Orthogonal Axiomatic CI/CD for Safety-Critical AI Systems: Considering ISO-26262, DO-178C, EU AI Act for Audit readiness, Deployment Velocity, Incident Resolution, Liability Reduction. V-2.0

Safety-critical AI must be reliably safe and checkable at all times; normal testing methods don’t scale to complex systems, so safety must be built into deployment and continuously verified. Safety-critical AI needs provable, enforceable, auditable guarantees; traditional testing (coverage, regression, audits) fails at scale due to combinatorial complexity requiring deployment-integrated safety verification. This paper is about an orthogonal axiomatic framework for governing safety-critical AI systems through continuous integration and continuous deployment (CI/CD) pipelines. The framework is founded on six independent axioms Totality (A1), Liveness (A2), Byzantine Robustness (A3), Transport Integrity (A4), Causal Traceability (A5), and Semantic Grounding (A6); whose mutual independence we establish via explicit separating models. An audited oracle maps every reachable system state to a binary safety verdict against the axiom set, defining a safe manifold M as the intersection of all axiom-satisfying states. A meta-axiom guarantees that the axiom system itself is irredundant and complete over the operational domain. We derive six enforceable invariants, each the CI/CD projection of an axiom, and prove a bounded-drift convergence theorem: any axiom-violating state is corrected to within ε-distance of M in at most ⌈d0/δ⌉ rollback steps. A concrete toy system demonstrates violation detection, rollback, and reconvergence. Governance comparisons with ISO 26262, DO-178C, and the EU AI Act illustrate regulatory alignment. The contribution is a minimal, orthogonal, regulator-ready, and CI/CD-enforceable safety architecture for autonomous AI. Keywords: safety-critical AI, formal verification, CI/CD, axiomatic systems, orthogonalindependence, bounded convergence, governance, safe manifold.