This paper introduces a comprehensive study fo-cused on prominent cyber resilience methods. Resilience is defined as a system's capacity to function despite attacks or breaches. Our aim is to highlight techniques that guarantee sys-tem security and performance, even in compromised conditions. While detecting intrusions and attacks is vital, there is an absence of methods addressing accountability and system robustness. By merging monitoring with explainability and resilience methods, as well as AI techniques, we foster intrusion detection and create protective strategies. Our resilience model emphasizes self-repair and introspection, drawing inspiration from techniques like Moving Target Defence (MTD). We provide real-world examples to demonstrate anomaly detection, explainability and system robustness, including the detection of autonomous vehicle communication anomalies, explainable AI techniques, and an electric vehicle charging scenario.