AI-Powered Cybersecurity Governance: The Role of Business Analysts in Ethical AI Deployment


Authors : Comfort Claire Adaji; Alliy Adewale Bello; Chioma Emmanuela Ukatu; Nonso Okika; Olatoye Kabiru Agboola; Clifford Godwin Amomo

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/kmxrb64f

Scribd : https://tinyurl.com/3x7dkz66

DOI : https://doi.org/10.38124/ijisrt/25mar924

PlumX Metrics

Semantic Scholar

ResearchGate

IJISRT Repository

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

Abstract : Artificial intelligence (AI) has emerged as a key force in cybersecurity, including increased threat detection, automated response, and predictive analytics. However, as AI becomes more incorporated into cybersecurity systems, the ethical implications of its use must be carefully evaluated. Business analysts, who have historicsally served as liaisons between business stakeholders and technical teams, play an important role in ensuring that AI systems are implemented ethically within cybersecurity standards. This review examined the roles of business analysts in AI-powered cybersecurity governance, with an emphasis on assuring ethical AI deployment, legal compliance, and alignment with company values. Existing credible journals and materials were explored and investigated. Findings revealed that the roles that business analysts have to play in the deployment of ethical AI were critical. These included recognizing any ethical concerns connected to AI systems, creating plans to reduce these risks, and making sure rules and ethical standards are followed. Business analysts can also assist in bringing AI solutions into line with corporate principles and social norms by fostering stakeholder communication, which will advance accountability, transparency, and justice.

Keywords : AI-Powered, Cybersecurity, Governance, Business Analysts, Ethical AI.

References :

