Towards a Framework for Collaborative Enterprise Security of the users (especially if the existing law and enforcement system is not so effective) since these would act as pointers for their justification towards determining the overall utility and effectiveness of the reporting actions. Indeed, for a concrete realization of the presented framework, it is impor- tant to decide who verifies the reported violation and who approves the payouts, which would be determined by the existing corporate governance structure and policies of the organization. Finally, a big computational challenge for the proposed probabilistic model for param- eter estimation (ref. Section on `Experimental Analysis') is how to make it scalable to meet the needs of large organizations. The success of the payoff model largely depends on estimating the values of rewards and punishments properly. The experimental setup presented for estimating different parameters of a payoff matrix does not appear to scale well. However, our hope is that the model to also deal with the scenarios where social networking (in particular transient social networking) based collaborative proof generation against security violations can also be enabled. In our experiments, we implicitly assumed that individual motivation alone can determine the likelihood of reporting of a violation by a user as modelled by Eq. (5), which is still a high level abstraction and leaves the scope of further work in this direction. The objective of this work would be to relate human behaviour with intrinsic or extrinsic rewards and losses in a more detailed manner. Work in the direction of human behaviour modeling (Puleo, 2006) would contribute concretely toward this goal. Further analysis would require modeling the reporting behaviour of users for secondary violations in a general setting involving n players. This may in turn enable a derivation of closed form solutions for optimal estimates of parameters in the pay off