Improving Machine Learning Explainability for Automated Cybersecurity Decision Systems

Abstract

Rising reliance on machine learning (ML) algorithms in cybersecurity has led to major advancements in automated decision systems that enhance operational efficiency, threat identification, and response time. However, the "black-box" characteristic of many machine learning models makes it difficult to understand how decisions are acquired, which begs ethical issues around trust, accountability, and repercussions. Explainability of machine learning models helps to increase transparency in automated decision-making systems especially in cybersecurity applications where high-stakes judgments are made. Emphasizing post-hoc explanation tactics as well as model-intrinsic techniques, this study studies approaches to make machine learning models employed in cybersecurity more intelligible. Furthermore included in the paper are explainable artificial intelligence (XAI) effects on security specialists, regulatory compliance, and user trust. By means of trade-off analysis between explainability and performance, the article underlines the need of balanced solutions in cybersecurity environments.