Managing cyber risks in the face of AI- and ML - Driven Adversarial Attacks
Godwill Chimamiwa · 2024
This paper presents a critical analysis of current cyber risk management practices in light of new and evolving Artificial Intelligence (AI) and Machine Learning driven adversarial attacks. Many enterprises are constantly grappling with cybersecurity risks and increased threats from phishing, ransomware and many other forms of cyber-attacks, often resulting in substantial financial losses when the risks are not adequately addressed. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), such cyber-attacks and incidents will become more prevalent and potentially more devastating to businesses large and small. With AI and ML tools at their disposal, cybercriminals can dramatically reduce technical barriers for launching cyberattacks. They can easily develop more sophisticated social engineering tactics and ‘deep fakes’ that are not easily identifiable as such, thereby increasing the risks of unauthorized data disclosure. Drawing on literature review analysis, this research explores current and emerging AI- and ML-driven cyber threats faced by enterprises, effectiveness of current cyber mitigation measures and future management practices that can be leveraged to improve the security posture of enterprises. The study evaluates both technical and non-technical cyber risk management and mitigation measures and frameworks. The findings from this study help inform enterprise cyber risk managers and practitioners about the enormity of AI- and ML-driven cyber risks and presents emerging best practices to adequately mitigate those risks. The study contributes to the growing research on how threat actors are leveraging and AI and ML to expand cyber threats and how enterprises and organizations should respond to these ever evolving cyber risks.