A Practical Auditability Layer for AI Systems: A Lightweight Method for Traceable and Reproducible AI Reasoning

AI systems increasingly operate in regulated environments where decisions must be traceable, reproducible, and auditable. Large language models typically provide outputs without exposing the underlying reasoning, making it difficult to detect drift, inconsistency, or hallucinations. This preprint introduces a lightweight auditability layer that logs reasoning chains without modifying the underlying model. The method uses structured reasoning prompts, reasoning‑chain logging, and cross‑run consistency comparison to detect divergence and ensure stability. It is model‑agnostic, easy to implement, and suitable for enterprise workflows in clinical, financial, legal, and operational domains. Limitations and future extensions are included, such as semantic comparison of reasoning chains, multi‑model validation, and collapse‑vector‑based stability scoring. This work is part of the vW‑KG GL‑4 Continuum and contributes to practical AI safety, transparency, and governance.