reconstructed into a coherent narrative through a combination of analytical techniques. Event‑driven analysis allows regulators to identify trading activity consistently occurring ahead of market‑moving developments, while network mapping