Ethical Governance of AI-Driven Information Security: Balancing Data Privacy, Cyber Resilience, and Digital Trust in Organizations

Cyber threats represent a critical risk to global organisations, with cybercrime damages projected to exceed USD 10.5 trillion annually by 2025. Consequently, enterprises are rapidly adopting Artificial Intelligence (AI) to augment their security architectures, as traditional, rule based Security Information Systems (SIS) struggle to mitigate sophisticated, multi stage attacks. However, the application of AI in security raises significant ethical questions around data privacy, algorithmic transparency, and the balance between automated surveillance and civil liberties. As a conceptual paper, this study investigates how businesses can govern AI-Driven security solutions by bridging the theoretical divide between technical efficacy and ethical responsibility. To build this theoretical foundation, a systematic review of 32 peer reviewed articles was conducted using the PRISMA paradigm, synthesizing existing evidence across four core governance dimensions: technical performance, stakeholder accountability, regulatory compliance, and organisational process. Our conceptual analysis reveals a persistent "principles to practices gap"; while AI based SIS significantly outperform traditional systems in anomaly detection and incident response, these technological advancements have outpaced the operationalization of ethical norms within organisations. To address this gap, the paper proposes a novel, unified governance framework centred on digital trust. This model distinctly integrates the AI Trust Framework and Maturity Model (AI TMM), the Tiered Ethical Cybersecurity Model (TECM), and privacy preserving technologies such as federated learning to operationalize ethics by design. The article concludes with actionable policy pathways for legislators, organisational leaders, and researchers to increase cyber resilience while strictly respecting individual privacy rights.

AI-Driven Information Security, Technological Change, Cyber Law, Corporate Social Responsibility/Ethics, IT Management.

1. Ababneh, J., Alnemari, M. M., Attar, H., Alghamdi, H. A., Al Shawaheen, M., Saqarat, B., Abu Romman, L., & Iqtait, M. (2025). Cybersecurity Ethical Aspects (CEA). IEEE Access. https://doi.org/10.1109/ACCESS.2025.3641180 [Google Scholar] [Crossref]

2. Ali, G., Shah, S., & ElAffendi, M. (2025). Enhancing cybersecurity incident response: AI-Driven optimization for strengthened advanced persistent threat detection. Results in Engineering, 25, 104078. https://doi.org/10.1016/j.rineng.2025.104078 [Google Scholar] [Crossref]

3. Alnaabi, A. H, & Al Mahruqi, A.H. (2026). Artificial intelligence methods for enhancing cybersecurity in Oman: a comprehensive review. Journal of Cloud Computing, 15:48. https://doi.org/10.1186/s13677 026 00860 2 [Google Scholar] [Crossref]

4. Anil, V. K. S., & Babatope, A. B. (2024). The role of data governance in enhancing cybersecurity resilience for global enterprises. World Journal of Advanced Research and Reviews, 24(01), 1420–1432. https://doi.org/10.30574/wjarr [Google Scholar] [Crossref]

5. Barthwal, A., Campbell, M., & Shrestha, A. K. (2025). Privacy Ethics Alignment in AI: A Stakeholder Centric Framework for Ethical AI. Systems, 13, 455. https://doi.org/10.3390/systems13060455 [Google Scholar] [Crossref]

6. Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: themes, knowledge gaps and future agendas. Internet Research, 33(7), 133 167. https://doi.org/10.1108/INTR 01 2022 0042 [Google Scholar] [Crossref]

7. Chaudhary, G. (2024). Unveiling The black box Bringing Algorithmic transparency to AI. Masaryk University Journal of Law and Technology, 18(1), 95 122. https://scholar.google.com/scholar?q=Unveiling+The+black+box+Chaudhary [Google Scholar] [Crossref]

8. Dinu, A., et al. (2024). AI-Driven solutions for cybersecurity comparitive analysis and etical aspects. Revista Română de Informatică și Automatică (Romanian Journal of Information Technology and Automatic Control), 34(3), 35 48. https://doi.org/10.33436/v33i4y202304 [Google Scholar] [Crossref]

9. Emehin, O., Emeteveke, I., & Akanbi, I. (2024). Enhancing Cybersecurity with Safe and Reliable AI: Mitigating Threats While Ensuring Privacy Protection. International Journal of Computer Applications Technology and Research, 13(11), 1 12. https://doi.org/10.7753/IJCATR1311.1001 [Google Scholar] [Crossref]

10. George, A. S. (2025). Cyber Resilience in an AI-Driven World: A Strategic Framework. Partners Universal Innovative Research Publication (PUIRP), 3(6), 86 118. https://doi.org/10.5281/zenodo.18002783 [Google Scholar] [Crossref]

11. Iqbal, A. (2024). Digital Trust and Governance: Navigating Privacy and Innovation in the Data Driven Era. Journal of Management and Social Sciences Review, 2(2), 40 48. https://scholar.google.com/scholar?q=Ayesha+Iqbal+Digital+Trust+Governance [Google Scholar] [Crossref]

12. Jain, G., Singh, H., & Hashimy, L. (2025). Enhancing corporate governance with decentralized AI: a structuration theory perspective. Journal of Science and Technology Policy Management. https://doi.org/10.1108/JSTPM 10 2024 0425 [Google Scholar] [Crossref]

13. Jeon, K. H. (2026). Limitations and mitigation strategies for using generative artificial intelligence in medical writing: a narrative review. Journal of the Korean Medical Association, 69(2), 118. https://doi.org/10.5124/jkma.25.0163 [Google Scholar] [Crossref]

14. Kanchi, M., & Mittal, A. (2026). Trust, Privacy, and Risk Perception in AI Enabled Cyber Security Systems. 5th International Conference on Innovative Practices in Technology and Management (ICIPTM), 11465835. https://doi.org/10.1109/ICIPTM69057.2026.11465835 [Google Scholar] [Crossref]

