Human-Centered Cloud AI Collaboration Models for Sustainable and Secure Digital Enterprise Innovation
DOI:
https://doi.org/10.15662/IJEETR.2025.0705015Keywords:
Human-centered AI, cloud computing, digital enterprise innovation, sustainable AI, secure AI systems, Industry 5.0, collaborative intelligence, ethical AI, enterprise cloud architecture, AI governance, cybersecurity, explainable AI, human-AI collaboration, digital transformation, sustainable cloud infrastructureAbstract
Human-centered cloud artificial intelligence (AI) collaboration models are transforming modern digital enterprises by integrating human intelligence, cloud computing, machine learning, and collaborative automation into sustainable and secure innovation ecosystems. Enterprises increasingly adopt cloud AI systems to improve operational efficiency, enhance decision-making, strengthen cybersecurity, and support sustainable digital transformation. However, the rapid expansion of AI-driven cloud infrastructures also raises challenges concerning ethics, privacy, transparency, trust, workforce adaptation, governance, and environmental sustainability. Human-centered AI emphasizes the integration of human values, explainability, collaboration, and accountability into intelligent systems to ensure that technological innovation aligns with organizational and societal needs. This study examines the role of human-centered cloud AI collaboration frameworks in enabling secure and sustainable enterprise innovation. The paper explores the theoretical foundations of human-AI collaboration, cloud-native architectures, ethical governance, cybersecurity integration, and sustainability-oriented digital transformation. A comprehensive literature review identifies emerging trends, research gaps, and practical applications of collaborative AI systems in enterprise environments. The research methodology adopts a qualitative and conceptual research design supported by systematic literature analysis and framework evaluation. Findings indicate that human-centered cloud AI models significantly improve enterprise resilience, collaborative intelligence, innovation capability, operational sustainability, and adaptive decision-making. The study concludes that integrating ethical AI governance, explainable AI, cloud scalability, and human oversight is essential for creating trustworthy and sustainable digital enterprises in the Industry 5.0 era.
References
1. Bellisario, D., Martini, B., & Coletti, P. (2024). Human-centered and sustainable artificial intelligence in Industry 5.0: Challenges and perspectives. Sustainability, 16(13), 5448. https://doi.org/10.3390/su16135448
2. Chin, T., Ghouri, M. W. A., Jin, J., & Deveci, M. (2024). AI technologies affording the orchestration of ecosystem-based business models: The moderating role of AI knowledge spillover. Humanities and Social Sciences Communications, 11(496). https://doi.org/10.1057/s41599-024-03003-7
3. European Commission. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union.
4. Friedrich, J., Brückner, A., Mayan, J., Schumann, S., Kirschenbaum, A., & Zinke-Wehlmann, C. (2024). Human-centered AI development in practice—Insights from a multidisciplinary approach. Zeitschrift für Arbeitswissenschaft, 78, 359–376. https://doi.org/10.1007/s41449-024-00434-5
5. Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards transparency by design for artificial intelligence. Science and Engineering Ethics, 26, 3333–3361. https://doi.org/10.1007/s11948-020-00276-4
6. Polster, L., Bilgram, V., & Görtz, S. (2024). AI-augmented design thinking: Potentials, challenges, and mitigation strategies of integrating artificial intelligence in human-centered innovation processes. IEEE Engineering Management Review. https://doi.org/10.1109/EMR.2024.3512866
7. Ryan, M. (2024). We’re only human after all: A critique of human-centred AI. AI & Society. https://doi.org/10.1007/s00146-024-01976-2
8. Sudeeptha, I., Müller, W., Richter, A., & Leyer, M. (2024). Obstacles to human-AI collaboration. In Proceedings of the International Conference on Information Systems (ICIS 2024).
9. Truss, M., & Schmitt, M. (2024). Human-centered AI product prototyping with no-code AutoML: Conceptual framework, potentials and limitations. International Journal of Human–Computer Interaction, 41(15), 9304–9319. https://doi.org/10.1080/10447318.2024.2425454
10. Van der Vlist, F., Helmond, A., & Ferrari, F. (2024). Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence. Big Data & Society, 11(1). https://doi.org/10.1177/20539517241232630
11. Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 102544. https://doi.org/10.1016/j.ijinfomgt.2022.102544
12. Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
13. Zhang, Y., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford.
14. Narayanan, S. (2025). Autonomous cyber sovereignty: A dual-control architecture for agentic artificial intelligence in offensive defensive security ecosystems. World Journal of Advanced Research and Reviews, 25(3), 2538–2546.
15. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
16. Lanka, S. (2024). Redefining Digital Banking: ANZ’s Pioneering Expansion into Multi-Wallet Ecosystems. International Journal of Technology, Management and Humanities, 10(01), 33-41.
17. Gentyala, R. (2023). Beyond Syntax: A Framework for Semantically-Aware Verification Rules in Multi-Domain Data Cleansing. Journal of Scientific and Engineering Research, 10(3), 160-174.
18. Anand, L. (2024). AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(Special Issue 1), 5-12.
19. Vankayala, S. C. (2023). Observability-Driven QA for Serverless and PaaS Architectures: A Trace-Informed, SLO-Oriented Benchmarking Framework. International Journal of Science, Engineering and Technology, 11(5).
