The serious privacy and security problems related to online social networks (OSNs) are what fueled two complementary studies as part of this thesis. In the first study, we developed a general algorithm for the mining of data of targeted organizations by using Facebook (currently the most popular OSN) and socialbots. By friending employees in a targeted organization, our active socialbots were able to find new employees and informal organizational links that we could not find by crawling with passive socialbots. We evaluated our method on the Facebook OSN and were able to reconstruct the social networks of employees in three distinct, actual organizations. Furthermore, in the crawling process with our active socialbots we discovered up to 13.55% more employees and 22.27% more informal organizational links in contrast to the crawling process that was performed by passive socialbots with no company associations as friends. In our second study, we developed a general algorithm for reaching specific OSN users who declared themselves to be employees of targeted organizations, using the topologies of organizational social networks and utilizing socialbots. We evaluated the proposed method on targeted users from three actual organizations on Facebook, and two actual organizations on the Xing OSN (another popular OSN platform). Eventually, our socialbots were able to reach specific users with a success rate of up to 70% on Facebook, and up to 60% on Xing.