Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Explainable Artificial Intelligence for Transparent Cybersecurity Decision-Making

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  • John Smith
John Smith
PhD, Associate Professor, Department of Computer Science, Stanford University, Stanford, CA, USA

Published 05-10-2024

Keywords

  • Explainable Artificial Intelligence,
  • Cybersecurity
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
J. Smith, “Explainable Artificial Intelligence for Transparent Cybersecurity Decision-Making”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 106–113, Oct. 2024, Accessed: Nov. 06, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/179

Abstract

As cyber threats continue to evolve in complexity and frequency, the need for effective cybersecurity solutions has never been more critical. In this context, the integration of Explainable Artificial Intelligence (XAI) into cybersecurity systems presents a transformative opportunity to enhance transparency and trust in automated decision-making processes. This paper discusses the significance of explainability in AI-driven cybersecurity solutions, emphasizing how XAI can bridge the gap between technical efficacy and user trust. By examining various XAI models and their application in high-stakes cybersecurity scenarios, this research underscores the potential for XAI to improve the interpretability of decision-making processes, thereby fostering greater confidence among stakeholders. The findings suggest that the deployment of XAI in cybersecurity not only enhances operational effectiveness but also aligns with ethical considerations, promoting responsible AI usage in sensitive domains.

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