15. Khawar, M., Khalid, S., Rehman, M. U., Usman, A., Al Malwi, W., & Asiri, F. (2026). Shaping the future of cybersecurity: The convergence of AI, quantum computing, and ethical frameworks for a secure digital era. Computer Science Review, 60, 100882. https://doi.org/10.1016/j.cosrev.2025.100882 [Google Scholar] [Crossref]

16. Korobenko, D., Nikiforova, A., & Sharma, R. (2024). Towards a Privacy and Security Aware Framework for Ethical AI: Guiding the Development and Assessment of AI Systems. 25th Annual International Conference on Digital Government Research (DGO 2024), 742 753. https://doi.org/10.1145/3657054.3657141 [Google Scholar] [Crossref]

17. Koukaras, C., Hatzikraniotis, E., Mitsiaki, M., Koukaras, P., Tjortjis, C., & Stavrinides, S. G. (2025). Revolutionising Educational Management with AI and Wireless Networks: A Framework for Smart Resource Allocation and Decision Making. Applied Sciences, 15, 5293. https://doi.org/10.3390/app15105293 [Google Scholar] [Crossref]

18. Levine, L. (2019). Digital Trust and Cooperation with an Integrative Digital Social Contract. Journal of Business Ethics, 160, 393–407. https://doi.org/10.1007/s10551 019 04201 z [Google Scholar] [Crossref]

19. Masud, M. T., Keshk, M., Moustafa, N., Linkov, I., & Emge, D. K. (2025). Explainable Artificial Intelligence for Resilient Security Applications in the Internet of Things. IEEE Open Journal of the Communications Society, 6. https://doi.org/10.1109/OJCOMS.2024.3413790 [Google Scholar] [Crossref]

20. Mbah, G. O., & Evelyn, A. N. (2024). AI powered cybersecurity: Strategic approaches to mitigate risk and safeguard data privacy. World Journal of Advanced Research and Reviews, 24(03), 310 327. https://doi.org/10.30574/wjarr [Google Scholar] [Crossref]

21. Muhammad, M. H. B., Abas, Z. B., Ahmad, A. S. B., & Sulaiman, M. S. B. (2024). AI-Driven Security: Redefining Security Informations Systems within Digital Governance. International Journal of Research and Innovation in Social Science (IJRISS), 8(9). https://doi.org/10.47772/IJRISS [Google Scholar] [Crossref]

22. Mylrea, M., & Robinson, N. (2023). Artificial Intelligence (AI) Trust Framework and Maturity Model: Applying an Entropy Lens to Improve Security, Privacy, and Ethical AI. Entropy, 25, 1429. https://doi.org/10.3390/e25101429 [Google Scholar] [Crossref]

23. Muhammad Sajid Nawaz, Muhammad Ahsan Raza, Binish Raza, Manal Ahmad and Farial Syed. “AI-Driven Intrusion Detection Systems for Securing IoT Healthcare Networks”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.6 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160647 [Google Scholar] [Crossref]

24. Pahune, S., Akhtar, Z., Mandapati, V., & Siddique, K. (2025). The Importance of AI Data Governance in Large Language Models. Big Data and Cognitive Computing, 9, 147. https://doi.org/10.3390/bdcc9060147 [Google Scholar] [Crossref]

25. Saeed, M. M., & Alsharidah, M. (2024). Security, privacy, and robustness for trustworthy AI systems: A review. Computers and Electrical Engineering, 119, 109643. https://doi.org/10.1016/j.compeleceng.2024.109643 [Google Scholar] [Crossref]

26. Shaffi, S.M., Vengathattil, S., Sidhick, J.N., & Vijayan, R. (2025). AI-Driven Security in Cloud Computing: Enhancing Threat Detection, Automated Response, and Cyber Resilience. ArXiv, abs/2505.03945. [Google Scholar] [Crossref]

27. Slesinger, I., Yalaz, E., Rizou, S., Gibin, M., Krasanakis, E., & Papadopoulos, S. (2024). Training in Co Creation as a Methodological Approach to Improve AI Fairness. Societies, 14, 259. https://doi.org/10.3390/soc14120259 [Google Scholar] [Crossref]

28. Taddese, A. A., & Chekole, A. (2025). Data stewardship and curation practices in AI based genomics and automated microscopy image analysis for high throughput screening: a scoping review. Human Genomics, 19:16. https://doi.org/10.1186/s40246 025 00716 x [Google Scholar] [Crossref]

29. Utami, R. D., & Aimin, W. (2026). Balancing Personalization, Privacy, and Value: A Systematic Literature Review of AI Enabled Customer Experience Management. Information, 17(2), 115. https://doi.org/10.3390/info17020115 [Google Scholar] [Crossref]

30. Xia, H., Chen, H., Zhang, J. Z., & Kamal, M. M. (2026). Exploring the impact of responsible AI governance on corporate performance: A quasi natural experiment. Technological Forecasting & Social Change, 223, 124425. https://doi.org/10.1016/j.techfore.2025.124425 [Google Scholar] [Crossref]

31. Xu, H., Seng, K. P., Ang, L. M., & Smith, J. (2024). Decentralized and Distributed Learning for AIoT: A Comprehensive Review, Emerging Challenges, and Opportunities. IEEE Access, 12, 101016 101051. https://doi.org/10.1109/ACCESS.2024.3422211 [Google Scholar] [Crossref]

32. Yan, Z., Susilo, W., Bertino, E., Zhang, J., & Yang, L. T. (2020). AI-Driven data security and privacy. Journal of Network and Computer Applications, 172, 102842. https://doi.org/10.1016/j.jnca.2020.102842 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study