@article{ART003383494},
author={Seungman Jin and Deukjo Hong},
title={A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2026},
volume={31},
number={9},
pages={107-116}
TY - JOUR
AU - Seungman Jin
AU - Deukjo Hong
TI - A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector
JO - Journal of The Korea Society of Computer and Information
PY - 2026
VL - 31
IS - 9
PB - The Korean Society Of Computer And Information
SP - 107
EP - 116
SN - 1598-849X
AB - Financial institutions' rapid adoption of generative AI improves operational efficiency, but it also exposes them to autonomous AI threats that independently probe and attack external touchpoints, creating a problem in which threats evolve faster than security infrastructure and governance can be built. This study defines this speed gap as an independent research problem termed the Security Control Gap, and proposes a repeatedly recalculable risk-scoring model—based on likelihood, impact, detectability, and governance gap—that organizations can apply directly to security investment and phased response strategies. Applying this model to a Model Context Protocol (MCP)-based generative AI development environment, a Best-Worst Method (BWM) analysis identified unauthorized direct access to an external LLM (T1) as the top-priority threat; under an equal-weight baseline, T1 remained among the top two priorities together with T4 (Kendall's τ = 0.8), indicating that the core conclusion is largely, though not fully, insensitive to the weighting scheme.
KW - Generative AI;Security Control Gap;Risk Management;Best-Worst Method (BWM)
DO -
UR -
ER -
Seungman Jin and Deukjo Hong. (2026). A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector. Journal of The Korea Society of Computer and Information, 31(9), 107-116.
Seungman Jin and Deukjo Hong. 2026, "A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector", Journal of The Korea Society of Computer and Information, vol.31, no.9 pp.107-116.
Seungman Jin, Deukjo Hong "A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector" Journal of The Korea Society of Computer and Information 31.9 pp.107-116 (2026) : 107.
Seungman Jin, Deukjo Hong. A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector. 2026; 31(9), 107-116.
Seungman Jin and Deukjo Hong. "A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector" Journal of The Korea Society of Computer and Information 31, no.9 (2026) : 107-116.
Seungman Jin; Deukjo Hong. A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector. Journal of The Korea Society of Computer and Information, 31(9), 107-116.
Seungman Jin; Deukjo Hong. A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector. Journal of The Korea Society of Computer and Information. 2026; 31(9) 107-116.
Seungman Jin, Deukjo Hong. A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector. 2026; 31(9), 107-116.
Seungman Jin and Deukjo Hong. "A Repeatedly Applicable Risk Prioritization Model for Increasingly Advanced AI Threats in the Financial Sector" Journal of The Korea Society of Computer and Information 31, no.9 (2026) : 107-116.
