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Comparative analysis of cybersecurity artificial intelligence frameworks
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AI
DOI：10.1080/19393555.2025.2575773.png)
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Abstract
En 中文
This study addresses a critical gap in cybersecurity and artificial intelligence (AI) literature by conducting a systematic and comparative analysis of five key AI-focused cybersecurity frameworks: NIST AI RMF, MITER ATLAS, ENISA AI-TL, ISO/IEC 42,001:2023, and AI-SEC. Unlike prior works that remain largely conceptual, this research identifies and organizes six analytical dimensions (i.e. technical competence, risk management, ethical and legal compliance, governance and policy, human and organizational skills, and innovation and foresight) allowing a more structured and interdisciplinary evaluation. The analysis shows areas of convergence, particularly in technical safeguards and governance structures, while also revealing gaps in human capacity building and long-term monitoring of emerging threats. Innovative practices, such as adversarial simulations (i.e. MITER ATLAS, AI-SEC) and enhanced model deployment security, demonstrate promising directions for future development. The study makes two contributions. First, it advances academic understanding by providing a structured framework for evaluating AI-focused cybersecurity initiatives. Second, it provides practical insights for policymakers and organizational leaders, highlighting where existing frameworks succeed and where strategic improvements are necessary to ensure more resilient and ethically grounded cybersecurity practices.
Keywords:
Artificial intelligence
cybersecurity
lifecycle security
security frameworks
socio-technical impacts
Journal
I
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1.4
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39
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0