  When undertaking cyber security risk assessments, we must assign numeric
values to metrics to compute the final expected loss that represents the risk
that an organization is exposed to due to cyber threats. Even if risk
assessment is motivated from real-world observations and data, there is always
a high chance of assigning inaccurate values due to different uncertainties
involved (e.g., evolving threat landscape, human errors) and the natural
difficulty of quantifying risk per se. Our previous work has proposed a model
and a software tool that empowers organizations to compute optimal cyber
security strategies given their financial constraints, i.e., available cyber
security budget. We have also introduced a general game-theoretic model with
uncertain payoffs (probability-distribution-valued payoffs) showing that such
uncertainty can be incorporated in the game-theoretic model by allowing payoffs
to be random. In this paper, we combine our aforesaid works and we conclude
that although uncertainties in cyber security risk assessment lead, on average,
to different cyber security strategies, they do not play significant role into
the final expected loss of the organization when using our model and
methodology to derive this strategies. We show that our tool is capable of
providing effective decision support. To the best of our knowledge this is the
first paper that investigates how uncertainties on various parameters affect
cyber security investments.
