Typically, security managers and safeguards designers only consider the surface security issues, i.e., the first-order security problems. These safeguards often constitute highly constraining organizational structures that can become costly burdens. The purpose of this paper is to analyze a fundamentally different perspective on the nature of information systems security safeguards. The paper discusses issues and aspects of managing adaptive systems security including adaptive safeguard techniques. This is because predictability is an attribute of security, and organizational spontaneity implies a large degree of entropy. In particular organizational flexibility and adaptability may be affected. The second-order issues regard the impact of these safeguards on the organization. These issues also regard the safeguards that may be deployed to protect systems from these. These first-order issues regard the direct threats against systems such as computer abuse, software unreliability, and natural disasters. However, much of the current work in this area is concerned with first-order issues of the security problem. Security is an important aspect of information systems. Our approach links known dynamical machines states with human qualia and thus offers new insight into the hard problem of artificial intelligence. By examining changes specific to people or locations we can model human cognitive symbols featuring these dependencies, such as attachment and grief. Drastic change in any of these environmental inputs can again lead to inoperative programs.
In the third stage, we consider learning machines trained to implement multiple functions in simple sequential programs using environmental data to specify subroutine names, control flow instructions, memory calls, and so on. This state immediately predicts and explains the characteristics of the semantic experiences of humans in similar circumstances.
In this situation, learning machines are unable to extract information from their input and will typically enter a dynamical state characterized by null outputs and a lack of response. Second, we model the dysfunctional human response to a radically changed environment as being the natural response of any learning machine facing novel inputs from well outside its previous training set. For this class of symbols, pre-learned programs become inoperative so these syntactical programs cannot be the source of experienced qualia. First, we introduce a new class of human semantic symbols which appears when unexpected and drastic environmental change causes humans to become surprised, confused, uncertain, and in extreme cases, unresponsive, passive and dysfunctional. We address this question in three stages. The hard problem in artificial intelligence asks how the shuffling of syntactical symbols in a program can lead to systems which experience semantics and qualia.
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