Roles and Challenges of AI-Based Cybersecurity: A Case Study

Abstract

Nowadays, organizations face complex cyber-attacks that are difficult to counter with traditional protections. Therefore, many modern organizations tend to use AI-based cybersecurity systems to secure their assets and infrastructure and to reduce the level of potential risks.

Because of the lack of clarity of the AI roles in cybersecurity and the ambiguity of the related challenges, this study aims to explore the most prominent roles of AI-based cybersecurity related to information security in the prevention phase, the detection phase and the response phase. The study also aims to determine the most prominent challenges facing AI-based cybersecurity.

To achieve the goals of this study, we adopt a case-study methodology that origins from the general framework which is information security and ends with the specific framework which is AI-based cybersecurity. After designing a matrix to analyze the case study, the study concludes nine important roles for AI-based cybersecurity distributed over the three phases. Three roles are in the prevention phase, which are automated assessment of security vulnerabilities, awareness and training and authentication. Two roles are in the detection phase; namely, detection of intrusion and security breaches and detection of spams and phishing and four rules are in the response stage, which are analyzing malware, automating routine tasks, deploying traps and topple attackers and isolating important assets.

The study also identifies eight elements that represent the most prominent challenges facing AI-based cybersecurity: regulations and principles, trust, accountability, privacy, bias, training datasets, human resources and financial costs. In conclusion, the study presents a set of recommendations drawn from the results.