Information leakage via the networks formed by subjects (e.g., Facebook, Twitter) and objects (e.g., blogosphere) - some of whom may be controlled by malicious insiders - often leads to unpredicted access control risks. While it may be impossible to precisely quantify information flows between two entities (e.g., two friends in a social network), this paper presents a first attempt towards leveraging recent advances in modeling socio-information networks to develop a statistical risk estimation paradigm for quantifying such insider threats. In the context of socio-information networks, our models estimate the following likelihoods: prior flow - has a subject s acquired covert access to object o via the networks? posterior flow - if s is granted access to o, what is its impact on information flows between subject s′ and object o!? network evolution - how will a newly created social relationship between s and s′ influence current risk estimates? Our goal is not to prescribe a one-size-fits-all solution; instead we develop a set of composable network-centric risk estimation operators, with implementations configurable to concrete socio-information networks. The efficacy of our solutions is empirically evaluated using real-life datasets collected from the IBM SmallBlue project and Twitter.