  1. Bibi, P. (2022). Artificial Intelligence in Cybersecurity: Revolutionizing Database Management for Enhanced Protection.
  2. Ibegbulam, C.M., Olowonubi, J.A., Fatounde, S.A., & Oyegunwa, O.A. (2023). Artificial intelligence in the era of 4ir: drivers, challenges and opportunities. Engineering Science & Technology Journal, 4(6), 473-488.
  3. Abrahams, T.O., Farayola, O.A., Kaggwa, S., Uwaoma, P.U., Hassan, A.O., & Dawodu, S.O. (2024). Cybersecurity awareness and education programs: a review of employee engagement and accountability. Computer Science & IT Research Journal, 5(1), 100-119.
  4. Rangaraju, S. (2023). Secure by Intelligence: Enhancing Products with AI-Driven Security Measures. EPH-International Journal of Science and Engineering, 9(3), 36-41.
  5. Shah, V. (2021). Machine Learning Algorithms for Cybersecurity: Detecting and Preventing Threats. Revista Espanola de Documentacion Cientifica, 15(4), 42-66.
  6. Kasowaki, L., & Emir, K. (2023). AI and Machine Learning in Cybersecurity: Leveraging Technology to Combat Threats (No. 11610).
  7. Csernatoni, R., & Mavrona, K. (2022). The Artificial Intelligence and Cybersecurity Nexus: Taking Stock of the European Union’s Approach. EU CYBER DIRECT. Testo disponibile al sito: https://eucd.s3.eu-central-1.amazonaws.com/eucd/assets/HAycHo-M/the-aiandcybersecurity-nexus-taking-stock-of-the-eu-s-approach.pdf (ultimo accesso 31/03/2023).
  8. Vaseashta, A. (2022). Nexus of advanced technology platforms for strengthening cyber-defense capabilities. In Practical applications of advanced technologies for enhancing security and defense capabilities: Perspectives and Challenges for the Western Balkans (pp. 14-31). IOS Press
  9. Bécue, A., Praça, I., & Gama, J. (2021). Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities. Artificial Intelligence Review, 54(5), 3849-3886.
  10. Nassar, A., & Kamal, M. (2021). Machine learning and big data analytics for cybersecurity threat detection: a holistic review of techniques and case studies. Journal of Artificial Intelligence and Machine Learning in Management, 5(1), 51-63.
  11. Marda, V. (2018). Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180087.
  12. Sontan, A.D., & Samuel, S.V. (2024). The intersection of Artificial Intelligence and cybersecurity: Challenges and opportunities. World Journal of Advanced Research and Reviews, 21(2), 1720-1736.
  13. Dawodu, S.O., Omotosho, A., Akindote, O.J., Adegbite, A.O., & Ewuga, S.K. (2023). Cybersecurity risk assessment in banking: methodologies and best practices. Computer Science & IT Research Journal, 4(3), 220-243.
  14. Sobana, S., Prabha, S.K., Seerangurayar, T., & Sudha, S. (2022). Securing future autonomous applications using cyber-physical systems and the Internet of Things. In Handbook of Research of Internet of Things and Cyber-Physical Systems (pp. 81-148). Apple Academic Press.
  15. Ghillani, D. (2022). Deep learning and artificial intelligence framework to improve the cyber security. Authorea Preprints.
  16. Paul, J. (2024). Ethical Implications of AI In Business.
  17. Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big data, 7, 1-29.
  18. Xuan, C. D., Duong, D., & Dau, H. X. (2021). A multi-layer approach for advanced persistent threat detection using machine learning based on network traffic. Journal of Intelligent & Fuzzy Systems, 40(6), 11311-11329.
  19. Kim, S., Park, K. J., & Lu, C. (2022). A survey on network security for cyber–physical systems: From threats to resilient design. IEEE Communications Surveys & Tutorials, 24(3), 1534-1573.
  20. Cheng, J., Yang, Y., Tang, X., Xiong, N., Zhang, Y., & Lei, F. (2020). Generative adversarial networks: A literature review. KSII Transactions on Internet and Information Systems (TIIS), 14(12), 4625-4647..
  21. Hu, Y., Kuang, W., Qin, Z., Li, K., Zhang, J., Gao, Y., ... & Li, K. (2021). Artificial intelligence security: Threats and countermeasures. ACM Computing Surveys (CSUR), 55(1), 1-36.
  22. Sarker, I. H., Furhad, M. H., & Nowrozy, R. (2021). Ai-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Computer Science, 2(3), 173.
  23. Amrollahi, M., Hadayeghparast, S., Karimipour, H., Derakhshan, F., & Srivastava, G. (2020). Enhancing network security via machine learning: opportunities and challenges. Handbook of big data privacy, 165-189.
  24. Fourati, F., & Alouini, M. S. (2021). Artificial intelligence for satellite communication: A review. Intelligent and Converged Networks, 2(3), 213-243.
  25. Egbuna, O.P. (2021). The Impact of AI on Cybersecurity: Emerging Threats and Solutions. Journal of Science & Technology, 2(2), 43-67.
  26. Aldahdooh, A., Hamidouche, W., Fezza, S.A. & Déforges, O. (2022). Adversarial example detection for DNN models: A review and experimental comparison. Artificial Intelligence Review, 55(6), 4403-4462.
