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AI-Driven Cybersecurity Governance in Financial Services: Enhancing Ethical Auditing, Automated Compliance Monitoring and Explainable AI for Stakeholder Trust
Subject area: Science,Engineering and Technology · Area of research: Cybersecurity and Finance
Abstract
Artificial Intelligence (AI) governance has emerged as a critical field, requiring a balance between innovation, regulation, and ethical responsibility. This study examines the role of Explainable AI (XAI) in fostering transparency and trust in machine learning models while addressing security and fairness concerns. AI governance frameworks must navigate complex regulatory landscapes to ensure responsible deployment while mitigating biases that can influence decision-making processes. The integration of blockchain technology and federated learning presents promising solutions for data security and privacy preservation, although scalability and interoperability challenges remain. AI-driven cybersecurity strategies, including anomaly detection and defense against adversarial attacks, are essential for safeguarding financial institutions and other high-risk sectors. The study highlights the importance of ethical considerations in algorithmic decision-making, emphasizing principles such as fairness, accountability, and transparency. Findings suggest that adopting standardized XAI frameworks, strengthening regulatory policies, and investing in fairness-aware AI algorithms can enhance responsible AI governance. Future AI governance models should encourage interdisciplinary collaboration, promote research on AI risk mitigation, and ensure public trust through transparent and ethical AI applications.
Keywords
AI Governance, AI Risk Management, Cybersecurity In AI, Ethical AI, Explainable AI (XAI)
How to cite this paper
@article{1708005,
author = {Samuel Olatunji Olawore, Chukwuebuka Okoli, Oreoluwa Abimbola Serifat, Blessing Unwana Umoh; Michael Friday Okoli, Ugochukwu Daniel Ofurum; Adeniji, Omotayo Leo},
title = {AI-Driven Cybersecurity Governance in Financial Services: Enhancing Ethical Auditing, Automated Compliance Monitoring and Explainable AI for Stakeholder Trust},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {1198-1217},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708005.pdf},
abstract = {Artificial Intelligence (AI) governance has emerged as a critical field, requiring a balance between innovation, regulation, and ethical responsibility. This study examines the role of Explainable AI (XAI) in fostering transparency and trust in machine learning models while addressing security and fairness concerns. AI governance frameworks must navigate complex regulatory landscapes to ensure responsible deployment while mitigating biases that can influence decision-making processes. The integration of blockchain technology and federated learning presents promising solutions for data security and privacy preservation, although scalability and interoperability challenges remain. AI-driven cybersecurity strategies, including anomaly detection and defense against adversarial attacks, are essential for safeguarding financial institutions and other high-risk sectors. The study highlights the importance of ethical considerations in algorithmic decision-making, emphasizing principles such as fairness, accountability, and transparency. Findings suggest that adopting standardized XAI frameworks, strengthening regulatory policies, and investing in fairness-aware AI algorithms can enhance responsible AI governance. Future AI governance models should encourage interdisciplinary collaboration, promote research on AI risk mitigation, and ensure public trust through transparent and ethical AI applications.},
keywords = {AI Governance, AI Risk Management, Cybersecurity In AI, Ethical AI, Explainable AI (XAI)},
month = {April},
}