Integrating Artificial Intelligence and Distributed Ledger Technology for Enhanced Cybersecurity: Opportunities and Challenges

The integration of AI and DLT presents great opportunities and unique challenges in the realm of cybersecurity. This paper will examine how AI strengthens the processes of detection and response in cybersecurity, while DLT presents a decentralized and immutable premise for data management. The convergence of these two technologies may improve security for several sectors; finance, healthcare, and supply chain being prominent examples. AI's nature of being able to analyze large masses of data in the quest for threat anomaly detection complements DLT's decentralization and immutability as it affords more resilience in cybersecurity. Integration fosters technical and ethical challenges as well as regulations that need to be overcome in order for these technologies to be effective. This paper discusses the integrations of AI and DLT: Some of the best case studies and their future potential for augmenting cybersecurity protocols are also explained.

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Publication Details

Published
2025-05-07
DOI
https://doi.org/10.1109/aiit63112.2025.11082902
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
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article

Integrating Artificial Intelligence and Distributed Ledger Technology for Enhanced Cybersecurity: Opportunities and Challenges

Eng. Sana Abdulbaqi Al-Qubati
Network Security and Intrusion Detection
article

Integrating Artificial Intelligence and Distributed Ledger Technology for Enhanced Cybersecurity: Opportunities and Challenges

Eng. Sana Abdulbaqi Al-Qubati
article en

Abstract

The integration of AI and DLT presents great opportunities and unique challenges in the realm of cybersecurity. This paper will examine how AI strengthens the processes of detection and response in cybersecurity, while DLT presents a decentralized and immutable premise for data management. The convergence of these two technologies may improve security for several sectors; finance, healthcare, and supply chain being prominent examples. AI's nature of being able to analyze large masses of data in the quest for threat anomaly detection complements DLT's decentralization and immutability as it affords more resilience in cybersecurity. Integration fosters technical and ethical challenges as well as regulations that need to be overcome in order for these technologies to be effective. This paper discusses the integrations of AI and DLT: Some of the best case studies and their future potential for augmenting cybersecurity protocols are also explained.

Sana'a University (YE)
Openalex Percentile: Top 80%
Network Security and Intrusion Detection
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