Delay-Adjusted Modeling of Cybersecurity Breaches Using INLA: Evidence from State Attorney General Data

Marco Pirra (), Sofia Sarubbo () and Fabio Viviano ()
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Marco Pirra: University of Calabria, Department of Economics, Statistics and Finance
Sofia Sarubbo: University of Calabria, Department of Economics, Statistics and Finance
Fabio Viviano: University of Calabria, Department of Economics, Statistics and Finance

A chapter in New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2025, pp 238-248 from Springer

Abstract: Abstract This paper presents a statistical framework for analyzing cybersecurity breach data, with a focus on delayed reporting dynamics. Using legally mandated breach notification records from U.S. state attorneys general, we construct a monthly panel of breach occurrences and disclosures for California and Indiana from 2015 to 2025. We implement a Bayesian model with a negative binomial likelihood, incorporating structured temporal, delay-specific, and seasonal effects, and estimate it using Integrated Nested Laplace Approximation (INLA). The model adjusts for reporting lags and enables probabilistic estimation of latent breach incidence. Empirical results reveal significant cross-state differences in breach volume and reporting behavior, underscoring the importance of jurisdiction-specific models. Our findings contribute to the literature on cyber risk forecasting and offer actionable insights for insurers, regulators, and policymakers. The proposed framework supports delay-adjusted risk monitoring and can be extended to additional jurisdictions or enriched with covariates to capture sectoral or organizational heterogeneity.

Keywords: Cyber risk modeling; Reporting delays; Bayesian inference; Integrated Nested Laplace Approximation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-05551-4_21

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DOI: 10.1007/978-3-032-05551-4_21

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