Explainable AI-Enabled Adaptive Pre-Validation Framework for Trust-Oriented Security in Permissioned Blockchains
Abstract
Permissioned blockchain systems have emerged as a cornerstone for enterprise-grade distributed applications due to their controlled participation, high throughput, and deterministic consensus protocols. However, existing security mechanisms in such systems are still mostly static, based on pre-defined rules and deterministic validation logic that are not sufficient against evolving adversarial behaviours such as insider threats, transaction manipulation and stealthy anomaly patterns. This paper proposes a novel Explainable Artificial Intelligence (XAI) driven adaptive pre-validation framework, in which an intelligent dynamic decision-making layer is introduced before the blockchain transaction commitment. The framework proposes the usage of unsupervised anomaly detection (Isolation Forest), supervised ensemble classification (Random Forest), dynamic trust score, and explainability mechanisms (Shapley value-based attribution) for transparent and accountable transaction validation. Unlike traditional approaches, the system evaluates transactions in real time using behavioural patterns, prior trust, and contextual anomalies. The proposed framework provides a scalable, transparent, and adaptive security enhancement for enterprise blockchain environments by integrating explainable decision-making into the transaction validation process.