Privacy and security within Online Social Networks (OSNs) has become a major concern over recent years. As individuals continue to actively use and engage with these mediums, one of the key questions that arises pertains to what unknown risks users face as a result of unchecked publishing and sharing of content and information in this space. There are numerous tools and methods under development that claim to facilitate the extraction of specific classes of personal data from online sources, either directly or through correlation across a range of inputs. In this paper we present a model which specifically aims to understand the potential risks faced should all of these tools and methods be accessible to a malicious entity. The model enables easy and direct capture of the data extraction methods through the encoding of a data-reachability matrix for which each row represents an inference or data-derivation step. Specifically, the model elucidates potential linkages between data typically exposed on social-media and networking sites, and other potentially sensitive data which may prove to be damaging in the hands of malicious parties, i.e., fraudsters, stalkers and other online and offline criminals. In essence, we view this work as a key method by which we might make cyber risk more tangible to users of OSNs.