Regulatory and Ethical Implications of Explainable AI in Cybersecurity: Navigating Compliance, Responsibility, and Trust

Explainable Artificial Intelligence (XAI) promises to enhance transparency in cybersecurity systems by providing human-interpretable justifications for AI-driven decisions. However, integrating XAI into high-stakes security environments presents multifaceted regulatory and ethical challenges. This paper explores how XAI intersects with major compliance frameworks such as HIPAA, GDPR, and NIST, and investigates the risks of “explanation overfitting” where misleadingly plausible explanations mask flawed decision logic. Furthermore, the paper examines ethical dilemmas surrounding the delegation of critical security decisions to autonomous systems. We argue that without rigorous governance, the opacity and perceived objectivity of AI explanations may foster overreliance and weaken accountability. Recommendations are provided to align XAI development with both regulatory expectations and ethical imperatives.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-30
DOI
https://doi.org/10.5281/zenodo.15680982
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
0.00
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article

Regulatory and Ethical Implications of Explainable AI in Cybersecurity: Navigating Compliance, Responsibility, and Trust

Zenodo (CERN European Organization for Nuclear Research)
Explainable Artificial Intelligence (XAI)
article

Regulatory and Ethical Implications of Explainable AI in Cybersecurity: Navigating Compliance, Responsibility, and Trust

article en

Abstract

Explainable Artificial Intelligence (XAI) promises to enhance transparency in cybersecurity systems by providing human-interpretable justifications for AI-driven decisions. However, integrating XAI into high-stakes security environments presents multifaceted regulatory and ethical challenges. This paper explores how XAI intersects with major compliance frameworks such as HIPAA, GDPR, and NIST, and investigates the risks of “explanation overfitting” where misleadingly plausible explanations mask flawed decision logic. Furthermore, the paper examines ethical dilemmas surrounding the delegation of critical security decisions to autonomous systems. We argue that without rigorous governance, the opacity and perceived objectivity of AI explanations may foster overreliance and weaken accountability. Recommendations are provided to align XAI development with both regulatory expectations and ethical imperatives.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 57%
Explainable Artificial Intelligence (XAI)
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