Background: Intelligence, Surveillance, and Reconnaissance (ISR) operations are essential in collecting critical information in an effort to enhance and accelerate continuous mission planning. In particular, ISR collections can provide discernment into our adversary’s behavioural patterns, defensive and offensive posture, and regional threats. Nevertheless, the assessment and decision-making process for continuous mission planning remains in the hands of the human operators. Understanding how human operators consider the trade-offs between asset reallocation and continuous mission planning based on real-world collection is an unexplored area within the intelligence community. Therefore, the objective of this study was to determine if human operator risk profiles correlate to intel gain/loss (IGL) assessments. Methods: The study consisted of fifty participants (32 male and 18 female) with an average age of 30.4 (SD = 6.4). During the first phase of the study, participants were provided with 10 geographical maps and requested to select the optimal route based on risk versus reward. Each map had three routes which conveyed distance, number of stops, risk level, and reward factor. In addition, the map was color coded (green, yellow, red) to represent the crime index for a particular region. Green represented a low crime index rate, yellow represented a medium crime index rate, and red represented a high crime index rate. Lastly, each route had an associated probability of success with respect to risk level. The safest rate (low risk) had a probability of success at eighty percent, the moderate route (medium risk) had a probability of success at sixty percent, and the most dangerous route (high risk) had a probability of success at forty percent. Therefore, if the route the participant selected successfully traveled the path, the participant would be rewarded with the monetary value displayed under the map. However, if the route the participant selected was unsuccessful in traveling the route, they would receive no monetary value and their accumulated value would decrease by ten. Participants were unaware of the probability of success but following each of their selections, they were informed if they were successful or unsuccessful in the route they selected. The data from the first phase of the study was categorized with respect to low, medium, and high-risk selection by participant to develop a risk profile. As a result, a sequential ranking of risk was developed which discovered that twenty-three out of fifty participants had a low-medium risk profile (i.e., more low and medium risk routes) and twenty-seven out of fifty participants had a medium-high risk profile (i.e., more medium and high-risk routes). Next, participants were provided with an additional 10 geographical maps and requested to select the optimal route based on risk versus reward. However, during this set of maps, each participant traveled the same path and were instructed to make a dynamic decision mid-route between two paths. Again, the map was color coded (green, yellow, red) to represent the crime index for a particular region. The participants were not informed on the success rate for the selected path. The objective in this phase of the task was to assess the risk profile developed from the first phased and determine if a correlation exists between risk profile and dynamic IGL assessments. Results: The findings indicate that there was a statistically significant difference detected with respect to risk profile and dynamic IGL assessment (p < 0.01). One-hundred and fifty-seven out of two-hundred and thirty dynamic IGL routes selected were safer with less reward when participants displayed a low risk profile (68% selected low IGL routes whereas 32% selected high IGL routes). Whereas one-hundred and thirty-nine out of two-hundred and seventy dynamic IGL routes selected were safter with less reward when participants displayed a high-risk profile (51% selected low IGL routes whereas 49% selected high IGL routes). Moreover, it was discovered that as risk profiles increased from low-risk (i.e., risk adverse) to high-risk (i.e., risk-takers), there was a continual decline in safe dynamic IGL selected routes. Conclusion: With human operators supporting mission planning efforts, it’s imperative we understand how information is assessed and prioritized. Developing an profile of operators’ tendencies towards risk taking can enhance our understanding of their decision-making processes. As a result, strategies can be better informed by understanding risk taking propensity, allowing strategies to be tailored to align with operator’s risk profile. For example, a high-risk profile operator may respond better to aggressive strategies with high opportunity while a low-risk operator may prefer prioritizing safety and stability over opportunity gain. In this study, we developed a risk profile across fifty participants based on low, medium, and high-risk path selection. The profile was then correlated to dynamic IGL assessments. Findings provide new evidence that risk profiles could be used as an indicator to assess human operators’ decision-making during IGL assessments. Future research should be conducted to determine if the findings can be replicated across other domains.