Knowledge risk management (KRM) is an emerging field which offers a solution to the
problems associated with conventional risk management methods. The problem of
environmental complexity is manifested by individuals not knowing enough about the risk to
anticipate its likelihood and consequences. Environmental complexity creates uncertainty.
Knowledge moves individuals along the spectrum of uncertainty towards certainty; making
risk a ‘learnable’ rather than an entirely random event (Apgar, 2006). The problem of
cognitive constraints is caused by subjectivity. Subjectivity is manifested in two ways. First,
individuals’ do not perceive risk in the precise logic of decision theory (cited in March and
Shapira, 1987). The brain does not work in the way decision trees suggest it should. Second,
individuals vary in their perception of reality. Knowledge can increase objectivity by training
individuals to process risk the same way (e.g. HRI), and by providing individuals with better
tools for understanding the nature of risk. This paper contributes to KRM theory by
developing a decision support tool, i.e. conceptual model, to help this latter point. In this
way, the paper provides a solution to the problem of cognitive constraints presented by
conventional decision tree methods, by using KM tools and techniques to enable individuals
to generate deeper insight about the real nature of organizational risk