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Real Life Examples - OSINT in civil Litigation #2 - Accident Case

This is the second article in a series that looks at the benefits of using open source intelligence as a tool for civil litigation.

(specifics and details have been changed for privacy)

In this example, a client had come to use to locate 2 individuals. One had worked for a company who was doing road work, and the other had been a witness to an accident that took place at the construction site. The problem was that the 2 people had very common names, and very little additional information was available, besides the fact of their involvement in the accident. Before hiring our company, a previous investigations company had tried to locate the 2 individuals. Their method of operation was to find all people in the area with that name (using a database) and then send out letters and visit each of the people to try and determine who was the correct person. There are several problems with this approach.

1. You make the assumption that you have the correct mailing addresses for these people and that they will open your mail and not toss it thinking its junk mail.

2. You make the assumption that the person you are looking for will read your letter and contact you about a case they are involved in. If a person was involved in an even that may lead to litigation they tend to put 2 and 2 together and may run for the hills.

3. Most people don't answer the door for strangers in this day and age.

Needless to say, this approach didn't work, and Rush Intel was hired to make another attempt.

We began by looking at every bit of information that was known:

Person A: A security guard who was working nearby the accident scene. We know the people he was working for. He had a very common name, lets call him John Smith.

Person B: A worker from the construction company, he was simply a day laborer, we knew that his boss picked him up near his house in Ontario, but did not know his address, we knew his approx age of 35, and he had a very common name, lets call him Jose Romero.

We began with the security guard. Using the BSIS database (The CA agency that issues guard cards) we were able to get an idea of how many people had a guard card with the name John Smith. This also gave us a very important advantage: the guard card records have a middle name or initial. Now we have narrowed our scope. 4 Potential matches. We now check into the background of these people. We also know the date of the accident which was several years ago. We eliminate 2 of them, who didn't become registered until recently. The remaining 2 people are further checked out, we locate their social media, online accounts, and other information. After combing through one of the Facebook accounts, we can see that he is friends with the people we knew to be the employer of our person at the time. The other persons social media has no connections. We have found person A.

The Day laborer was a bit more tricky. We begin with an overall search for people with his name in the area of Ontario. 75 people. We further narrow our search using dates and the description of the neighborhood in which his boss would pick him up. Then we narrowed our search by calculating his approx age today. This dropped our possible number to 23. We collected data on these people. Particularly phone numbers. We fed these numbers into a proprietary software algorithm which searches for social media based on the phone number used to set it up. After reviewing the data, and determining all trades associated with the potential people, we had one person, who worked as a day laborer, right age, right location, right person.

We were able to confidently say we had the right person without ever having to contact them. Our client also relayed to us afterwards that they had spoke to both people and indeed they were the correct person. By comparing several points of data, we can eliminate the slow, and problematic methods used by Investigators of time passed. As a bonus, these people are now easy to serve because they have not been tipped off to any potential litigation.

Open Source intelligence is about using information that is available to everyone. However the key to being effective is to know how to filter out the information that you don't want. This takes experience, the right tools, and a team that knows where to look.

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