Interpretability in cybersecurity refers to the degree to which a human can understand the internal workings and decision-making processes of an artificial intelligence (AI) or machine learning (ML) model. High interpretability means that security professionals can trace how an AI system arrives at its conclusions, which is crucial for trust, accountability, and compliance. This transparency helps in identifying biases, debugging errors, and ensuring that the model's actions align with organizational policies and ethical standards. In cybersecurity, interpretability is especially important for detecting threats, explaining automated responses, and maintaining regulatory compliance.