Auditability and regulatory compliance are increasingly required for deploying large language models (LLMs). Prior work typically targets isolated stages such as training or unlearning and lacks a unified mechanism for verifiable accountability across model updates. This paper presents AuditableLLM, a lightweight framework that decouples update execution from an audit-and-verification layer and records each update as a hash-chain-backed, tamper-evident audit trail. The framework supports parameter-efficient fine-tuning such as Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA), full-parameter optimization, continual learning, and data unlearning, enabling third-party verification without access to model internals or raw logs. Experiments on LLaMA-family models with LoRA adapters and the MovieLens dataset show negligible utility degradation (below 0.2% in accuracy and macro-F1) with modest overhead (3.4 ms/step; 5.7% slowdown) and sub-second audit validation in the evaluated setting. Under a simple loss-based membership inference attack on the forget set, the audit layer does not increase membership leakage relative to the underlying unlearning algorithm. Overall, the results indicate that hash-chain-backed audit logging can be integrated into practical LLM adaptation, update, and unlearning workflows with low overhead and verifiable integrity.

As the rapid evolution and expansion of Internet of Things (IoT) devices continues to accelerate, modern infrastructures face increasing cyber risks, largely driven by device inter-connectivity, limited security maturity, and interdependent attack propagation across networks. Traditional cyber insurance models often overlook these IoT-specific characteristics, relying on uniform or simplified risk assumptions that fail to capture real-world vulnerabilities. To address this gap, this paper presents a maturity-aware cyber insurance optimization framework tailored for interconnected IoT environments. The framework integrates organizational security maturity, interdependent risk propagation modeled through a modified Susceptible–Infected–Susceptible (SIS) process, and a Stackelberg game formulation that captures strategic interactions between the insurer and the defender. Through numerical studies on representative IoT topologies, we demonstrate that maturity-aware, risk-sensitive premium structures quantitatively outperform uniform pricing baselines in cost-efficiency and insurer sustainability. Specifically, our experimental results reveal that operating at an optimal intermediate maturity level ($M=3$) reduces the defender’s total expected cost by approximately $40\%$ (from $255.38$ k to $152.36$ k) compared to the baseline state ($M=1$). Furthermore, this structural hardening triggers an $88.3\%$ reduction in full-coverage insurance premiums (from $225.38$ k to $26.36$ k). In contrast, our uniform-pricing baseline exhibits reduced profitability in our experiments due to cross-subsidization effects, reinforcing the value of tiered, risk-proportional pricing for mitigating adverse-selection incentives. In summary, this work establishes a tractable, economically viable framework for cyber insurance in IoT ecosystems and provides a foundation for future extensions to richer network settings.