Trusted Computing in Data Science: Viable Countermeasure in Risk Management Plan

Abstract

The need for secure data systems has prompted, the constant reinforcement of security systems in the attempt to prevent and mitigate risks associated with information security. The purpose of this paper is to examine the effectiveness of trusted computing in data science as a countermeasure in risk management planning. In the information age, it is evident that companies cannot ignore the impact of data, specifically big data, in the decision making processes. It promotes not only the proactive capacity to prevent unwarranted situations while exploiting opportunities but also the keeping up of the pace of market competition. However, since the overreliance on data exposes the company, trusted computing components are necessary to guarantee that data acquired, stored, and processed remains secure from internal and external malice. Numerous measures can be adopted to counter the risks associated with data exploitation and exposure due to data science practices. Nonetheless, trusted computing is a reasonable point to begin with, in the aim to protect provenance systems and big data systems through the establishment of a 'chain of trust' among the various computing components and platforms. The research reveals that trusted computing is most effective when combined with other hardware-based security solutions since attack vectors can follow diverse paths. The results demonstrate the potential that the technology provides for application in risk management.