LATIDIA · Ciberseguridad

Hacia una ciberdefensa responsable aumentada por IA: reconocimiento de patrones, defensa en profundidad y el caso de la colaboración entre humanos e IA

arXiv: 2609.25921v1Tipo de anuncio: nuevo Resumen: La literatura sobre ciberseguridad ha documentado ampliamente los beneficios operativos de la inteligencia artificial (IA) para la detección de amenazas, la respuesta a incidentes y la prevención, mientras que

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arXiv:2609.25921v1 Announce Type: new Abstract: Cybersecurity literature has extensively documented the operational benefits of artificial intelligence (AI) for threat detection, incident response, and prevention, while raising qualitative concerns about over-automation, algorithmic bias, and analyst-skill erosion. What remains largely absent is a formal, falsifiable model connecting three constructs that recur across this literature: Defense-in-Depth Theory, the Artificial Intelligence Theory of Pattern Recognition, and human-AI collaboration in security operations. This paper develops such a model. We formalize layered defense as a Bernoulli detection cascade in which AI augmentation enters multiplicatively across layers; we formalize each layer's pattern-recognition behavior as a Neyman-Pearson/Bayesian detector with a derived closed-form optimal threshold; and we formalize human-AI