Minimise model risk management oversight for cyber security solutions

Liming Brotcke
Additional contact information
Liming Brotcke: Ally Financial, USA

Journal of AI, Robotics & Workplace Automation, 2023, vol. 3, issue 1, 55-63

Abstract: Adoption of artificial intelligence (AI) and machine learning (ML)-powered cyber security tools and models by financial institutions has received considerable attention in the model risk management community. In parallel, developing trustworthy AI that is more explainable, fair, robust, private and transparent has also received considerable research and regulatory attention. Appropriate governing of cyber security models is inevitable. The prevailing thought at present is to have the model risk management function to oversee the development, implementation and use of such cyber security tools and models. This study first demonstrates two primary challenges of executing this oversight and then offers a few practical suggestions to ensure a reasonable application.

Keywords: cyber security; data science; model validation; machine learning (search for similar items in EconPapers)
JEL-codes: G2 M15 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hstalks.com/article/8327/download/ (application/pdf)
https://hstalks.com/article/8327/ (text/html)
Requires a paid subscription for full access.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aza:airwa0:y:2023:v:3:i:1:p:55-63

Access Statistics for this article

More articles in Journal of AI, Robotics & Workplace Automation from Henry Stewart Publications
Bibliographic data for series maintained by Henry Stewart Talks ().