I received my degree from West Virginia University, where I majored in international studies and focused on national security and intelligence.

I also earned a degree in geography because I was fascinated by geographic information systems, particularly their application to the fields of national security and intelligence.

One of my favorite topics was, and still is open-source intelligence or OSINT.


I usually used various methods of open-source intelligence (OSINT) for my assignments, and I often had to find, sort through, and analyze large amounts of data.

I was loved two projects in the national security and Intelligence courses; one project involved mapping out the social connections between the coaching staff of various college teams at that university and when and where they met at previous university teams for example, coach Huggins’ assistant coaches were all people he had worked with earlier in his career.

The other project involved using a clustering tool on Facebook to make a social network map of our friends list. It made nodes, clusters, and connections based on where you knew people from. For me, that included high school, Marshall University, WVU, the people I went to basic training with, and people in my National Guard unit.

Clustering is a powerful technique that makes it possible to find patterns in a few data points, such as when, where and with whom you were with.

This was in 2009, however, many of those tools have been replaced. The methods, however, remain the same; simply updated versions of old ones. There are many new data-gathering tools and vast quantities of information available on social media networks.

OSINT is useful in more ways than simply finding out information on individuals or groups. Web scraping tools, statistical analysis, and other research methods can be used to gather and understand public sentiment toward something, such as a stock that’s going up or crashing.

It can be used to track down individuals who are in distress during a disaster, or to study people’s reactions to an event in order to determine the number of people involved, and the types of hashtags they are using so that riots can be foreseen.

Several years ago I worked with one of my friends who was finishing up her PhD in Criminology and presenting a paper on the temporal aspects of violent crime.

I scraped data from Google to create coordinates for ATMs, liquor stores, and bars within Dallas city limits. The data was then analyzed in ArcGIS using Python. After cross-referencing Dallas Police Department records with were able to make an analysis about which blocks of time were the most likely for crimes to occur and what types of property were most likely be crime scenes.

This paper was presented at a criminology conference in 2019 by my partner. The findings and conclusions drawn were formulated using data that was open-source, which is freely available to the public.

OSINT tools and processes which have been developed to increase the speed of data collection and analysis can be harnessed by organizations, regardless of size.

These tools allow organizations to maximize their available resources, provide detection capabilities warning of threats, and proactively monitor information for the purpose of early detection, alerting and prevention of security incidents.

Further more, automation of the OSINT process can streamline information analysis for organizations, and the methods and applications of open-source intelligence are nearly limitless.

I plan on writing more on OSINT in future posts.