arbiter
JournalsAccount

Generative Artificial Intelligence and Cyber Security in Central Banking

Iñaki Aldasoro et al.

Journal of Financial Regulation2024https://doi.org/10.1093/jfr/fjae008article
ABDC B
Weight
0.77

Abstract

Generative artificial intelligence (gen AI) introduces novel opportunities to strengthen central banks’ cyber security but also presents new risks. This article uses data from a unique survey among cyber security experts at major central banks to shed light on these issues. Responses reveal that most central banks have already adopted or plan to adopt gen AI tools in the context of cyber security, as perceived benefits outweigh risks. Experts foresee that AI tools will improve cyber threat detection and reduce response time to cyber attacks. Yet gen AI also increases the risks of social engineering attacks and unauthorized data disclosure. To mitigate these risks and harness the benefits of gen AI, central banks anticipate a need for substantial investments in human capital, especially in staff with expertise in both cyber security and AI programming. Finally, while respondents expect gen AI to automate various tasks, they also expect it to support human experts in other roles, such as oversight of AI models.

18 citations

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1093/jfr/fjae008

Or copy a formatted citation

@article{iñaki2024,
  title        = {{Generative Artificial Intelligence and Cyber Security in Central Banking}},
  author       = {Iñaki Aldasoro et al.},
  journal      = {Journal of Financial Regulation},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1093/jfr/fjae008},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Generative Artificial Intelligence and Cyber Security in Central Banking

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.77

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact1.00 × 0.4 = 0.40
M · momentum0.95 × 0.15 = 0.14
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.