As artificial intelligence systems scale in depth, dimensionality, and internal coupling, their behavior becomes increasingly governed by deep compositional transformation chains rather than isolated functional components. Iterative projection, normalization, and aggregation mechanisms induce complex operator dynamics that can generate structural failure modes, including representation drift, non-local amplification, instability across transformation depth, loss of aligned fixed points, and the emergence of deceptive or mesa-optimizing substructures. Existing safety, interpretability, and evaluation approaches predominantly operate at local or empirical levels and therefore provide limited access to the underlying structural geometry that governs these phenomena. This work introduces \emph{SORT-AI}, a projection-based structural safety module that instantiates the Supra-Omega Resonance Theory (SORT) backbone for advanced AI systems. The framework is built on a closed algebra of 22 idempotent operators satisfying Jacobi consistency and invariant preservation, coupled to a non-local projection kernel that formalizes how information and influence propagate across representational scales during iterative updates. Within this geometry, SORT-AI provides diagnostics for drift accumulation, operator collapse, invariant violation, amplification modes, reward-signal divergence, and the destabilization of alignment-relevant fixed points. SORT-AI is intentionally architecture-agnostic and does not model specific neural network designs. Instead, it supplies a domain-independent mathematical substrate for analysing structural risk in systems governed by deep compositional transformations. By mapping AI failure modes to operator geometry and kernel-induced non-locality, the framework enables principled analysis of emergent behavior, hidden coupling structures, mesa-optimization conditions, and misalignment trajectories. The result is a unified, formal toolset for assessing structural safety limits and stability properties of advanced AI systems within a coherent operator–projection framework.