A Hybrid Blockchain-Based Framework for Adaptive Cyber-Risk Prediction and Multi-Layer Threat Mitigation in Enterprise Networks

The environment of cybersecurity is changing at a higher rate than most automated defensive systems can keep pace with, and most enterprise-level solutions are based on a fixed set of rules or a black box with machine learning results. This leads to a loophole between identifying and controlling responses, particularly where the mitigation should demand accountability, proportionality, and justifiable reliability. Current AI–blockchain models enhance logging and detection and are seldom used to enforce adaptive, understandable, or risk-weighted response automation. It presents AGML, a hybrid governance-based defense framework that integrates blockchain mitigation execution with reinforcement-tuned prediction of cyber-risks. The system scores the risk continuously, mitigates severity depending on the situation, and recalculates behavior via a closed feedback mechanism. The blockchain layer is an enforcement boundary and not a passive ledger as all activities are auditable and not tamperable. The results of the evaluation show that there is a quantifiable increase in comparison with recent baselines: 96.48% detection accuracy, 95.22% precision, 94.65% recall, and a false-positive rate of 2.81. The average response latency was 312 ms and around 26 ms was due to governance validation. The system was also found to be stable in repeated adversarial cycles and exhibited stable convergence as opposed to drifting. These findings indicate that responsible and responsive automation, not rapid but uninhibited automation, could provide a more feasible solution to the resilient enterprise cybersecurity.