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Enhancing Causal AI Enabled Autonomous Cloud Security Intelligence Framework for Proactive Enterprise Cyber Defense Systems

Karthik Durai

Full Stack Developer, Optionality, Quebec, Canada

Abstract

The rapid adoption of cloud computing has significantly transformed enterprise information technology infrastructures while simultaneously expanding the cyber threat landscape. Traditional cloud security mechanisms often rely on reactive detection techniques that struggle to identify sophisticated and evolving cyberattacks in real time. The integration of Causal Artificial Intelligence (Causal AI) into autonomous cloud security intelligence frameworks provides a promising approach to improving proactive cyber defense capabilities by identifying cause-and-effect relationships rather than relying solely on statistical correlations. This study explores an enhanced Causal AI-enabled autonomous cloud security intelligence framework designed to strengthen enterprise cybersecurity through predictive threat detection, automated incident response, continuous risk assessment, and adaptive decision-making. The proposed framework integrates causal inference models, machine learning algorithms, cloud-native security tools, threat intelligence platforms, and automated orchestration technologies to establish a resilient cyber defense ecosystem. The research adopts a qualitative and conceptual methodology supported by extensive literature analysis to evaluate existing cloud security limitations and identify opportunities for intelligent automation. The findings suggest that incorporating causal reasoning significantly improves threat explainability, reduces false positives, accelerates response time, and enhances organizational cyber resilience. The study concludes that Causal AI represents a transformative advancement in autonomous cloud security by enabling enterprises to anticipate cyber threats, optimize security operations, and ensure continuous protection against increasingly sophisticated attacks

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0805006

Pages

14962-14671

Published

October 15, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Karthik Durai (%2025). Enhancing Causal AI Enabled Autonomous Cloud Security Intelligence Framework for Proactive Enterprise Cyber Defense Systems. International Journal of Science, Research and Technology , Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14962-14671. https://doi.org/10.15662/IJSRAT.2025.0805006

