# Approaches to Responsible Governance of GenAI in Organizations

arXiv: https://arxiv.org/abs/2504.17044 · HTML: https://mlsanity.com/paper/2504.17044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Responsible AI Governance: A Systematic Literature Review](https://mlsanity.com/paper/2401.10896/) (2023)
- [Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study](https://mlsanity.com/paper/2502.00015/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises (FAIGMOE)](https://mlsanity.com/paper/2510.19997/) (2025)

## Abstract (arXiv)

PEER-REVIEWED AND ACCEPTED IN IEEE- ISTAS 2025 The rapid evolution of Generative AI (GenAI) has introduced unprecedented opportunities while presenting complex challenges around ethics, accountability, and societal impact. This paper draws on a literature review, established governance frameworks, and industry roundtable discussions to identify core principles for integrating responsible GenAI governance into diverse organizational structures. Our objective is to provide actionable recommendations for a balanced, risk-based governance approach that enables both innovation and oversight. Findings emphasize the need for adaptable risk assessment tools, continuous monitoring practices, and cross-sector collaboration to establish trustworthy GenAI. These insights provide a structured foundation and Responsible GenAI Guide (ResAI) for organizations to align GenAI initiatives with ethical, legal, and operational best practices.