Neuro-Symbolic AI Architectures for Explainable Compliance and Cyber Risk Management in Multi-Cloud Systems
Abstract
The increasing adoption of multi-cloud environments has introduced significant challenges in regulatory compliance, cybersecurity risk assessment, policy enforcement, and explainability. Conventional artificial intelligence models can identify complex cyber threats and compliance anomalies, but their decision-making processes are often difficult to interpret, creating challenges for auditors, security teams, and regulatory authorities. This paper proposes a Neuro-Symbolic AI architecture for explainable compliance and cyber risk management in multi-cloud systems, integrating neural learning models with symbolic reasoning, formal security policies, compliance rules, knowledge graphs, and risk ontologies. The proposed approach combines machine learning for detecting behavioral anomalies and emerging threats with symbolic reasoning for validating decisions against regulatory requirements and organizational security policies. A unified knowledge representation layer is used to connect cloud assets, vulnerabilities, controls, threats, regulations, and business processes across heterogeneous cloud platforms. The methodology includes multi-cloud data collection, normalization, knowledge graph construction, neural threat detection, symbolic policy reasoning, explainability generation, and risk prioritization. The framework is designed to provide traceable evidence for automated decisions while supporting adaptive cyber risk management. By combining statistical learning with deterministic reasoning, the proposed architecture aims to improve detection accuracy, regulatory alignment, transparency, and operational decision-making. The research provides a structured foundation for developing trustworthy AI-driven security and compliance solutions for complex enterprise multi-cloud infrastructures.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 10 No. 5 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
18705-18715 |
Published |
October 9, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Carsten Riggelsen (%2025). Neuro-Symbolic AI Architectures for Explainable Compliance and Cyber Risk Management in Multi-Cloud Systems. International Journal of Emerging Trends in Engineering and Management Research , Vol. 10 No. 5 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 18705-18715. https://doi.org/10.15662/ijetemr.2025.1005008 |
References
Omi, M. S. H., Ara, J., Ali, M. M., Hoque, M. R., Ferdausi, S., Fatema, K., ... & Bijoy, M. H. I. (2025, July). Integrating Deep Neural Networks with Explainable AI for Precise Brain Tumor Detection and Classification. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN) (pp. 1-6). IEEE.
2. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
3. Raja, G. V. (2023). Modernizing enterprise systems using AI with machine learning and cloud computing for intelligent systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
4. Hoque, M. J., Hasan, M. M., Khatun, M. M., Akter, F., & Mohammad, A. R. (2021). Impact of COVID-19 on Consumer Buying Behavior During COVID-19 Pandemic Using Data Analytics. Journal of Business and Management Studies, 3(2), 296-307.
5. Kondapalli, K. K., Somajohassula, D. K., & Muppalla, L. K. (2022). Adaptive AI-orchestration and zero-trust security with federated threat intelligence for sustainable enterprise cloud architectures. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 12(1), 176-192.
6. Praneeth, P. (2022). Prediction of Cost Overruns in Solar EPC Projects Using Machine Learning Techniques: A Data-Driven Study in India. International Journal of Engineering Science & Humanities, 12(2), 71-85.
7. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
8. Nisar, K. (2024). Prompting, retrieval, and fine-tuning: Foundations of enterprise language model adaptation. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9310-9319.
9. 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.
10. Kundurthy, O. H., Kaata, S. K., Vikram, S., Somayajula, R., & Gangavarapu, R. (2025, September). A Framework for Lightweight Generative AI: Enabling Secure, Scalable, and Cloud-to-Edge Intelligence with MicroLLMs. In 2025 International Conference on Electronics and Computing, Communication Networking Automation Technologies (ICEC2NT) (pp. 1-8). IEEE.
11. Vemireddy, S. (2022). Modernizing enterprise financial platforms through distributed cloud architectures. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7420–7426.