  27. Dash, B., Sharma, P. & Ali, A. (2022). Federated learning for privacy-preserving: A review of PII data analysis in Fintech. International Journal of Software Engineering & Applications (IJSEA), 13(4).
  28. Citron, D.K. & Solove, D.J. (2022). Privacy harms. BUL Rev., 102, p.793.
  29. Thapa, C. & Camtepe, S. (2021). Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in biology and medicine, 129, p.104130.
  30. Hamon, R., Junklewitz, H., Sanchez, I., Malgieri, G. & De Hert, P. (2022). Bridging the gap between AI and explainability in the GDPR: towards trustworthiness-by-design in automated decision-making. IEEE Computational Intelligence Magazine, 17(1), 72-85.
  31. Tschider, C.A. (2020). Beyond the" Black Box". Denv. L. Rev., 98, p.683.
  32. Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A. & Baum, K. (2021). What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research. Artificial Intelligence, 296, p.103473.
  33. Fontes, C., Hohma, E., Corrigan, C.C. & Lütge, C. (2022). AI-powered public surveillance systems: why we (might) need them and how we want them. Technology in Society, 71, p.102137.
  34. Lescrauwaet, L., Wagner, H., Yoon, C. & Shukla, S. (2022). Adaptive legal frameworks and economic dynamics in emerging technologies: Navigating the intersection for responsible innovation. Law and Economics, 16(3), 202-220.
  35. Golbin, I., Rao, A.S., Hadjarian, A. & Krittman, D. (2020).  Responsible AI: a primer for the legal community. In 2020 IEEE international conference on big data (big data) (pp. 2121-2126). IEEE.
  36. Banik, S. & Dandyala, S.S.M. (2023). The Role of Artificial Intelligence in Cybersecurity Opportunities and Threats. International Journal of Advanced Engineering Technologies and Innovations, 1(04), pp.420-440.
  37. Mbah, G. O., & Evelyn, A. N. (2024). AI-powered cybersecurity: Strategic approaches to mitigate risk and safeguard data privacy.
  38. Adesoye, A. (2024). The role of sustainable packaging in enhancing brand loyalty among climate-conscious consumers in fast-moving consumer goods (FMCG). Int Res J Mod Eng Technol Sci, 6(3), 112-130.
  39. Forrester Research (2024). “Conducting Cybersecurity Gap Analyses.” https://forrester.com/cyber-gap
  40. El Mestari, S. Z., Lenzini, G., & Demirci, H. (2024). Preserving data privacy in machine learning systems. Computers & Security, 137, 103605.
  41. Tikkinen-Piri, C., Rohunen, A., & Markkula, J. (2018). EU General Data Protection Regulation: Changes and implications for personal data collecting companies. Computer Law & Security Review, 34(1), 134-153.
  42. Khan, F. & Mer, A. (2023). Embracing Artificial Intelligence Technology: Legal Implications with Special Reference to European Union Initiatives of Data Protection. In Digital Transformation, Strategic Resilience, Cyber Security and Risk Management (pp. 119-141). Emerald Publishing Limited.
  43. 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(10), p.1429.
  44. Larson, D.B., Magnus, D.C., Lungren, M.P., Shah, N.H. & Langlotz, C.P. (2020). Ethics of using and sharing clinical imaging data for artificial intelligence: a proposed framework. Radiology, 295(3), 675-682
  45. Walters, R. & Novak, M. (2021). Artificial Intelligence and Law. In Cyber Security, Artificial Intelligence, Data Protection & the Law (pp. 39-69). Singapore: Springer Singapore
  46. Aljeraisy, A., Barati, M., Rana, O. & Perera, C. (2021). Privacy laws and privacy by design schemes for the internet of things: A developer’s perspective. ACM Computing Surveys (Csur), 54(5), 1-38.
  47. Felzmann, H., Fosch-Villaronga, E., Lutz, C. & Tamò-Larrieux, A. (2020). Towards transparency by design for artificial intelligence. Science and Engineering Ethics, 26(6), 3333-3361
  48. Mamo, N., Martin, G.M., Desira, M., Ellul, B. & Ebejer, J.P. (2020). Dwarna: a blockchain solution for dynamic consent in biobanking. European Journal of Human Genetics, 28(5), 609-626.
  49. Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M. & Wirtz, J. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875-888.
  50. Char, D.S., Abràmoff, M.D. & Feudtner, C. (2020). Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics, 20(11), 7-17
  51. Wamba-Taguimdje, S.L., Fosso Wamba, S., Kala Kamdjoug, J.R. & Tchatchouang Wanko, C.E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 893-1924
  52. Selbst, A.D. (2021). An Institutional View of Algorithmic Impact. Harvard Journal of Law & Technology, 35(1).
  53. Richey Jr, R.G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M. & Dwivedi, Y.K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549
  54. Burr, C. & Leslie, D. (2023). Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies. AI and Ethics, 3(1), 73-98
  55. Brendel, A.B., Mirbabaie, M., Lembcke, T.B. & Hofeditz, L. (2021). Ethical management of artificial intelligence. Sustainability, 13(4), p.1974.
  56. Sama, L.M., Stefanidis, A. & Casselman, R.M. (2022). Rethinking corporate governance in the digital economy: The role of stewardship. Business Horizons, 65(5), 535-546.
