Improving cybersecurity through explainable artificial intelligence: a systematic literature review

The rapid adoption of artificial intelligence (AI) in cybersecurity has introduced significant challenges in terms of interpretability, trust, and regulatory compliance.This systematic literature review examines how Explainable AI (XAI) bridges the gap between advanced threat detection and human understanding by enhancing transparency in AI-driven security systems.The study synthesizes research across five key domains: technical foundations of XAI, human-AI collaboration, regulatory compliance, adversarial robustness, and scalability.Findings reveal that XAI techniques-such as Shapley Additive Explanations (SHAP) and attention mechanisms-improve analysts' trust and decision-making, while addressing biases and legal mandates, including the General Data Protection Regulation (GDPR).However, trade-offs between explainability and performance persist, necessitating future work on real-time XAI and the development of standardized evaluation metrics for this purpose.This review highlights XAI's transformative potential in developing resilient and accountable cybersecurity frameworks.