Kalshi is asking some traders to give up a little privacy before they can bet on sensitive events. That is a small change with a big message: prediction markets are starting to look much more like regulated exchanges.
Kalshi wants to know where certain users work before it lets them trade on markets that could be vulnerable to insider information. That includes contracts tied to corporate performance, national security and geopolitical events, including markets connected to the Iran war. For a business built around turning future events into tradable contracts, this is not a minor compliance tweak. It is a sign that the prediction market boom is entering a more serious phase.
The company said on June 9 that it will require employment disclosures for some users who want to trade in markets it considers high risk. According to the Associated Press, Kalshi will score markets for manipulation risk and use that process to screen for users who may have access to material nonpublic information. If someone is flagged as a presumptive insider, the trade can be blocked before it goes through.
This is the kind of surveillance language Wall Street understands very well. It is also the kind of language prediction markets have often tried to avoid sounding like they needed. For years, the appeal of these platforms was simple: trade your view on elections, inflation, sports, court rulings or global events, and let prices tell the public what the crowd believes. That story works better when the crowd looks broad and independent. It gets much harder when the winning trader may be sitting inside the room where the answer is already known.
Kalshi is not an offshore message board with odds attached. It is a federally regulated U.S. prediction market platform, and that distinction matters. The company has tried to separate itself from crypto-adjacent competitors by leaning into know-your-customer rules, compliance staff and the idea that event contracts can become a legitimate financial product rather than a novelty wager.
That ambition comes with obligations. Traditional exchanges do not simply trust that traders are behaving well. They monitor order flow, compare trading behavior against sensitive announcements and refer suspicious cases to regulators when needed. Kalshi now appears to be building a similar structure for markets where the tradable information can come from a workplace, a campaign, a government office or a military assignment.
The company has already made more than 20 referrals to law enforcement or regulators for suspected manipulation or insider trading. It has also said the new employment checks have helped prevent more than 100 suspicious trades. Those numbers are still small compared with securities markets, but they matter because prediction markets are much younger and much more reputationally fragile. A handful of obvious insider cases can do more damage here than a much larger scandal would do in a mature market with decades of enforcement history.
The problem is not theoretical. Former Representative George Santos has reportedly drawn federal scrutiny over a Kalshi trade tied to whether he would attend the State of the Union. Kalshi has also disclosed action against political candidates who traded on their own races. In another case cited by AP, a U.S. Army soldier was charged with using classified information to profit from a Polymarket contract tied to U.S. military operations in Venezuela. These examples show why ordinary identity checks are no longer enough. Some information is valuable precisely because only a few people have it.
The privacy trade-off is getting harder
Prediction markets have always sold a particular kind of freedom. Users could trade on questions that traditional finance did not touch, from election outcomes to pop culture awards to global crises. That openness is part of the appeal. It is also what creates the tension Kalshi now has to manage.
As the markets attract more informed users, the line between smart research and unfair access becomes more important. A trader who studies earnings trends or public shipping data is adding information to the market. A trader who works at the company reporting results next week is doing something very different. The market may not care at first because the price becomes more accurate, but regulators and ordinary users certainly do.
Employment disclosure is a blunt but practical tool. It will not catch every relationship, and in most cases Kalshi says it will not verify employer information unless suspicious activity triggers a review. Still, it gives the company a way to identify obvious conflicts before trades settle. A defense contractor betting on a national security event, a company employee trading on earnings, or a campaign worker betting on an internal political development should not be treated as just another sharp trader.
There is a business reason for this as well. Kalshi wants more liquidity, more institutional respect and more room to expand into contracts that look increasingly financial. That cannot happen if regulators see the platform as a place where insiders quietly monetize privileged information. The more serious the markets become, the more they need rules that look familiar to the financial system they are trying to join.
That may disappoint users who liked the lighter touch. But it is also the price of legitimacy. If prediction markets want to be treated as real markets, they have to accept the boring machinery that keeps real markets usable. Kalshi’s employment disclosure rule is not the end of that process. It is probably the beginning of the part where the industry stops asking to be believed and starts proving it can police itself.
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