2026· International Conference on Conceptual Structures· pp. 104-119· 0 citations· 37 references
Computer Science
TL;DR
TrustChain-RAG is presented, an architecture born from five years of operating a multilingual knowledge pipeline for a Life Sciences organization, building on prior patents that combines multilingual Context Graphs with lattice-aligned RBAC, locally-hosted RAG, and a private blockchain audit trail.
BEAT (Blockchain–Edge AI Trust) is presented, which closes that gap through co-design of three coupled layers: a Graph Attention Network–Long Short-Term Memory (GAT-LSTM) edge module that converts device interaction graphs into normalised trust evidence vectors.
Pavansai Ramarao Maddali, Vijay Kumar Damera, Ratna Kumar Prathipati· International Journal of Adv...· 0 citations
This study examines the technological, financial, and regulatory obstacles of blockchain-based food traceability and proposes a new architecture called the Dynamic Trust-Weighted Oracle Consensus (DTW-OC) framework, which introduces a reputation-weighted, cross-validated oracle layer that scores every IoT sensor and human data source in real time and feeds that score into block-validator selection.
Shikha Singh· Journal of Intelligent Decis...· 0 citations
Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design.
Yayu Gao, Yong Xiao, Hao Hu et al.· IEEE Transactions on Cogniti...· 0 citations
Multi-cloud adoption has widened the enterprise attack surface to a degree that perimeter-based defence can no longer address. Traffic is now flowing continuously across AWS, Azure, and GCP, and the majority of deployed Zero Trust Architecture (ZTA) systems are still using static rule tables, with no ability to provide an audit trail of the reasoning behind decisions, and with logs stored in datastores that can be modified by an insider without detection. This paper proposes ZT-ChainGuard, a framework that overcomes these three limitations in one architecture that integrates an ensemble machine learning trust-scoring engine, ZTA policy enforcement and a blockchain-based audit trail. The trust-scoring engine is a two-layer stacking ensemble, with XGBoost and Random Forest as base learners, and Logistic Regression as a meta-learner, and it returns a continuous trust score, P(Attack | flow), for each network flow, which is then used to trigger the ZT policy decision at a threshold of 0.5. The explanation of each decision is provided by SHAP values at both the global and per-flow level, and each decision is stored as an immutable, SHA-256 hash-chained block. On CICIDS2017 (2.83 million flows, 14 attack classes) the framework achieves 99.90% accuracy, 99.71% F1-score, and 99.99% ROC-AUC; on ToN-IoT (2.23 million IoT records, 9 attack types) it achieves 99.81% accuracy, 99.88% F1-score, and 100% ROC-AUC. The latency of inferences is 0.006ms per sample, and the overhead of auditing the blockchain is 0.019ms per block. This performance is not just a quirk of a particular split, as it is shown to be stable across the three folds of three-fold cross validation.
V. K· Journal of Intelligent Decis...· 0 citations
Autonomous AI agents increasingly communicate with other agents, invoke tools, exchange intermediate results, and request human approvals. These workflows create a new auditability problem: organizations must reconstruct what happened, when it happened, which agent or human was involved, which control or policy applied, and whether records were modified afterwards. Motivated by the 2026 OpenAI/Hugging Face incident, this position and architecture paper proposes a product- and vendor-neutral black-box architecture for agentic processes. The architecture creates blockchain-anchored cryptographic commitments for selected agent communications, human-in-the-loop approvals, tool calls, and process artifacts without placing sensitive content on-chain. We define an evidence model that distinguishes temporal anchoring and artifact integrity from event ordering, capture authenticity, authorized anchoring, and causal traceability. The latter properties require additional architectural controls. We then discuss practical use for Governance, Risk, and Compliance (GRC), including compliance testing, risk-based evidence selection, monitoring evidence streams, incident reconstruction, and regulatory reporting readiness under the EU AI Act, NIS2, and the Cyber Resilience Act (CRA). This position and architecture paper does not present an empirical performance or security evaluation. The approach does not prevent agent misbehavior or prove semantic truth. Rather, it strengthens the evidentiary basis for later verification of critical process traces.
The growing nature of global supply chains has posed a great challenge in terms of providing a level of transparency, security, scalability, and privacy among the distributed logistics networks. Conventional supply chain management systems are based on the central databases that are susceptible to alteration of data, fraud and insufficient interoperability between the stakeholders. Despite the fact that blockchain technology has became a promising solution to enhance supply chain transparency and traceability, scalability, the high transaction latency, privacy leakage, and energy-consuming consensus mechanisms are current limitations with regard to the existing blockchain-based systems. This paper has provided a new framework to solve these problems which includes AI-driven Adaptive Sharded Blockchain with Zero-Knowledge Traceability (AASB-ZKT) of smart supply chain. The solution proposed combines the use of artificial intelligence-based anomalies detection, dynamically sharded blockchain, privacy protection based on zero-knowledge proofs, smart contracts powered by IoT, and opinion-based weighted consensus. The framework is meant to enhance the throughput of transactions, decrease the latency, increase fraud detection, and provide privacy without traceability. The empirical analysis conducted on the experimental basis proves that the suggested strategy is much more effective in enhancing the scalability, security, and operational efficiency in contemporary supply chain environments.
R. Sreekumar, M. Nidhi· International Conference on...· 0 citations
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