MANAGING UNKNOWN-UNKNOWNS IN CYBER-SECURITY

Techniques are described herein for managing unknown-unknowns in cybersecurity. Trust degradation is a precursor index to failure. The use cases of scoring the trust degradation in a system span to almost every aspect in networking, edge and cloud included. A well devised Trust Evaluation Function (TEF) will cover many use cases: for example (1) better and adaptive private key management (e.g., re-keying); (2) better and adaptive end user experience password management and its fine grain monitoring in a data center; (3) better and adaptive digital asset certifications; (4) troubleshooting; and (5) real-time scalability and risk assessment for extremely large network, for example in federated cloud environment. The features of a digital trust scoring will start to reflect the likelihood of erosion of trust created on day 0. Platform independency is achieved when the score is a degradation of the trust and not the trust value alone. A trust value may start erroneously, but the rate of change may lead to continuous evaluation. Therefore, the originating trust is set as a prior. Erosion will thus work with time against the assumed original trust. In the example of an expiration date or a combinatorial complexity erosion of a private key, the realization of a trust erosion is not a Boolean fail pass type, but a relative factor number. On a comprehensive integrated analytical dashboard, the trust factor produces the percent life left of given a digital secret. DETAILED DESCRIPTION Trust under certainty does not recognize the element of unknown | unknown. Simply, the foundational models of certainty lie in the notion that the definition of prior is what has been known or measured. No provisions are made otherwise for undeclared variables. Trust under uncertainty intends to model the unknown. But the argument remains that trust under uncertainty will use certainty approaches once certain criteria are met. For general discussion purposes, trust under uncertainty obeys the law of information preservation as defined in Information Theory principles. The clear 2 Maluf: MANAGING UNKNOWN-UNKNOWNS IN CYBER-SECURITY Published by Technical Disclosure Commons, 2018 2 5670 difference is that trust under uncertainty will optimally extrapolate, in terms of new a posteriori hypothesis, trust outside the scope of the known models and priors. With lack of ideal models, the concept of discarding random variables for density functions is of interest. For example, the individual outcome of a specific atom in a Brownian motion is not of interest. On the other hand, the density function of few atoms is useful. Thus the kinetic energy of the collective makes sense based on the sampling of density functions. Similarly, trust under uncertainty relies on density functions and is measured in terms of entropy uncertainty. It reflects the uncertainty of a whole system not dependent on the assumed models. There are three basic definitions that trust may fall under. First, outcomes are known. In this case, the range of outcomes is known and the outcome is also known. This is the easiest way to make risk management decisions. This reflects a deterministic event process. This is also known as “you know what you know” paradigm or the kk paradigm. Trust can be summarized as making decisions based on what is “known to be known” or {k | k}. The model parameters are defined and deterministic. Observations in {k | k} have known means but zero variances: y(t) = f(μ). Second, outcomes are unknown, but probabilities are known. In this case, the range of outcomes are known but the individual outcome is unknown. This reflects a deterministic process with known density functions. All of the outcomes are known as are the probabilities of each. This is also known “you know what you don’t know paradigm”