From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916017601511424 |
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| author | Mumtaz, Ummara Mumtaz, Summaya |
| author_facet | Mumtaz, Ummara Mumtaz, Summaya |
| contents | Artificial intelligence systems are increasingly deployed in high-stakes domains, yet it remains unclear whether existing governance frameworks ensure accountability after deployment. This study makes two contributions. First, it presents a cross-regulatory empirical analysis of 480 real-world AI incidents from the AI Incident Database (AIID), evaluating their alignment with post-deployment provisions in three major governance frameworks: the EU AI Act (Articles 72-73), the NIST AI Risk Management Framework (MANAGE and GOVERN functions), and the General Data Protection Regulation (GDPR Articles 22, 33-35). The results reveal substantial governance gaps across these frameworks, indicating persistent weaknesses in post-deployment accountability. Second, based on these findings, the study proposes the Proactive AI Governance Compliance Framework (PAGCF), a four-phase lifecycle methodology designed to shift governance from reactive incident response toward pre-deployment compliance assurance. The framework includes risk-stratified governance tiers, an implementation checklist linked to specific regulatory provisions, and a projected impact analysis that uses internal monitoring as a proxy for proactive governance capacity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16281 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework Mumtaz, Ummara Mumtaz, Summaya Computers and Society Artificial Intelligence Artificial intelligence systems are increasingly deployed in high-stakes domains, yet it remains unclear whether existing governance frameworks ensure accountability after deployment. This study makes two contributions. First, it presents a cross-regulatory empirical analysis of 480 real-world AI incidents from the AI Incident Database (AIID), evaluating their alignment with post-deployment provisions in three major governance frameworks: the EU AI Act (Articles 72-73), the NIST AI Risk Management Framework (MANAGE and GOVERN functions), and the General Data Protection Regulation (GDPR Articles 22, 33-35). The results reveal substantial governance gaps across these frameworks, indicating persistent weaknesses in post-deployment accountability. Second, based on these findings, the study proposes the Proactive AI Governance Compliance Framework (PAGCF), a four-phase lifecycle methodology designed to shift governance from reactive incident response toward pre-deployment compliance assurance. The framework includes risk-stratified governance tiers, an implementation checklist linked to specific regulatory provisions, and a projected impact analysis that uses internal monitoring as a proxy for proactive governance capacity. |
| title | From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2605.16281 |