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ZENODO
Journal . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Journal . 2025
License: CC BY
Data sources: Datacite
ZENODO
Journal . 2025
License: CC BY
Data sources: Datacite
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Extorsión digital con inteligencia artificial en el Perú: revisión sistemática del marco normativo y lineamientos para su prevención

Authors: Palomino Firma, Sarasara Victoria;

Extorsión digital con inteligencia artificial en el Perú: revisión sistemática del marco normativo y lineamientos para su prevención

Abstract

Este artículo tiene como finalidad examinar el marco normativo peruano sobre extorsión digital potenciada por IA, compararlo con estándares internacionales y proponer lineamientos normativo-operativos para su prevención, persecución y prueba pericial. Empleando metodología de revisión sistemática (PRISMA 2020) de 30 fuentes académicas, normativas e institucionales (2020–2025), con análisis documental, jurídico-comparado y síntesis cualitativa temática, obteniéndose como resultados que, la evidencia muestra la capacidad de generación sintética (deepfakes y clonación de voz) supera la detección humana y algorítmica en contextos reales; el Perú avanzó con la Ley N.º 32314 (agravante por uso de IA), pero persisten vacíos de procedimientos de retiro ágil de contenidos, preservación temprana de evidencia y estándares periciales para audio/imagen sintética. En el derecho comparado, la UE (AI Act), Ofcom (Reino Unido) y eSafety (Australia) establecen obligaciones de diligencia, transparencia y herramientas de atribución/provenance. En consecuencia, se recomienda la implementación de un modelo híbrido complemente la sanción penal con deberes de plataformas (detección, etiquetado y preservación), fortalecimiento de laboratorios forenses y programas de prevención y educación en ciberseguridad, para mejorar la respuesta estatal y la protección de las víctimas.

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Keywords

extorsión digital, inteligencia artificial, deepfakes

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green