Optimising blockchain security: Computational analysis of adaptive AI coaching

Blockchain networks face evolving security risks that require rapid and consistent responses from employees. This study presents an AI Coach that mirrors human reasoning through stages of context detection, world modeling, belief updating, preparation, execution, and feedback. In doing so, the AI Coach provides cognitive support. The architecture is defined by six types of matrices that include state connectivity, connectivity weights, combination functions, combination function parameters, speed factors, and initial values. In simulations of anomalous transactions, smart contract breaches, consensus delays, and unauthorized access, the AI Coach effectively prioritized critical events and guided response actions, demonstrating its ability to support more structured and efficient security workflows. These results underscore the effectiveness of the AI Coach in improving reliability and responsiveness in blockchain security monitoring.