A data-driven risk assessment of cybersecurity challenges posed by generative AI
Rami Mohawesh, Mohammad Ashraf Ottom, Haythem Bany Salameh
Abstract
Generative artificial intelligence (GenAI) refers to machines that can create new ideas and generate outputs similar to human cognition. This technology has ushered in a new era, offering remarkable learning capabilities and producing unique results. In this paper, we explore the role of GenAI in cybersecurity, highlighting potential risks such as data poisoning attacks, privacy concerns, and bias in decision-making. The study aims to examine how GenAI can enhance cybersecurity by improving AI algorithms and propose strategies for mitigating associated risks. As GenAI continues to gain significance across industries, especially healthcare, it is crucial to understand its potential benefits and the risks it may pose to ensure safe and responsible deployment. • Enhance cybersecurity by automating threat detection and response to cyber risks. • Identify system anomalies, phishing attempts, and malware with advanced pattern recognition. • Generate realistic threat scenarios to strengthen security measures and preparedness. • Raise concerns about data poisoning, privacy risks, and biased outputs in cybersecurity. • Advocate for ethical safeguards and responsible use to prevent misuse and security breaches.