• Shravani Shivaji Shitole
• Prof. Pritam Ramesh Ahire

As artificial intelligence (AI) increasingly integrates into cybersecurity systems, the need for transparency and trust in AI-driven decisions becomes critical. Explainable AI (XAI) seeks to address this by making the inner workings of AI models more interpretable and their outcomes more understandable to human users, including security analysts, IT administrators, and decision-makers. In cybersecurity, where the stakes involve detecting and mitigating threats in real time, opaque “black-box” AI systems may hinder timely and informed responses. XAI offers the potential to clarify how models identify malware, detect anomalies, or flag vulnerabilities, ensuring that AI tools complement human expertise rather than replace it. By providing insights into the decision-making process, XAI enhances trust, enables better compliance with security regulations, and improves accountability, making AI a more reliable partner in fortifying digital defenses. This paper explores the applications of XAI in cybersecurity, its advantages in fostering human-AI collaboration, and the challenges associated with balancing explainability, performance, and complexity in high-stakes environments

Explainable AI, Cybersecurity, XAI, Trust in AI, AI Transparency, Anomaly Detection, AI Interpretability, AI-Driven Threat Detection, Human-AI Collaboration, Black-box Models, AI Accountability, Cyber Threat Mitigation.

"Explainable Artificial Intelligence in CyberSecurity", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.f555-f563, November-2024, Available :http://www.jetir.org/papers/JETIR2411562.pdf

"Explainable Artificial Intelligence in CyberSecurity", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppf555-f563, November-2024, Available at : http://www.jetir.org/papers/JETIR2411562.pdf