Generative AI–Enabled Decision Intelligence: An Integrated Analytics and Autonomous Systems Framework for Cybersecurity and Retail Enterprises
DOI:
https://doi.org/10.15662/IJEETR.2026.0801003Keywords:
Generative AI, Enterprise Analytics, Decision Intelligence, Cybersecurity Analytics, Retail Optimization, Predictive Modeling, AI Governance, Context-Aware SystemsAbstract
Enterprises operating in highly dynamic digital ecosystems face increasing pressure to make timely, accurate, and secure decisions across retail operations, cybersecurity management, and strategic governance. Traditional analytics systems, which rely primarily on historical and structured data, are no longer sufficient to address complex, context-aware decision requirements. This study proposes an integrated generative AI and analytics framework designed to enhance enterprise decision intelligence while simultaneously strengthening cybersecurity resilience and retail optimization capabilities. The framework unifies predictive analytics, generative AI models, and security-aware data pipelines to enable context-driven forecasting, anomaly detection, and automated decision support. By incorporating multivariate data sources such as consumer behavior, operational signals, threat intelligence feeds, and external contextual factors, the proposed approach enables enterprises to move from reactive decision-making to proactive and adaptive intelligence. The framework also emphasizes governance, fairness, and explainability to ensure trust and compliance in AI-driven systems. Through architectural analysis and methodological design, this research demonstrates how generative AI can augment enterprise analytics platforms to deliver scalable, secure, and actionable insights. The proposed model provides a unified foundation for retail demand optimization, enterprise operational intelligence, and cybersecurity risk mitigation, contributing to the development of next-generation intelligent enterprise systems.
References
1. Bishop, C. M. (2010). Pattern recognition and machine learning. Springer.
2. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
3. Karnam, A. (2025). Rolling Upgrades, Zero Downtime: Modernizing SAP Infrastructure with Intelligent Automation. International Journal of Engineering & Extended Technologies Research, 7(6), 11036–11045. https://doi.org/10.15662/IJEETR.2025.0706022
4. Ahmad, S. (2025). The Impact of Structured Validation and Audit Frameworks on the Fairness and Efficiency of AI-Driven Hiring Systems. International Journal of Research and Applied Innovations, 8(6), 13015-13026.
5. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
6. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
7. Vimal Raja, G. (2025). Context-Aware Demand Forecasting in Grocery Retail Using Generative AI: A Multivariate Approach Incorporating Weather, Local Events, and Consumer Behaviour. International Journal of Innovative Research in Science Engineering and Technology (Ijirset), 14(1), 743-746.
8. Khan, M. I. (2025). Big Data Driven Cyber Threat Intelligence Framework for US Critical Infrastructure Protection. Asian Journal of Research in Computer Science, 18(12), 42-54.
9. Pearl, J. (2010). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press.
10. Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.
11. Ferdousi, J., Shokran, M., & Islam, M. S. (2026). Designing Human–AI Collaborative Decision Analytics Frameworks to Enhance Managerial Judgment and Organizational Performance. Journal of Business and Management Studies, 8(1), 01-19.
12. Ananth, S., Radha, K., & Raju, S. (2024). Animal Detection In Farms Using OpenCV In Deep Learning. Advances in Science and Technology Research Journal, 18(1), 1.
13. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
14. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
15. Mittal, S. (2025). From attribution to action: Causal incrementality and bandit-based optimization for omnichannel customer acquisition in retail media networks. International Journal of Research Publications in Engineering, Technology and Management, 8(6), 13171–13181. https://doi.org/10.15662/IJRPETM.2025.0806021
16. Kusumba, S. (2025). Driving US Enterprise Agility: Unifying Finance, HR, and CRM with an Integrated Analytics Data Warehouse. IPHO-Journal of Advance Research in Science And Engineering, 3(11), 56-63.
17. Sharma, A., Chaudhari, B. B., & Kabade, S. (2025, July). Artificial Intelligence-Powered Network Intrusion Detection System (IDS) with Hybrid Deep Learning Approach in Cloud Environments. In 2025 IEEE North-East India International Energy Conversion Conference and Exhibition (NE-IECCE) (pp. 1-6). IEEE.
18. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
19. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
20. Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89.
21. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
22. Manda, P. (2025). Optimizing ERP resilience with online patching: A deep dive into Oracle EBS 12.2. x ADOP architecture. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 11786-11797.
23. Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. Journal of Strategic Information Systems, 27(1), 101–113.
24. Brundage, M., et al. (2018). The malicious use of artificial intelligence. AI & Society, 34(1), 1–15.
25. Christadoss, J., Panda, M. R., Samal, B. V., & Wali, G. (2025). Development of a Multi-Objective Optimisation Framework for Risk-Aware Fractional Investment Using Reinforcement Learning in Retail Finance. Futurity Proceedings, 3.
26. Lakshmi, A. J., Dasari, R., Chilukuri, M., Tirumani, Y., Praveena, H. D., & Kumar, A. P. (2023, May). Design and Implementation of a Smart Electric Fence Built on Solar with an Automatic Irrigation System. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 1553-1558). IEEE.
27. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E. (2025, September). Cloud-Based Extreme Learning Machines for Mining Waste Detoxification Efficiency. In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 1348-1353). IEEE.
28. M. R. Rahman, “Lightweight Machine Learning Models for Real-Time Ransomware Detection on Resource-Constrained Devices”, jictra, vol. 15, no. 1, pp. 17–23, Dec. 2025, doi: 10.51239/jictra.v15i1.348.
29. 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.
30. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
31. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
32. Rai, A., & Tiwana, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141.
