# A five-layer framework for AI governance: integrating regulation, standards, and certification

arXiv: https://arxiv.org/abs/2509.11332 · HTML: https://mlsanity.com/paper/2509.11332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems](https://mlsanity.com/paper/2201.06952/) (2022)
- [Standardised schema and taxonomy for AI incident databases in critical digital infrastructure](https://mlsanity.com/paper/2501.17037/) (2025)
- [RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems](https://mlsanity.com/paper/2306.01774/) (2023)
- [Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting](https://mlsanity.com/paper/2501.14778/) (2025)
- [Enhancements for Developing a Comprehensive AI Fairness Assessment Standard](https://mlsanity.com/paper/2504.07516/) (2025)
- [A multilevel framework for AI governance](https://mlsanity.com/paper/2307.03198/) (2023)

## Built on by (most cited first, 2 of 2)

- [A federated architecture for sector-led AI governance: lessons from India](https://mlsanity.com/paper/2603.26865/) (2026)
- [Benchmark-Based Comparative Assessment of Publicly Benchmarked Indian Foundation Models: A Capability and Evaluation-Maturity Framework](https://mlsanity.com/paper/2608.11891/) (2026)

## Abstract (arXiv)

Purpose: The governance of artificial iintelligence (AI) systems requires a structured approach that connects high-level regulatory principles with practical implementation. Existing frameworks lack clarity on how regulations translate into conformity mechanisms, leading to gaps in compliance and enforcement. This paper addresses this critical gap in AI governance. Methodology/Approach: A five-layer AI governance framework is proposed, spanning from broad regulatory mandates to specific standards, assessment methodologies, and certification processes. By narrowing its scope through progressively focused layers, the framework provides a structured pathway to meet technical, regulatory, and ethical requirements. Its applicability is validated through two case studies on AI fairness and AI incident reporting. Findings: The case studies demonstrate the framework's ability to identify gaps in legal mandates, standardization, and implementation. It adapts to both global and region-specific AI governance needs, mapping regulatory mandates with practical applications to improve compliance and risk management. Practical Implications - By offering a clear and actionable roadmap, this work contributes to global AI governance by equipping policymakers, regulators, and industry stakeholders with a model to enhance compliance and risk management. Social Implications: The framework supports the development of policies that build public trust and promote the ethical use of AI for the benefit of society. Originality/Value: This study proposes a five-layer AI governance framework that bridges high-level regulatory mandates and implementation guidelines. Validated through case studies on AI fairness and incident reporting, it identifies gaps such as missing standardized assessment procedures and reporting mechanisms, providing a structured foundation for targeted governance measures.