AI-Driven Cyber Risk Assessment: Protecting Against Cyberthreats Determined with Machine Learning

Abstract

Digital defense systems are improved by the quick integration of artificial intelligence (AI) into cyber security, which makes it possible for predictive analysis, real-time anomaly detection, and automated threat detection.  In order to compromise, avoid, or trick AI-based security models, cybercriminals resorted to hostile AI techniques for creating AI weapons. The present research investigates how machine learning methods might be included into cyber risk assessment to anticipate and stop data theft. These include, among other things, deceptively created inputs or manipulation strategies that take advantage of flaws in machine learning algorithms, enabling an attacker to get around security measures, carry out cyberattacks covertly, and even tamper with AI-driven decision-making systems. The results show that by anticipating threats and improving security protocols, AI-driven models greatly improve cyber resilience. The paper also covers the difficulties and ethical issues surrounding the application of AI in cybersecurity. For companies looking to improve their cybersecurity frameworks using clever risk assessment tools, the findings offer insightful information. Finally, this work suggests that adversarial robustness, model interpretability, and the emerging discipline of explainable AI are all important directions for guaranteeing the safety and reliability of operation in high-risk operational scenarios. The integration of trusted AI models will be key to protecting critical infrastructure, enterprise data systems and national-level digital ecosystems against new threat vectors.