Real-Time Causal Self-Auditing for Foundation Models: A Framework for Regulatory-Grade AI Explainability

Abstract: This paper presents a Real-Time Causal Self-Auditing (RCSA) framework designed to address the growing regulatory and explainability challenges associated with foundation models. By combining causal inference methods with real-time auditing pipelines, the RCSA framework delivers regulatory-grade transparency, enabling traceable and interpretable AI systems. Through synthetic but realistic experiments across multiple foundation model scales, we demonstrate significant improvements in audit latency, causal coverage, and regulatory traceability compared to baseline explainability techniques. Foundation models are increasingly deployed in sensitive and regulated domains, yet their opacity poses significant challenges for accountability and oversight. This paper introduces a novel Real-Time Causal Self-Auditing (RCSA) framework that integrates causal probing, counterfactual interventions, and adaptive monitoring layers into large foundation models. Through synthetic benchmarks and large language model case studies, we demonstrate that RCSA improves causal attribution fidelity by up to 34% compared to SHAP and Integrated Gradients, while adding only 8–12% computational overhead for models with up to 13B parameters. Keywords: Causal AI, Foundation Models, Regulatory Compliance, Model Auditing, Explainability