The Kalshi surveillance team is under scrutiny after the prediction market detected suspicious trades linked to a White House employee.
US regulators are investigating Gabriel Perez, a longtime teleprompter operator for President Donald Trump. He allegedly used advance knowledge of presidential speeches to place profitable trades.
Kalshi identified the activity, froze the account, and reported the case to the Commodity Futures Trading Commission. However, questions remain about how its surveillance system detects insiders and protects the sensitive information it collects.
Teleprompter Operator Faces Investigation
Perez allegedly traded on markets involving words and phrases that Trump might use during public speeches.
These “mention markets” allow users to predict whether a person will say a specific word or discuss a certain subject. Perez reportedly had early access to speech content because of his work at the White House.
According to reports, he placed trades connected to more than a dozen appearances. These included the State of the Union address and a speech at the World Economic Forum in Davos.
The trades may have generated more than $90,000 in profit. However, Kalshi froze the money after its monitoring tools detected unusual activity.
Perez has reportedly cooperated with the investigation. The White House also placed him on unpaid administrative leave.
Kalshi Reported the Activity to Regulators
Kalshi notified the CFTC after reviewing the suspicious transactions.
The federal regulator is now examining whether Perez traded with material non-public information. Discussions have reportedly included a possible settlement that could require him to surrender the profits.
Kalshi prohibits users from trading with confidential information. Its rules also restrict people who can directly influence the outcome of a market.
The company says its systems monitor activity around the clock. Nevertheless, it provides limited public information about the exact methods used to identify suspicious accounts.
Keeping those methods private may prevent traders from learning how to avoid detection. At the same time, the lack of detail makes it harder to evaluate the system’s accuracy.
Automated Tools Screen High-Risk Trades
The Kalshi surveillance team uses automated tools to review trading patterns and identify potential insiders.
During the first quarter of 2026, the company opened more than 150 insider trading investigations. It also claims to have blocked over 100 suspicious trades before they could proceed.
Furthermore, Kalshi referred at least 20 cases to law enforcement and completed five formal disciplinary actions.
The company has introduced a risk-scoring framework for new markets. Contracts with a greater risk of insider activity may receive stronger controls before trading begins.
Unusual timing, large positions, repeated success, and links to the subject of a market could all indicate suspicious behavior. However, Kalshi has not published a complete list of its detection signals.
Employment Checks Add Another Safeguard
Kalshi now requires some traders to disclose their employment details before entering high-risk markets.
The information may help the platform identify users with access to confidential knowledge. For example, a government employee could have advance knowledge of a policy announcement. Similarly, a company worker might know the outcome of an unpublished financial report.
Kalshi has also added tools that let users report suspicious activity from individual market pages. The surveillance team reviews those submissions alongside automated alerts.
In addition, the company works with external market-monitoring specialists. An independent advisory committee provides further oversight of its surveillance and enforcement programs.
Data Collection Creates Security Questions
Employment verification could strengthen Kalshi’s ability to prevent misconduct. However, collecting more personal information also creates additional security and privacy risks.
Employment records may reveal where a user works, what role they hold, and which sensitive events they could influence. Therefore, attackers could find this information valuable.
Kalshi must protect the data from unauthorized access, internal misuse, and accidental exposure. It must also limit access to employees who genuinely need the information.
There is no indication that Kalshi’s employment database has suffered a breach. Still, expanding surveillance creates a larger collection of sensitive user data that requires strong security controls.
The Kalshi surveillance team may play an important role in protecting prediction markets. Yet the platform must balance effective monitoring with transparency, privacy, and secure data handling.
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