The recent case involving a wager on the fate of Nicolas Maduro has brought a modern challenge to an old problem into focus. An individual allegedly used advance knowledge of a planned operation to place a highly profitable bet on the prediction market platform, Polymarket. What matters most here is what this reveals; that there is a growing class of platforms where being “right early” is financially rewarded, and where that advantage may come from information not yet public. 

From Forecasting to Incentive Frameworks  

Platforms like Polymarket and Kalshi are often described as forecasting tools – markets that aggregate information to predict future outcomes. Sports wagering platforms like DraftKings and FanDuel operate on a similar premise in a different domain. But, from a security and investigative perspective, these platforms represent something more consequential: they monetize information advantage. The better your information, and the earlier you act on it, the greater potential reward.   

A Familiar Pattern in a New Form 

This is not a new dynamic. In traditional financial markets, insider trading has long been a core enforcement priority.  Individuals with access to non-public information, such as earnings results, mergers, or regulatory actions, have historically attempted to profit by trading ahead of public disclosure.   

These activities are tightly regulated and enforced by entities such as the US Security and Exchange Commission and Financial Industry Regulatory Authority, which monitor trading activity, investigate anomalies, and prosecute violations. Over time, this has led to clear legal frameworks and well-defined consequences for the misuse of such information.   

Where Prediction Markets Diverge 

Prediction markets introduce a similar financial incentive structure, but without the same level of regulatory oversight. In the United States, on prediction platforms like Kalshi and Polymarket, bettors are technically trading futures contracts, and so for that reason, they operate under oversight from the Commodity Futures Trading Commission, providing a degree of structure and compliance. But other platforms, particularly those operating offshore or in decentralized environments, exist in a more ambiguous space. The result is a fundamental imbalance, where the incentive to act on insider knowledge exists, but the enforcement environment is still developing.   

Where Insight Becomes Risk 

So, this creates a new category of exposure, that includes: 

  • Employees with knowledge of internal events 
  • Individuals with access to operational and/or classified information 
  • Third parties observing signals not yet widely understood.   

All of which now have a mechanism to convert that knowledge into financial gain.  And unlike traditional insider trading where activity may be buried in complex financial transactions, these platforms often create visible, time-stamped, and traceable behavior tied directly to real-world outcomes. This is where insider risk comes into focus. What looks like success in predication markets on the surface may, in some cases, reflect uneven access to information behind it, which raises questions not just about what is happening, but who may already be in a position to act on it. 

From Alert to Intelligence 

Open-source intelligence (OSINT) helps investigators treat insider risk indicators not as one-off anomalies, but as part of a bigger picture. Drawing on how OSINT is used in more traditional insider risk scenarios, we know that context is critical, both for proactive risk management and for investigations once something has happened.  

In the case of predictive markets, OSINT can help connect what we see in the market to external context, digital footprints, and relationships that may point to access to privileged, non-public information. One of the key advantages is the external line of sight OSINT provides. Internal systems might tell you that something unusual has occurred, but they rarely explain why, and that “why” matters when considering ongoing risk and future mitigation. By incorporating publicly and commercially available information, investigators can validate internal findings, test whether there is anything more behind them, and determine whether an event is isolated or part of a broader pattern of access, influence or exploitation. 

For example, patterns of early or disproportionate confidence can be looked at alongside open-source context to assess whether a bet reflects market news or privileged awareness. Over time, if the same actors consistently align early with real-world outcomes, it becomes meaningful and shifts the focus of the event itself to the people behind the activity.  

From there, the investigation naturally deepens. OSINT can be used to layer behavioral and situational indicators, such as financial pressures, ideological positioning, external affiliations etc., to build a more complete picture of insider risk. The aim isn’t necessarily to prove intent, but to understand exposure and motivation well enough to support proportionate escalation and decision-making. 

Used well, OSINT doesn’t just help explain what has already happened. It also helps organizations understand what information may already be in play, who may hold it, and what that means for the decisions they need to make next. This approach reflects a broader shift in insider threat mitigation, where OSINT is used not only to detect anomalies, but to add context, validate findings and surface early indicators of risk exposure. Some of these practical applications are explored in more detail in OSINT Combine’s blog on insider threat mitigation. 

As with any insider risk activity, this needs to be done carefully. OSINT should be used proportionately, lawfully and within clear governance frameworks. The objective is not broad surveillance, but targeted, intelligence-led enrichment that supports defensible and accountable decision-making. 

A Shift in How Insider Risk Is Observed 

The broader implications in all of this are that prediction and wagering markets are not just reflections of future expectations, but they are increasingly indicators of unevenly distributed knowledge. For organizations managing insider risk, fraud and security threats, this introduces a whole new lens into not just what is happening, but who may already know that it is going to happen.   

Consider a scenario where a small number of accounts take highly confident positions on a geopolitical or corporate outcome hours, or even days, before it becomes public. In isolation, this may appear as informed speculation. However, when combined with open-source indicators such as professional affiliations, proximity to relevant organizations, or prior patterns of accurately timed positions this behavior may point to access to information not yet in the public domain. 

In this context, the value is not just in identifying the anomalous transaction, but in understanding the network, exposure and potential pathways through which that knowledge may have been obtained. 

Looking Ahead 

Prediction markets will continue to grow, and perhaps, so will their legitimacy as forecasting tools. But alongside that growth is an emerging reality, that wherever information creates a financial advantage, it will be used, and often before it is meant to be. In traditional markets, regulators like the SEC and FINRA evolved to address this risk over several decades. In prediction markets, that evolution is still underway.