20. Bellundagi, M. (2023). Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039.
21. Mulla, F. A. (2024). Modern Mobile Testing Tools: A Comprehensive Guide to Quality Assurance and Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 10-32628.
22. Adepu, G. (2022). Machine learning-driven environmental monitoring systems for real-time regulatory compliance and risk detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 22–37.
23. Sengupta, J., & Alzbutas, R. (2022). Intracranial hemorrhages segmentation and features selection applying cuckoo search algorithm with gated recurrent unit. Applied Sciences, 12(21), 10851.
24. Mallireddy, S. (2024). Servicenow Create Enterprise Workflows for Various Digitalize Business Processes. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(4), 1-6.
25. Raja, G. V. (2023). AI Driven Secure Intelligent Framework for Fraud Detection Cybersecurity and Cloud Based Enterprise Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9068-9076.
26. Parupalli, A., & Pandya, S. (2022). Compliance-Driven Data Governance: A Survey on GDPR, and HIPAA in Cloud Databases. vol, 12, 828-836.
27. Bonthala, D. (2025). Telemetry Driven Cost Governance for Enterprise Data and AI Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 9361-9372.
28. Boddupally, H. L. (2024). Embedding Governance into LLM Workflow Architectures for Enterprise-Wide Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(7), 279-294.
29. Narayanan, S. (2024). Third-party AI vendor risk: Developing assessment frameworks for machine learning service providers. International Journal of Computer Science and Engineering and Information Technology, 10(4), 1133–1142. https://philarchive.org/archive/NARTAV
30. Soundappan, S. J. (2025). Privacy Preserving Data Analytics Frameworks using Homomorphic Encryption Techniques. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14531.
31. Appani, C. (2025). AI-powered threat detection in real-time payment systems. International Journal of Environmental Sciences, 11(19s), 22–27. https://doi.org/10.64252/9yf23877
32. Kasireddy, J. R. (2025). The ethical implications of AI in financial market surveillance: Are we over-monitoring traders? European Journal of Accounting, Auditing and Finance Research, 13(4), 17–36. https://doi.org/10.37745/ejaafr.2013/vol13n41736
33. Vankayala, S. C. (2021). Engineering Quality into Cloud-Native Financial Platforms on Microsoft Azure. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(1), 4361-4367.
34. Kunadi, S. K. (2022). Building scalable master data management systems for enterprise data platforms. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(2), 4830–4843.
35. Nagender Yamsani. (2017). Constructing Master Data to Be Auditable by Design: How Lineage Transparency and Change Discipline Are Engineered in Enterprise-Scale Data Estates. In International Journal of Science, Engineering and Technology (Vol. 5, Number 5). Zenodo. https://doi.org/10.5281/zenodo.18184902
36. Gopinathan, V. R. (2024). Secure explainable AI on Databricks–SAP cloud for risk-sensitive healthcare analytics and swarm-based QoS control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8452-8459.
37. Rahman, M. B., Yasin, M., & Ahmed, M. P. (2024). Data-Driven Population Health Analytics for Identifying High-Risk Groups and Health Disparities. American Journal Of Botany And Bioengineering, 1(11), 58-82.
38. Suvvari, S. K. (2023). Shift Left: Moving the Inclusion of Accessibility Functionalities to the Left in Agile Product Development Life Cycle. Journal of Computational Analysis and Applications, 31(4).
39. Sugumar, R. (2024). Next-generation security operations center (SOC) resilience: Autonomous detection and adaptive incident response using cognitive AI agents. International Journal of Technology, Management and Humanities, 10(02), 62-76.
40. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
41. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
42. Macha, Y., & Pulichikkunnu, S. K. (2023). An Explainable AI System for Fraud Identification in Insurance Claims via Machine-Learning Methods. Int. J. Adv. Res. Sci. Commun. Technol, 3(3), 1391-1400.
43. Vankayala, S. C. (2023). Observability-Driven QA for Serverless and PaaS Architectures: A Trace-Informed, SLO-Oriented Benchmarking Framework. International Journal of Science, Engineering and Technology, 11(5).
44. Dave, B. L. (2024). Driving Salesforce Testing Excellence with AI and Metadata-Driven Intelligent Automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10647-10655.
45. Balamuralidhar Sarabu, V. (2021). System-of-record governance in enterprise retail platforms: Architectural design principles for financial data ownership and consistency. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 1–16.
46. Adepu, R. (2022). Building secure multi-cloud infrastructure for mission-critical enterprise workloads. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 14–32.
47. Mali, R. K. (2023). A Scalable Microservice Framework for Multi-Modal Logistics Route Optimization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8382-8391.
48. Panda, S. S. (2023). Smart Machines, Smarter Outcomes the Rise of Self-Learning Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9004-9015.
49. Mathew, A., Jackson, E., & Tobesman, A. (2025). Agentic AI: A Game-Changer in Cybersecurity Defense. Science and Technology: Developments and Applications Vol. 7, 112-120.
50. Vankayala, S. C. (2021). Engineering Quality into Cloud-Native Financial Platforms on Microsoft Azure. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(1), 4361-4367.
51. Bonthala, D. (2025). Telemetry Driven Cost Governance for Enterprise Data and AI Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 9361-9372.





