Transparent Threat Detection: Explainable AI-Driven Cybersecurity for Enhanced Trust and Accountability
Keywords:
Explainable AI (XAI), Cybersecurity Threat Detection, AI Transparency and Trust, Intrusion Detection Systems (IDS), Machine Learning in CybersecurityAbstract
Modern cyber threats now consist of complex adaptive threats demanding adopting robust security systems that can quickly detect threats. Standard AI cybersecurity solutions work properly but function independently, hindering professionals' ability to learn about threat detection methods. This research analyzes XAI's applications for cybersecurity and shows how XAI enhances cybersecurity by cementing trust relations, improving operational efficiency, and raising accountability standards. This paper employs simulation methods to demonstrate the security operational advantages of XAI-based threat detection while retaining regulatory compliance through real-time visual threat detection models. The research behind this study relies on established findings from AI security frameworks and hybrid systems that incorporate explainable cybersecurity elements.
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