Towards trustworthy cybersecurity: Reclassifying insider threat detection using SHAP

Explainable artificial intelligence methods remain critical for trustworthy insider threat detection, yet existing approaches lack systematic frameworks for validating explanation quality against domain expertise. This research presents a SHAP (SHapley Additive exPlanations) based reclassification framework that integrates exact Shapley value computation with domain knowledge mapping to enhance detection accuracy whilst enabling transparent analyst oversight. The framework introduces a threat directions mapping that systematically translates feature attributions into cybersecurity interpretations, achieving high alignment with security analyst assessments across 27 behavioural indicators. Human-in-the-loop reclassification guided by automated explanation quality assessment demonstrates practical feasibility for operational deployment. Experimental evaluations with the help of 316,250 CMU CERT instances yield substantial accuracy improvements and significant false positive reduction compared to baseline classification. Statistical validation through paired t-tests confirms highly significant improvements ( p = 0 . 0023 , Cohen’s d = 0 . 368 ). A thorough comparative analysis demonstrates the strengths of our scheme: LIME (Local Interpretable Model-agnostic Explanations) provides computational efficiency for real-time response, whilst SHAP delivers mathematical rigour supporting forensic analysis and regulatory compliance. This work advances trustworthy artificial intelligence in cybersecurity through mathematically rigorous explanation frameworks enabling confident human oversight without sacrificing detection performance.