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Cited by 3Information and Cyber SecurityInformation Technology Governance and StrategyNetwork Security and Intrusion Detection

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Adaptive Program Management Strategies for AI-Based Cyber Defense Deployments in Critical Infrastructure and Enterprise Digital Transformation Initiatives

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Adepoju, Adewumi Sunday·2025·International Journal of Research Publication and Reviews
DOI 10.55248/gengpi.6.0725.2651
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Abstract

As artificial intelligence (AI) becomes an integral component of modern cyber defense, its deployment in critical infrastructure and enterprise environments demands strategic agility and contextual responsiveness.Traditional static program management methodologies are insufficient for navigating the evolving threat landscape, the complexity of AI systems, and the velocity of cyber-physical integration.This paper presents a comprehensive examination of adaptive program management strategies specifically tailored to guide AI-based cyber defense deployments in high-stakes environments, including national infrastructure, utility grids, healthcare networks, and large-scale enterprise IT ecosystems.The discussion begins with a macro-level exploration of the digital transformation challenges facing critical sectors, highlighting increasing threat sophistication, regulatory pressures, and the convergence of operational technology (OT) with information technology (IT).It then narrows to the unique challenges posed by AI-enhanced cybersecurity tools such as model drift, adversarial attacks, and explainability gaps which necessitate continuous oversight and iterative governance models.Leveraging principles from agile, DevSecOps, and continuous delivery frameworks, this paper introduces a hybrid adaptive management framework that supports modular AI integration, real-time risk assessment, and cross-domain collaboration between cybersecurity, data science, and executive governance teams.The proposed model emphasizes continuous stakeholder feedback loops, dynamic threat modeling, and governance structures that evolve in tandem with AI system maturity.Case examples from energy, finance, and defense sectors are analyzed to contextualize best practices and lessons learned, illustrating how adaptive program governance can accelerate secure AI adoption without compromising system integrity or compliance mandates.This strategic lens enables decision-makers to future-proof their digital security investments while fostering resilience and operational continuity in mission-critical domains.

