Physical AI Safety Maturity Model (PAS-MM): A Five-Level Framework for Industry Readiness
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866902315706875904 |
|---|---|
| author | Melchior, Mati |
| author_facet | Melchior, Mati |
| contents | <p>Physical AI deployments — humanoid robots, autonomous mobile robots, surgical systems, autonomous vehicles — are scaling faster than the safety vocabulary used to describe them. Safety claims across organizations are not comparable. Adjacent fields solved this problem with maturity models: CMMI for software process, AI Safety Levels (ASL) for AI safety posture, ISO/IEC 33001 for process assessment, PCI DSS for payment-industry security. Physical AI lacks an equivalent.</p> <p>This paper proposes the Physical AI Safety Maturity Model (PAS-MM), a five-level classification system: Ad-hoc, Documented, Compliant, Certified, and Defense-in-depth. Levels are anchored to recognized functional-safety standards (IEC 61508 SIL, ISO 13849 PL) and to the Physical AI Safety Stack introduced in a forthcoming companion paper. PAS-MM includes a 30-question self-assessment instrument across six categories: hazard analysis, software safety, hardware safety, certification posture, operational monitoring, and incident response. A 0–90 score maps to a PAS Level via a defined rubric. Application to approximately 30 publicly-known Physical AI organizations, using public information only, shows clustering at PAS 1–2, a small population at PAS 3–4, and no organization at PAS 5. The paper concludes with adoption guidance for analysts, regulators, insurers, customers, and Physical AI organizations.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20048267 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Physical AI Safety Maturity Model (PAS-MM): A Five-Level Framework for Industry Readiness Melchior, Mati Physical AI Safety maturity model IEC 61508 ISO 13849 certification industry assessment robotics safety functional safety <p>Physical AI deployments — humanoid robots, autonomous mobile robots, surgical systems, autonomous vehicles — are scaling faster than the safety vocabulary used to describe them. Safety claims across organizations are not comparable. Adjacent fields solved this problem with maturity models: CMMI for software process, AI Safety Levels (ASL) for AI safety posture, ISO/IEC 33001 for process assessment, PCI DSS for payment-industry security. Physical AI lacks an equivalent.</p> <p>This paper proposes the Physical AI Safety Maturity Model (PAS-MM), a five-level classification system: Ad-hoc, Documented, Compliant, Certified, and Defense-in-depth. Levels are anchored to recognized functional-safety standards (IEC 61508 SIL, ISO 13849 PL) and to the Physical AI Safety Stack introduced in a forthcoming companion paper. PAS-MM includes a 30-question self-assessment instrument across six categories: hazard analysis, software safety, hardware safety, certification posture, operational monitoring, and incident response. A 0–90 score maps to a PAS Level via a defined rubric. Application to approximately 30 publicly-known Physical AI organizations, using public information only, shows clustering at PAS 1–2, a small population at PAS 3–4, and no organization at PAS 5. The paper concludes with adoption guidance for analysts, regulators, insurers, customers, and Physical AI organizations.</p> |
| title | Physical AI Safety Maturity Model (PAS-MM): A Five-Level Framework for Industry Readiness |
| topic | Physical AI Safety maturity model IEC 61508 ISO 13849 certification industry assessment robotics safety functional safety |
| url | https://doi.org/10.5281/zenodo.20048267 |