Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance

Technical standards, or simply standards, are established documented guidelines and rules that facilitate the interoperability, quality, and accuracy of systems and processes. In recent years, we have witnessed an emerging paradigm shift where the adoption of generative AI (GenAI) models has increased tremendously, spreading implementation interests across standard-driven industries, including engineering, legal, healthcare, and education. In this paper, we assess the criticality levels of different standards across domains and sectors and complement them by grading the current compliance capabilities of state-of-the-art GenAI models. To support the discussion, we outline possible challenges and opportunities with integrating GenAI for standard compliance tasks while also providing actionable recommendations for entities involved with developing and using standards. Overall, we argue that aligning GenAI with standards through computational methods can help strengthen regulatory and operational compliance. We anticipate this area of research will play a central role in the management, oversight, and trustworthiness of larger, more powerful GenAI-based systems in the near future.

Publication Details

Journal
ArXiv.org
Published
2025-02-03
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00

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article

Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance

ArXiv.org
Adversarial Robustness in Machine Learning
article

Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance

article en

Abstract

Technical standards, or simply standards, are established documented guidelines and rules that facilitate the interoperability, quality, and accuracy of systems and processes. In recent years, we have witnessed an emerging paradigm shift where the adoption of generative AI (GenAI) models has increased tremendously, spreading implementation interests across standard-driven industries, including engineering, legal, healthcare, and education. In this paper, we assess the criticality levels of different standards across domains and sectors and complement them by grading the current compliance capabilities of state-of-the-art GenAI models. To support the discussion, we outline possible challenges and opportunities with integrating GenAI for standard compliance tasks while also providing actionable recommendations for entities involved with developing and using standards. Overall, we argue that aligning GenAI with standards through computational methods can help strengthen regulatory and operational compliance. We anticipate this area of research will play a central role in the management, oversight, and trustworthiness of larger, more powerful GenAI-based systems in the near future.

ArXiv.org
UK Research and Innovation, University of Bath
Openalex Percentile: Top 73%
Adversarial Robustness in Machine Learning
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Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance · ArXiv.org (2025) | TGRS Research Map | TGRS