– Executive Summary –
Law enforcement agencies have the responsibility of using every tool available to them to protect and serve their communities and citizens. Intelligence to proactively prevent violent crimes is a crucial resource that must be effectively optimized in order to accomplish this task. Open-source intelligence (OSINT) is heavily relied upon by law enforcement agencies around the nation to provide accurate, real-time information that can aid officers in preventing violent crime. Although law enforcement has been using this intelligence for centuries, the rapid development and expansion of the internet and social media platforms have increased the amount of information and reliance on open-source intelligence.
This thesis researches the background of open-source intelligence (OSINT) and the current use of OSINT by law enforcement and conducts a case study of OSINT use prior to and during the initial Ukraine invasion by Russian military forces. The use of open-source intelligence during the Ukraine invasion in February 2022 yielded unprecedented beneficial results in preparation against Russian forces. Although this invasion is an international military conflict, it was selected as a case study because it demonstrates the effective use of evolving modern-day social media open-source intelligence. The mass amounts of data revolving around the conflict were analyzed in detail and proactive defensive maneuvers may be attributed to this intelligence.
The history of the United States government using open-source intelligence in war can be traced back to WWI. Traditional sources included newspapers, magazines, and print media, which were used to counter enemy military tactics during various U.S. conflicts. The creation of the internet changed open-source intelligence by creating platforms to share information around the world instantaneously and without restrictions. The influence of social media was globally highlighted during the Iranian presidential election protests in 2009. Because of Twitter, Iranian citizens were still able to communicate with the world, despite their government’s attempts to shut down communication outside of the country.
In December 2021, thousands of Russian troops began moving and staging on Ukraine-bordering Russian land and Russian-occupied Crimea. By January and February of 2022, global news outlets were covering the impending conflict and highlighting President Putin’s actions and speeches. The Ukraine-Russia conflict had gathered the world’s attention and was “trending” on social media platforms, which resulted in millions of citizens viewing and “sharing” related posts. Pictures, videos, and other information were posted on social media by Russian and Ukrainian citizens and was analyzed by others around the world. Satellite imagery and Google Maps were used to pinpoint staging locations of Russian military assets, disprove Russian mal-information, and counter Russian military tactics.
United States law enforcement has several identified obstacles that have prevented the full utilization of OSINT including the massive amounts of data to be analyzed, low public sentiment around law enforcement technology, a culture of information hoarding, and a lack of national collection standards. The case study showed that, because the public sentiment of Ukraine was so high, much of the world was willing to assist in the dissemination and analysis of social media posts. Support for Ukraine was high, and crowdsourcing was so rampant that many OSINT databases were formed to help gather intelligence in central locations. Public sentiment, crowdsourcing, and these novel OSINT databases were vital in preparing Ukraine for the Russian invasion.
This thesis makes several recommendations through researching current law enforcement OSINT use and the Ukraine case study. The first recommendation is to solicit public crowdsourcing by improving public trust and sentiment toward law enforcement technology. Highlighting and showcasing the successful use of OSINT that demonstrates proactive measures of preventing violent acts such as mass shootings would aid this effort. The second recommendation is the creation of a national OSINT database similar to other national law enforcement databases. This would centralize important intelligence that would assist law enforcement safety measures around the country.
The third recommendation is the creation of an open-source intelligence national collection standards committee. This committee would form collection standards and disseminate “best practices” to guide intelligence analysts in the most efficient and effective collection methods. The research suggests that if implemented correctly, these recommendations would increase law enforcement’s ability to improve public safety and prevent violent crimes. In conclusion, this research means to improve law enforcement’s ability to use open-source intelligence to protect the citizens of the communities they serve. By increasing the amount of reliable, real-time open-source intelligence to law enforcement officers, they can more effectively preserve human life and prevent harm to civilians.