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 nine requirement sets (AI-1 through AI-9) covering data partitioning, bias detection and mitigation, ML test coverage, operational drift monitoring, output verification, out of distribution detection, adversarial robustness, explainability, and human AI interaction. A three tier classification system (Safety Critical, Mission Critical, Mission 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. All terminology is traceable to ISO/IEC standards and NIST publications, with framework specific terms documented in a formal source traceability matrix.