Formal Decision Traces for Data-Driven Verification and Post- Quantum Attestation in Cyber-Resilient Explainable AI Systems

As artificial intelligence systems are deployed in complex, interconnected cyber-physical environments—spanning healthcare diagnostics, critical infrastructure management, and autonomous vehicle control—the limitations of traditional explainability methods become increasingly consequential. Post-hoc explanation techniques such as SHAP and LIME describe model behavior but cannot guarantee constraint satisfaction, leaving safety-critical systems vulnerable to adversarial manipulation and providing insufficient assurance for trustworthy AI deployment. This paper presents a theoretical framework and architecture for Formal Decision Traces (FDTs), a data-driven verification approach that integrates Satisfiability Modulo Theories (SMT) solvers with neural language models to produce machine-checkable proof certificates for system outputs, bridging the gap between explainable analytics and formal AI security. We make four contributions: (1) We present a theoretical framework for verified explainability and formally define FDTs as structured verification artifacts, establishing their advantages over attention-based and feature attribution methods for cyber-resilient applications. (2) We analyze the adversarial robustness properties of SMT-verified outputs, providing formal guarantees that constraint-based verification detects any encodable violation—a completeness property that probabilistic guardrails cannot achieve. (3) We propose post-quantum secure attestation using NIST FIPS 204 (ML-DSA) lattice-based signatures, addressing the quantum threat to long-lived verification records in safety-critical domains. (4) We validate the architecture through a proof-of-concept implementation using the Z3 SMT solver across three safety-critical domains (healthcare, SCADA, autonomous vehicles), characterizing verification performance at 2–4 ms per query (N = 100 trials), demonstrating dual-channel cross-validation agreement across 21 test cases, confirming tamper detection across all FDT components, and measuring solver scalability to 500 constraints at sub-2 ms. These results provide empirical evidence that the proposed architecture is computationally feasible for real-time cyber-physical system integration.