Requirements and Verification Standards for Artificial Intelligence in Safety-Critical Applications

This framework establishes the AI specific requirements and verification standards against which Safety Critical Labs conducts certification assessments for systems incorporating artificial intelligence or machine learning capabilities in safety critical applications.

The framework addresses verification gaps that traditional software assurance does not cover. AI and ML systems introduce probabilistic outputs, emergent behavior from training data, and potential performance degradation over time. These characteristics require verification approaches designed specifically for data driven systems.

The framework defines ten requirement sets (AI-1 through AI-10) covering operational and data foundations (including operational design domain definition and data partitioning), bias detection and mitigation, ML test coverage, continuous validation and drift monitoring, hallucination prevention, out of distribution detection, adversarial robustness, explainability, human AI teaming, and privacy and data protection. A three tier classification system (Safety Critical, Mission Critical, Operational Support) determines which requirements apply based on consequence level, not probability.

The framework is domain agnostic and applicable across aerospace, aviation, automotive, medical, industrial, and other safety critical domains. Terminology is traceable to ISO/IEC standards and NIST publications, and each parent requirement carries Applicable Domain Standards citations drawing from cross domain (NIST AI RMF, ISO/IEC 22989, 23894, 25059, and 42001, NIST SP 800-53), aviation, automotive, medical, and EU and US regulatory sources. Framework specific terms are documented in a glossary aligned with ISO/IEC 22989:2022 and ISO/IEC 5338:2023.