Govern once/comply many: Leveraging cyber security framework experience to support AI governance

F. Paul Greene
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F. Paul Greene: Harter Secrest & Emery LLP, USA

Cyber Security: A Peer-Reviewed Journal, 2025, vol. 9, issue 1, 49-60

Abstract: Data protection is notoriously complex and artificial intelligence (AI) has only added to that complexity. In addition, many organisations are floundering as they seek to adopt AI in an ethical and trustworthy manner. This paper addresses skill sets and frameworks familiar to IT and cyber security professionals that can be leveraged to help build a robust approach to AI governance. Adopting the maxim of ‘govern once/comply many’, the paper compares and contrasts existing cyber security frameworks and approaches that address the governance concerns that arise with AI. It also uses the National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework as a lens through which to assess the utility of cyber security frameworks to inform AI governance efforts. Generally, the map, measure, manage and govern functions of the NIST Artificial Intelligence Risk Management framework align well with the confidentiality, integrity, and availability foci of established cyber security frameworks, forming the beginnings of a common language, when it comes to issues of data protection and AI governance. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Keywords: artificial intelligence; cyber security; governance; framework (search for similar items in EconPapers)
JEL-codes: M15 (search for similar items in EconPapers)
Date: 2025
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