From Black Box to Trustworthy AI: A Secure Framework for Explainable Cybersecurity Decision-Making

The escalating amalgamation of Artificial Intelligence (AI) within the domain of cybersecurity has engendered notable progressions in the realms of threat identification and mitigation. Nevertheless, the widespread deployment of opaque black-box AI models, which inherently lack clarity regarding their decision-making methodologies, poses substantial difficulties for cybersecurity practitioners. This scholarly article seeks to address this particular constraint by introducing an innovative secure framework aimed at elucidating AI processes within the cybersecurity context. The proposed framework amalgamates various explainability techniques, including SHAP, LIME, and Grad-CAM, to elucidate the rationale underlying AI decisionmaking. Additionally, it integrates comprehensive security layers that encompass data safeguarding, adversarial mitigation, and model provenance to uphold the integrity and robustness of AI systems. Moreover, trust-enhancing mechanisms, which include thorough logging, human-in-the-loop supervision, and model certification, are incorporated to cultivate confidence in AIdriven cybersecurity determinations. This framework aspires to augment transparency and reliability in essential security applications such as malware identification, phishing remediation, and AI-facilitated Security Operations Centers (SOCs), thereby paving the path for a more credible and efficacious application of AI within the cybersecurity paradigm.