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Research & review articles are invited for publication in September 2026 (Vol. 28, Issue 3) || Submission: up to 28th September || Editorial decision: within 48 hrs.

Artificial Intelligence for cybersecurity risk prioritization in complex digital ecosystems

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  • Artificial Intelligence for cybersecurity risk prioritization in complex digital ecosystems

Mofeoluwa John Olatunde *

Independent Researcher, United Kingdom.
 
Research Article
Global Journal of Engineering and Technology Advances, 2025, 20(01), 253-269.
Article DOI: 10.30574/gjeta.2024.20.1.0130
DOI url: https://doi.org/10.30574/gjeta.2024.20.1.0130
Received on 08 June 2024; revised on 22 July 2024; accepted on 25 July 2024
 
The rapid development of complex digital environments has increased cybersecurity vulnerabilities, and as a result, there is a need to incorporate sophisticated processes for risk prioritization. Artificial intelligence (AI) is becoming a game-changer, an instrument that not only provides unknown speed, scale, and precision in determining and prioritizing threats of possible harm. This paper discusses how AI can be applied to enhance risk prioritization in cybersecurity, its advantages and risks, governance requirements, and explores the future implications. Based on recent studies dating back to mid-2024, it focuses on predictive analytics models, end-to-end automated security systems, and autonomous risk management systems that encompass Security Operations Centers (SOC), Governance, Risk, and Compliance (GRC) operations, and cloud protection. Despite the ability of AI to mitigate risk proactively through the use of predictive modeling, AI has vulnerabilities that may lead to harm, which could take the form of adversarial attacks, data poisoning, or misfortunes resulting from overreliance. The article, which was recently published, emphasizes measures such as ethical governance, transparency, human oversight, and adherence to industry principles, including ISO 42001 and the NIST AI Risk Management Framework. It examines emergent technologies, such as multi-agent reinforcement learning, distributed threat intelligence systems, and the emerging predictive/preemptive means of addressing cybersecurity. As the process of AI integration into the existing security systems and the dynamics of the human-AI harmonious balancing have taken place, the ability of companies to be safe in the face of an accelerated change in cyber threats will increase. The conclusion highlights that AI has to augment human knowledge and has to be used as a force multiplier not a replacement and good governance is the key to safe and successful engagement. After all, the ability allowed by AI to prioritize risks has the potential not only to put the whole sphere of cybersecurity into a completely new realm, as reactive rather than predictive, but also enable businesses to better protect their more multi-faceted online environments.
 
Artificial Intelligence; Cybersecurity Risk Prioritization; Predictive Analytics; Multi-Agent Reinforcement Learning; Digital Ecosystem Security; Threat Intelligence Integration
 
https://gjeta.com/sites/default/files/fulltext_pdf/GJETA-2024-0130.pdf

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Mofeoluwa John Olatunde. Artificial Intelligence for cybersecurity risk prioritization in complex digital ecosystems. Global Journal of Engineering and Technology Advances, 2024, 20(1), 253-269. Article DOI: https://doi.org/10.30574/gjeta.2024.20.1.0130

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