References

1. Potdar, A., Kodela, V., Srinivasagopalan, L. N., Khan, I., Chandramohan, S., & Gottipalli, D. (2025, July). Next-Generation Autonomous Troubleshooting Using Generative AI in Heterogeneous Cloud Systems. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-7). IEEE.
2. Chaganti, S. (2024). A unified MLOps and data architecture blueprint for cross-enterprise decisioning in global financial and tourism ecosystems. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 601–611. https://ijisae.org/index.php/IJISAE/article/view/7960
3. Hossain, M. B., Rahman, R., & Hoque, K. (2021). Feature-Driven Supervised Learning for Detecting DDoS Attack. International Journal of Science and Research Archive, 4(01), 393-402.
4. Meesala, A. (2024). Distributed securities pricing reconciliation at global scale: Price validation engine for financial institutions. World Journal of Advanced Research and Reviews, 21(2), 2212-2220.
5. 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.
6. Kanji, R. K. (2021). Federated data governance framework for ensuring quality-assured data sharing and integration in hybrid cloud-based data warehouse ecosystems through advanced ETL/ELT techniques. International Journal of Computer Techniques, 8(3), 1-9.
7. Gopinathan, V. R. (2023). Intelligent Cloud Security through Continuous Threat Detection and Risk Assessment. International Research Journal of Innovative Engineering, 7(6), 13571-13581.
8. Vollem, S. (2023). From reactive alerts to predictive intelligence: AI-assisted monitoring in modern cloud environments. International Journal of Research and Applied Innovations, 6(1), 8337-8345.
9. Adepu, R. (2024). AI-Driven Infrastructure Automation for Autonomous Cloud Operations and Fault Remediation. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 748-757.
10. Challa, R. (2023). Reliability Engineering for Zero-Downtime Integration of HPC Systems into Regulated Environments. International Journal of Research and Applied Innovations, 6(6), 10082-10092.
11. Soundappan, S. J. (2022). Blockchain Enabled Zero Trust Security Architecture for Cloud Native Distributed Applications. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12167.
12. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
13. Anand, L. (2025). Modernizing Enterprise Systems through Generative AI Autonomous Operations and Cloud-Native Engineering. International Journal of Humanities and Information Technology, 7(02), 54-69.
14. 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.
15. Rohit Wadhwa. (2024). Security and Data Integrity Challenges in Event-Driven MicroservicesBased Distributed Enterprise Systems. International Journal of Computer Science and Information Technology Research, 5(3), 59–73.
16. Mohammed, S. (2021). Hybrid cloud architecture strategy for global infrastructure operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(6), 4078-4081.
17. Chaba, A. (2020). A Reusable Enterprise Commerce Deployment Framework for Accelerated Digital Transformation. International Journal of Research and Applied Innovations, 3(2), 3068-3082.
18. Meesala, L. K. (2024). Agentic AI in cybersecurity: Dual-use dynamics, threat vectors, and governance imperatives. World Journal of Advanced Research and Reviews, 24(3), 3667-3672.
19. Panyala, V. R. (2024). Architecting autonomous cloud platforms with AI-driven self-optimization capabilities. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 10000-10003.
20. Onik, T. A., Irin, K. N., Azam, M. N., Akter, K. S., Nabil, M. A., Hossain, I., & Akter, S. (2023). Artificial Intelligence-Driven Early Detection of Neurological Disorders and Its Implications for Personalized Rehabilitation Strategies. Vascular and Endovascular Review, 6(2), 104-111.
21. Juvvadi, R. R. (2024). Embedding ESG and carbon accounting into the financial ledger: A sub-ledger architecture for CSRD-aligned reporting. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(1), 7531–7535.
22. Narayanan, S. (2023). Operationalizing artificial intelligence security in the cloud: A practical integration framework for enterprise risk management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
23. Seetala, S. R. (2023). Real-Time Data Monitoring Using Cloud Observability Tools: Architectures, Techniques and Emerging Practices. J Artif Intell Mach Learn & Data Sci, 6(4), 3367-3374.
24. 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.
25. Rajasekharan, R. (2024). Automation and DevOps in database management: Advancing efficiency, reliability, and innovation in modern data ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(5), 8821-8825.
26. Singh, I. K. (2025). Intelligent software validation frameworks for mission-critical enterprise applications using AI and knowledge graphs. International Journal of Research and Applied Innovations (IJRAI), 8(4), 12723–12735.
27. Anumula, S. K., Ponnarangan, S., Nujumudeen, F., Deka, M. N., Balamuralitharan, S., & Venkatesh, M. (2025). Intelligent Systems and Robotics: Revolutionizing Engineering Industries. arXiv preprint arXiv:2512.00033.
28. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
29. 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.
30. Gujarathi, M. (2024). Monolith-to-microservices migration in enterprise operations platforms: A workflow-oriented architecture. International Journal of Research Publications in Engineering, Technology and Management, 7(1), 10018–10029.
31. Vas, M. R. (2024). Predictive Analytics and AI-Driven Models for Intelligent Decision-Making in Cloud-Based Enterprises. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9234-9243.
32. Soundappan, S. J. (2023). AI-Driven Secure Enterprise Analytics and Intelligent Cloud Data Management Frameworks. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(3), 8236-8242.
33. 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.
34. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
35. Anand, L. (2025). Modernizing Enterprise Systems through Generative AI Autonomous Operations and Cloud-Native Engineering. International Journal of Humanities and Information Technology, 7(02), 54-69.
36. 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.
37. Gopinathan, V. R. (2023). Intelligent Cloud Security through Continuous Threat Detection and Risk Assessment. International Research Journal of Innovative Engineering, 7(6), 13571-13581.
38. 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.
39. Soundappan, S. J. (2022). Blockchain Enabled Zero Trust Security Architecture for Cloud Native Distributed Applications. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12167.
40. Mathew, A., & Federico, R. (2025). Phishing 2.0: leveraging cyber threat intelligence to combat deepfake-enhanced social engineering. Int. J. Innov. Res. Sci. Eng. Technol, 14(02).
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.