verifiable audit trails

This work addresses the lack of traceable and tamper-resistant transparency mechanisms in large language models (LLMs) deployed in high-stakes decision-making contexts, which undermines accountability. To bridge this gap, the paper introduces the first LLM lifecycle auditing framework that integrates technical provenance with governance records. It proposes a reference architecture enabling cross-organizational traceability and implements a lightweight, open-source Python-based auditing layer. By leveraging append-only logs, event emitters, structured metadata, and an auditor interface, the system seamlessly integrates into existing LLM workflows with minimal intrusiveness. This design ensures complete, tamper-evident traceability across critical stages—including training, deployment, and monitoring—thereby facilitating robust accountability and responsibility attribution throughout the model’s lifecycle.

This study addresses the vulnerability of high-accuracy fraud detection models in enterprise settings, where insiders may tamper with logs or bypass approval workflows, thereby compromising audit integrity. To counter this, the authors propose a tamper-proof AI auditing system that uniquely leverages blockchain as an active enforcement layer. By anchoring explainable machine learning predictions and approval workflows atomically to a Layer-2 chain (e.g., Polygon) via smart contracts, the system ensures end-to-end immutability, verifiability, and compliance with GDPR. Empirical evaluation demonstrates strong performance: an F1 score of 0.895, a PR-AUC of 0.974, inference latency under 25 milliseconds, transaction costs below \$0.01 per operation, and scalability to support tens of thousands of monthly payments.

To address inherent deficiencies in log management for distributed systems—specifically tamper resistance, scalability, and real-time performance—this paper proposes a blockchain-based log auditing framework tailored for large-scale deployments. The approach employs a hybrid on-chain/off-chain architecture: on-chain smart contracts automate log validation, enforce fine-grained access control, and anchor cryptographic hash timestamps; off-chain logs are stored immutably in IPFS, with zero-knowledge proofs enabling privacy-preserving, regulatory-compliant verification. Experimental evaluation demonstrates sustained throughput exceeding 10⁴ logs per second, support for over ten million logs, end-to-end latency under 200 ms, and a 67% reduction in storage overhead. The framework fully satisfies audit requirements of China’s Multi-Level Protection Scheme (MLPS) Level 3 and the EU’s General Data Protection Regulation (GDPR).

Existing blockchain-based solutions struggle to support log integrity verification for resource-constrained IoT edge devices due to high consensus overhead and strong network dependencies. This work proposes a lightweight, ledger-free tamper-evident verification mechanism that employs a resource-aware adaptive chunking strategy for log batching, combined with a Merkle tree structure and O(log n) inclusion proofs to enable deterministic single-entry verification anchored to a trusted root. Experimental evaluation on 100,000 synthetic IoT log entries demonstrates a throughput exceeding 130,000 entries per second, with per-entry verification and proof generation latency of approximately 22 ms, an average proof size of 1,006 bytes, peak memory usage under 5 MB, and perfect precision, recall, and F1 scores (all 1.0) across tampering rates ranging from 1% to 50%.

Current AI-assisted scientific writing lacks auditable generation processes and mechanisms for accountability, undermining the verifiability of research credibility and compliance. This work proposes a novel auditing paradigm embedded directly within the production workflow, enforcing end-to-end traceability, immutability, and third-party reproducibility of AI involvement through preregistered blind-spot indicator cards, sealed execution environments, and automated gatekeeping intercepts. Core technical components include Git-sealed lineage anchoring, hash-bound provenance tracking, red-flag interception protocols, cross-model role isolation, and programmatic assembly. In experimental validation, one project was automatically terminated when preregistered confirmatory tests triggered a No-Go decision. An open-source toolkit is released to enable independent recomputation of all core audit metrics by third parties.

Existing automated approaches for mapping cyber threat intelligence (CTI) to MITRE ATT&CK lack supporting evidence, provenance tracking, and validation history, making their credibility difficult to assess. This work proposes the first knowledge graph–driven framework for CTI governance that enables auditable management of TTP assertions through fine-grained evidence preservation, complete provenance chains, versioned trust decisions, and lossless revocation mechanisms. The framework integrates multi-extractor collaborative verification, assertion aggregation, consensus modeling, and policy-driven validation, all underpinned by versioned knowledge graph management. Evaluated on 65 CTI reports comprising 5,303 sentences, the approach achieves a precision of 90.6% under six-party consensus and efficiently supports seven categories of audit queries concerning provenance, trustworthiness, and versioning.