LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are tools used to interpret machine learning models, including those in cybersecurity. They help explain which input features most influenced a model’s decision, making AI systems more transparent and trustworthy. LIME works by approximating the model locally with an interpretable one, while SHAP uses game theory to assign each feature an importance value for a particular prediction. These tools are crucial for understanding, debugging, and validating AI-driven cybersecurity solutions, ensuring that automated decisions can be trusted and audited. Their use helps organizations comply with regulations and detect potential biases or errors in AI systems.