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April 12, 2026Open Access

Explainable AI For Cybersecurity Decision-Making

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Authors

FSFarah Syazwani

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Overview

This review explores how explainable AI enhances threat detection in cybersecurity, suggesting improved decision-making and trust.

Key Points

  • The review aims to explore the integration of explainable AI techniques in cybersecurity to improve interpretability and trust.
  • Review of existing explainable AI methodologies in cybersecurity frameworks.
  • Analysis of feature attribution, model-agnostic explanations, and rule-based learning.
  • Discussion of regulatory compliance and ethical concerns in AI deployment.
  • Explainable AI techniques enhance interpretability in threat detection and incident response.
  • Barriers such as accuracy versus interpretability trade-offs and adversarial manipulation are identified.
  • Hybrid approaches and human-in-the-loop systems show promise for future cybersecurity advancements.

Cite This Study

Farah Syazwani (2021) studied this question.

synapsesocial.com/papers/69db38534fe01fead37c6880https://doi.org/10.5281/zenodo.19492116
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