Explainable AI in Cybersecurity: A Comprehensive Framework for enhancing transparency, trust, and Human-AI Collaboration

Explainable AI (XAI) is transforming cybersecurity through increased openness, reliability, and teamwork among human analysts and AI tools. This comprehensive framework explores the application of XAI techniques like SHAP, LIME, and EBM to address the challenges of explainability and trustworthiness in AI-driven cybersecurity solutions. This paper tackles the core issue of limited clarity and explainability in conventional AI models for cybersecurity. This opacity can impede trust-building and productive teamwork between humans and AI systems.As a solution, this paper suggests incorporating Explainable AI (XAI) methodologies into cybersecurity structures. This would offer understandable explanations about how AI systems reach their conclusions. This empowers cybersecurity professionals to understand, validate, and effectively respond to cyber threats. The evaluation of XAI techniques against traditional cybersecurity models reveals superior performance, particularly in intrusion detection systems (IDS), phishing detection, incident response, vulnerability assessment, threat intelligence, anomaly detection, malware detection, SOAR, and user behavior analytics (UBA). This research not only strengthens cybersecurity defenses but also fosters a deeper understanding of AI's role in safeguarding digital ecosystems.