Managing cyber risks in the face of AI- and ML - Driven Adversarial Attacks

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 other forms of cyber-attacks, often resulting in substantial financial losses when risks are not adequately addressed. With the advent of AI and ML, such cyber-attacks and incidents are expected to become more prevalent and potentially more devastating to businesses of all sizes. With AI and ML tools at their disposal, cybercriminals can significantly reduce technical barriers to launching cyberattacks. They can easily develop more sophisticated social engineering tactics and "deep fakes" that are challenging to identify, thereby increasing the risks of unauthorized data disclosure. Drawing on a literature review analysis, this research explores current and emerging AI- and ML-driven cyber threats faced by enterprises, assesses the effectiveness of current cyber mitigation measures, and discusses future management practices to enhance 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 aim to inform enterprise cyber risk managers and practitioners about the enormity of AI- and ML-driven cyber risks and present emerging best practices to adequately mitigate those risks. This study contributes to the growing body of research on how threat actors leverage AI and ML to expand cyber threats and how enterprises and organizations should respond to these ever-evolving cyber risks.