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Citations

Cites

Research works printed but not linked: 18

  1. Maharjan P. The role of artificial intelligence-driven big data analytics in strengthening cybersecurity frameworks for critical infrastructure. Global Research Perspectives on Cybersecurity Governance, Policy, and Management. 2023 Nov 7;7(11):12-25.
  2. Ramli A, Darus MY, Mohd Yussoff Y, Azni B. Integrated cybersecurity framework for enhanced threat detection and incident response in the digital era. Malaysian Journal of Computing (MJoC). 2025;10(1):2099-116.
  3. Varma AJ, Taleb N, Said RA, Ghazal TM, Ahmad M, Alzoubi HM, Alshurideh M. A roadmap for SMEs to adopt an AI based cyber threat intelligence. InThe effect of information technology on business and marketing intelligence systems 2023 Feb 9 (pp. 1903-1926). Cham: Springer International Publishing.
  4. Khan A, Jhanjhi NZ, Omar HA, Hamid DH, Abdulhabeb GA. Future Trends in Generative AI for Cyber Defense: Preparing for the Next Wave of Threats. InVulnerabilities Assessment and Risk Management in Cyber Security 2025 (pp. 135-168). IGI Global Scientific Publishing.
  5. Jamiu OA, Chukwunweike J. DEVELOPING SCALABLE DATA PIPELINES FOR REAL-TIME ANOMALY DETECTION IN INDUSTRIAL IOT SENSOR NETWORKS. International Journal Of Engineering Technology Research & Management (IJETRM). 2023Dec21;07(12):497-513.
  6. Adebowale OJ, Ashaolu O. Thermal management systems optimization for battery electric vehicles using advanced mechanical engineering approaches. Int Res J Mod Eng Technol Sci. 2024 Nov;6(11):6398. Available from: https://www.doi.org/10.56726/IRJMETS45888 · DOI
  7. De Azambuja AJ, Plesker C, Schützer K, Anderl R, Schleich B, Almeida VR. Artificial intelligence-based cyber security in the context of industry 4.0-a survey. Electronics. 2023 Apr 19;12(8):1920.
  8. Emmanuel Oluwagbade, Alemede Vincent, Odumbo Oluwole, Animashaun Blessing. LIFECYCLE GOVERNANCE FOR EXPLAINABLE AI IN PHARMACEUTICAL SUPPLY CHAINS: A FRAMEWORK FOR CONTINUOUS VALIDATION, BIAS AUDITING, AND EQUITABLE HEALTHCARE DELIVERY. International Journal of Engineering Technology Research & Management (IJETRM). 2023Nov21;07(11).
  9. Qudus L. Advancing cybersecurity: strategies for mitigating threats in evolving digital and IoT ecosystems. Int Res J Mod Eng Technol Sci. 2025 Jan;7(1):3185.
  10. Kezron IE. Novel cybersecurity framework for AI-driven drone integration by critical SMEs in economically distressed US rural communities: Advancing secure precision operations in high-risk environments. Well Testing Journal. 2025 Jul 10;34(S3):1-44.
  11. Aburub F, Almomani A. Data-Driven Cyber Defense Leveraging Business Intelligence. InComplexities and Challenges for Securing Digital Assets and Infrastructure 2025 (pp. 457-482). IGI Global Scientific Publishing.
  12. Ndibe OS. Ai-driven forensic systems for real-time anomaly detection and threat mitigation in cybersecurity infrastructures. International Journal of Research Publication and Reviews. 2025;6(5):389-411.
  13. Mahmud F, Barikdar CR, Hassan J, Goffer MA, Das N, Orthi SM, Hasan SN, Hasan R. AI-Driven Cybersecurity in IT Project Management: Enhancing Threat Detection and Risk Mitigation. Journal of Posthumanism. 2025 Apr 17;5(4):23-44.
  14. Faruk MI, Plabon FW, Saha US, Hossain MD. AI-Driven Project Risk Management: Leveraging Artificial Intelligence to Predict, Mitigate, and Manage Project Risks in Critical Infrastructure and National Security Projects. Journal of Computer Science and Technology Studies. 2025 Jun 12;7(6):123-37.
  15. KEZRON IE. Cybersecurity framework for securing cloud and AI-driven services in small and medium-sized businesses. Journal of Tianjin University Science and Technology. 2025;58(6).
  16. Arafah M, Al-Banna AK, Aladawi A. Cybersecurity in the Age of Digital Transformation: Protecting Assets, Infrastructure, and Innovation. InComplexities and Challenges for Securing Digital Assets and Infrastructure 2025 (pp. 265-290). IGI Global Scientific Publishing.
  17. Govea J, Gaibor-Naranjo W, Villegas-Ch W. Transforming cybersecurity into critical energy infrastructure: A study on the effectiveness of artificial intelligence. Systems. 2024 May 5;12(5):165.
  18. Kubilay B, Celiktas B. Relationships Among Organizational-Level Maturities in Artificial Intelligence, Cybersecurity, and Digital Transformation: A Survey-Based Analysis. IEEE Access. 2025 May 19.

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Metadata

Authors
Adepoju, A. S.
Corresponding author
Adepoju, Adewumi Sunday
Venue
International Journal of Research Publication and Reviews
Publisher
Genesis Global Publication
Published
2025-07
Volume
6
Issue
7
Pages
5599-5615
ISSN
2582-7421 (ISSN-L)
DOI
10.55248/gengpi.6.0725.2651
OpenAlex
W4412782652
Cited by
3
References
21
Citations by year
2026: 2 · 2025: 1
FWCI
3.13
Type
journal article
Language
en
Open access
Locations
International Journal of Research Publication and Reviews (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
critical infrastructure cybersecurity, enterprise digital transformation, AI governance, model drift, adversarial attacks, explainability, DevSecOps, continuous delivery, IT-OT convergence, energy sector, financial services, defense industry, regulatory compliance

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