Cyber Insurance in the Age of AI-Powered Attacks: Pricing and Coverage Strategies as AI-Generated Malware and Deepfake Fraud become Mainstream

Abstract

The adoption of artificial intelligence (AI) as an agent of cyberattacks has reshaped the threat ecosystem that creates unequal beats in terms of speed, magnitude, and flexibility. In contrast to more traditional forms of activity, artificial intelligence (AI) malicious activities like generative malware, deep fraction schemes, and auto-pishing are developed like self-regulating processes hidden in advance of the usual detection and countermeasures. These changes contradict the original data of cyber insurance, which in olden days relied on retroactive data collected by the actuaries and foreseeable loss distributions. Increased uncertainty in the process of estimating the probability of losses, correlated risks and loss of sufficient solvency margins adversely impact insurers in the environment where adversarial AI technologies are rapidly expanding. The constraints of the deterministic pricing and coverage approaches have been progressively restricted as systemic and adaptive loss is created as the lynchpin advances of the dynamic and self-informed attack systems that the traditional models cannot predict. To support such complexities, this current paper suggests a combined analytical architecture, which will combine probabilistic risk modeling, behavioral analytics, and dynamical pricing algorithms to meet AI-based threat backgrounds. The framework also adds the adaptive parameters which include attack automation index, impossibility of impersonation, and model-driven threat intelligence, to real-time recalibrate premiums. Empirical findings and simulated loss models indicate that next-generation pricing methods tend to ignore AI-inflicted losses of up to 35, which puts insurers at risk of having large portfolios. The suggested model will allow the use of current threat information to influence the dynamically recalculate the premiums and coverage by insurers, boosting actuarial conditions and the strength of the market. These results point to the historical lack of urgency regarding the necessity of a paradigm shift to AI-sensitive cyber insurance foundations that would focus on ongoing risk detection, well-founded data distribution, and collaboration of regulating bodies to protect the financial uncertainty of clever cyber risks