  57. Gegenhuber, T. & Mair, J. (2024). Open social innovation: taking stock and moving forward. Industry and Innovation, 31(1), 130-157
  58. European Commission. (2024). “Guidelines for AI Governance.” https://ec.europa.eu/ai-governance
  59. Floridi, L. (2018). Soft ethics, the governance of the digital and the General Data Protection Regulation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180081.
  60. NIST (2024). Cybersecurity Framework. https://doi.org/10.6028/NIST.CSWP.29
  61. Taddeo, M., McCutcheon, T., & Floridi, L. (2019). Trusting artificial intelligence in cybersecurity is a double-edged sword. Nature Machine Intelligence, 1(12), 557-560.
  62. Leenen, L., Ramluckan, T., & van Niekerk, B. (2021). Impact of AI Regulations on Cybersecurity Practitioners. In ECCWS 2021 20th European Conference on Cyber Warfare and Security (p. 230). Academic Conferences Inter Ltd.
  63. Nguyen, M.T. & Tran, M.Q. (2023). Balancing security and privacy in the digital age: an in-depth analysis of legal and regulatory frameworks impacting cybersecurity practices. International Journal of Intelligent Automation and Computing, 6(5), 1-12.
  64. Ahmad, I.A.I., Anyanwu, A.C., Onwusinkwue, S., Dawodu, S.O., Akagha, O.V., & Ejairu, E. (2024). Cybersecurity challenges in smart cities: a case review of african metropolises. Computer Science & IT Research Journal, 5(2), 254-269.
  65. Obi O.C Ibrahim Ahmad I.A.., Akagha O.V., Dawodu S.O., Anyanwu A.C., & Onwusinkwue S. (2024). Comprehensive review on cybersecurity: modern threats and advanced defense strategies. Computer Science & IT Research Journal
  66. Mayr-Dorn, C., Vierhauser, M., Bichler, S., Keplinger, F., Cleland-Huang, J., Egyed, A. & Mehofer, T. (2021). Supporting quality assurance with automated process-centric quality constraints checking. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) (pp. 1298-1310). IEEE.
  67. Gudala, L., Shaik, M. & Venkataramanan, S. (2021). Leveraging machine learning for enhanced threat detection and response in zero trust security frameworks: An Exploration of Real-Time Anomaly Identification and Adaptive Mitigation Strategies. Journal of Artificial Intelligence Research, 1(2), 19-45.
  68. Mantelero, A., Vaciago, G., Samantha Esposito, M. & Monte, N. (2020). The common EU approach to personal data and cybersecurity regulation. International Journal of Law and Information Technology, 28(4), 297-328.
  69. Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., Eling, M., Goodloe, A., Gupta, J., Hart, C. & Jirotka, M., 2021. Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), 566 571.
  70. Bakumenko, A. & Elragal, A. (2022). Detecting anomalies in financial data using machine learning algorithms. Systems, 10(5), p.130
  71. Marotta, A. & Madnick, S., 2021. Convergence and divergence of regulatory compliance and cybersecurity. Issues in Information Systems, 22(1).
  72. Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97, 101804. https://doi.org/https://doi.org/10.1016/j.inffus.2023.101804
  73. Safitra, M.F., Lubis, M. & Fakhrurroja, H. (2023). Counterattacking cyber threats: A framework for the future of cybersecurity. Sustainability, 15(18), p.13369
  74. Anuyah, S., Singh, M. K., & Nyavor, H. (2024). Advancing clinical trial outcomes using deep learning and predictive modelling: Bridging precision medicine and patient-centered care. arXiv preprint arXiv:2412.07050.
  75. KPMG (2024). The Role of CISOs in Cybersecurity Governance. https://kpmg.com/ciso-governance
  76. Aslam, M. (2024). AI and cybersecurity: an ever-evolving landscape. International Journal of Advanced Engineering Technologies and Innovations, 1(1), 52-71.
  77. Al-Mansoori, S. & Salem, M.B. (2023). The role of artificial intelligence and machine learning in shaping the future of cybersecurity: trends, applications, and ethical considerations. International Journal of Social Analytics, 8(9), 1-16.
  78. OZDEN, C. (2023). AI ethical consideration and cybersecurity. International Studies in Social, Human and Administrative Sciences-I, 85.
  79. Vemuri, N., Thaneeru, N., & Tatikonda, V.M. (2023). Securing trust: ethical considerations in AI for cybersecurity. Journal of Knowledge Learning and Science Technology, 2(2), 167-175.
  80. Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A. & Hall, P. (2022). Towards a standard for identifying and managing bias in artificial intelligence. NIST special publication, 1270(10.6028).
  81. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35
  82. Fu, R., Huang, Y. & Singh, P.V. (2020). Ai and algorithmic bias: Source, detection, mitigation and implications. Detection, Mitigation and Implications (July 26, 2020).
  83. Nassar, A. & Kamal, M. (2021). Ethical Dilemmas in AI-Powered Decision-Making: A Deep Dive into Big Data Driven Ethical Considerations. International Journal of Responsible Artificial Intelligence, 11(8), 1-11
