Autonomous driving paper index
How Human-Ai Decision Rights Shape Cybersecurity: Empirical Evidence On Data Breaches
One-line summary
Drawing on Agency Theory, we develop a framework that distinguishes between Autonomous AI strategy and an Advisory AI strategy.
Engineering notes
Key topics: autonomous driving, large language model, deployment. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Artificial intelligence has become a strategic cornerstone of organizational transformation, offering the potential to enhance cybersecurity while introducing new risks. Drawing on Agency Theory, we develop a framework that distinguishes between Autonomous AI strategy and an Advisory AI strategy. Using a large language model –based classification of job postings linked to panel data on 3,006 U.S. firms, we empirically examine how these two AI strategies impact data breach risks. We find that an Autonomous AI strategy increases breach risk, whereas an Advisory AI strategy reduces it. Furthermore, greater AI deployment depth amplifies the effects of both strategies—intensifying risk under Autonomous AI while strengthening the protective benefits of Advisory AI. Moreover, stronger AI governance does not offset the risks of Autonomous AI but further enhances the security benefits of Advisory AI. These findings demonstrate that the security implications of AI depend critically on how decision authority is structured within organizations.
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