12. Patel, C. (2024). AI-driven recommendation systems for improving online customer journey. International Journal of Current Engineering and Technology, 14(6), 549–556.
https://doi.org/10.14741/ijcet/v.14.6.18
13. 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.
14. Beeram, S. (2023). AI-driven zero trust identity security in Microsoft Azure: Adaptive risk-based access using Microsoft Entra ID. International Journal of Science, Research and Technology (IJSRAT), 6(5), 10708–10713.
15. Mohan, A. (2025). Causal inference in data science: A framework for attribution systems. European Journal of Computer Science and Information Technology, 13(36), 107–113.
16. Yepuri, V. K., Polamarasetty, V. K., Donthi, S., & Gondi, A. K. R. (2023). Containerization of a polyglot microservice application using Docker and Kubernetes.arXiv preprint arXiv:2305.00600
17. 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.
18. Tyagi, N. (2024). Deep reinforcement learning for algorithmic trading strategies. International Journal of Research and Applied Innovations, 7(2), 10415-10422.
19. Bandaru, P. K. (2022). Hardware-in-the-loop testing for connected vehicles: Enhancing software reliability through continuous validation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4645–4651.
20. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
21. Narra, R. (2024). A survey on scalable feature engineering techniques for cloud-native machine learning workflows. International Journal of Advanced Research in Science, Communication and Technology, 4(4), 664–677.
22. Das, P., Gogineni, A., Patel, K., & Jain, A. (2025, May). Integration of IoT and Machine Learning to Monitor the Air Quality by Measuring the Block Carbon Levels. In 2025 International Conference on Engineering, Technology & Management (ICETM) (pp. 1-6). IEEE.
23. Padmanabham, S. (2022). Enterprise identity and access management architecture for large financial institutions. International Journal of Research and Applied Innovations, 5(1), 9486–9490.
24. 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.
25. Karakondu, M., Jambagi, G., & Tatavarthi, S. (2024). Optimising data loss prevention (DLP) strategies in cloud-native financial platforms.
26. Narra, S. L. (2025). The Future of Endpoint Security: Autonomous Agents and Self-Healing Systems. Journal Of Multidisciplinary, 5(7), 109-117.
27. Seetharaman, K. M. R. (2025, May). Predicting Cryptocurrency Price Movements Using Leveraging Machine Learning Algorithms. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 1497-1502). IEEE.
28. Chaba, A. (2023). A scalable real-time customer data platform architecture for cross-channel enterprise personalization. International Journal of Research and Applied Innovations, 6(1), 8392–8396.
29. Challa, R. (2025). Architecting GPU-accelerated supercomputing for real-time clinical AI in large hospital systems. Computer Fraud & Security, 2025(2), 2134–2144.
30. Anand, L. (2023). Machine Learning Enabled Enterprise Integration through Intelligent API Governance Secure Cloud Infrastructure and Automated Operations. International Journal of Research and Applied Innovations, 6(3), 5972-5979.
31. Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Science, Research and Technology (IJSRAT), 7(3), 12085–12101.
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. Koganti, H. (2024). Beyond reactive scaling: A review of AI-driven proactive and context-aware auto-scaling in cloud-edge environments. International Journal of Engineering & Extended Technologies Research, 6(3), 8175–8183.
34. Himeluzzaman, M., Alam, A., Gazi, M. S., Abdullah, S. M., Chy, M. S. K., Onik, T. A., ... & Shakil, S. M. (2025). Countering AI-Generated Disinformation: A Novel Detection Model to Safeguard National Security. International Journal of Computer Technology and Electronics Communication, 8(4), 11192-11203.
35. Pawlicki, M., Pawlicka, A., Kozik, R., & Choras, M. (2024). Advanced insights through systematic analysis: Mapping future research directions and opportunities for xAI in deep learning and artificial intelligence used in cybersecurity. Neurocomputing, 590, 127759. https://doi.org/10.1016/j.neucom.2024.127759