  84. Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, p.102551.
  85. Sarker, S. Janicke, H., Ferrag, M.A., & Abuadbba, A. (2024). Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions toward automation, intelligence and transparent cybersecurity modeling for critical infrastructures. Internet of Things, 101110.
  86. Wang, X. & Lo, K. (2021). Just transition: A conceptual review. Energy Research & Social Science, 82, p.102291.
  87. Sanni, O., Adeleke, O., Ukoba, K., Ren, J. & Jen, T.C. (2024). Prediction of inhibition performance of agro-waste extract in simulated acidizing media via machine learning. Fuel, 356, p.129527.
  88. Anamu, U.S., Ayodele, O.O., Olorundaisi, E., Babalola, B.J., Odetola, P.I., Ogunmefun, A., Ukoba, K., Jen, T.C. & Olubambi, P.A. (2023). Fundamental design strategies for advancing the development of high entropy alloys for thermo-mechanical application: A critical review. Journal of Materials Research and Technology.
  89. Yu, P.K. (2020). The algorithmic divide and equality in the age of artificial intelligence. Fla. L. Rev., 72, p.3.
  90. Yarger, L., Cobb Payton, F. & Neupane, B. (2020). Algorithmic equity in the hiring of underrepresented IT job candidates. Online information review, 44(2), 383-395.
  91. Adebukola, A. A., Navya, A. N., Jordan, F. J., Jenifer, N. J., & Begley, R. D. (2022). Cyber security as a threat to health care. Journal of Technology and Systems, 4(1), 32-64.
  92. Fichter, K., Lüdeke-Freund, F., Schaltegger, S. & Schillebeeckx, S.J. (2023). Sustainability impact assessment of new ventures: An emerging field of research. Journal of Cleaner Production, 384, p.135452.
  93. Seyi-Lande, O., & Onaolapo, C. P. (2024). Elevating Business Analysis with AI: Strategies for Analysts. International Journal of Management Research and Economics, 4(2), 1-17. doi: 10.51483/IJMRE.4.2.2024.1-17
  94. Qureshi, F. (2023). Navigating the Future: Harnessing Artificial Intelligence for Business Success. Journal Environmental Sciences And Technology, 2(1), 81-92.
  95. Bharadiya, J.P. (2023). The Role of Machine Learning in Transforming Business Intelligence. International Journal of Computing and Artificial Intelligence, 4(1), 16-24.
  96. Deiana, G., Dettori, M., Arghittu, A., Azara, A., Gabutti, G. & Castiglia, P. (2023). Artificial Intelligence And Public Health: Evaluating Chatgpt Responses To Vaccination Myths And Misconceptions. Vaccines, 11(7), 1217
  97. Janardhanan, P.S. (2020). Project Repositories For Machine Learning with Tensor Flow. Procedia Computer Science, 171, 188-196.
  98. Munawar, H.S., Qayyum, S., Ullah, F. & Sepasgozar, S. (2020). Big data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis. Big Data and Cognitive Computing, 4(2), 4.
  99. Mhlanga, D. (2021). Financial inclusion in emerging economies: The application of machine learning and artificial intelligence in credit risk assessment. International journal of financial studies, 9(3), 39. https://doi.org/10.3390/ijfs9030039
  100. Rane, N. (2023). ChatGPT and similar Generative Artificial Intelligence (AI) for building and construction industry: Contribution, Opportunities and Challenges of large language Models for Industry.
  101. Taranto-Vera, G., Galindo-Villardón, P., Merchán-Sánchez-Jara, J., Salazar-Pozo, J., Moreno-Salazar, A. & Salazar-Villalva, V. (2021). Algorithms and Software For Data Mining and Machine Learning: A Critical Comparative View From A Systematic Review of the Literature. The Journal of Supercomputing, 77, 11481-11513.

Artificial intelligence (AI) has emerged as a key force in cybersecurity, including increased threat detection, automated response, and predictive analytics. However, as AI becomes more incorporated into cybersecurity systems, the ethical implications of its use must be carefully evaluated. Business analysts, who have historicsally served as liaisons between business stakeholders and technical teams, play an important role in ensuring that AI systems are implemented ethically within cybersecurity standards. This review examined the roles of business analysts in AI-powered cybersecurity governance, with an emphasis on assuring ethical AI deployment, legal compliance, and alignment with company values. Existing credible journals and materials were explored and investigated. Findings revealed that the roles that business analysts have to play in the deployment of ethical AI were critical. These included recognizing any ethical concerns connected to AI systems, creating plans to reduce these risks, and making sure rules and ethical standards are followed. Business analysts can also assist in bringing AI solutions into line with corporate principles and social norms by fostering stakeholder communication, which will advance accountability, transparency, and justice.

Keywords : AI-Powered, Cybersecurity, Governance, Business Analysts, Ethical AI.

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe