A Risk-Aware Digital Twin Framework for Release-Readiness Validation of Multi-Tenant Zero Trust SASE Systems

Download PDF
Abstract: Release decisions for multi-tenant Zero Trust Secure Access Service Edge (SASE) platforms are difficult because a change that is acceptable in aggregate can violate a critical tenant's security objective under workload shift, missing telemetry, or configuration faults. This paper presents RA-DT, a risk-aware digital-twin framework that validates release readiness by replaying versioned release candidates against tenant-aware stress scenarios and issuing a joint risk certificate. The certificate combines criticality-weighted expected loss, unweighted aggregate loss, conditional value-at-risk (CVaR) over tenant-scenario cells, worst-tenant false-negative rate, and maximum scenario false-positive rate. To keep the evaluation reproducible and avoid claiming unavailable enterprise traces, real network-flow features and labels are taken from the public UNSW-NB15 dataset, while tenant assignments, criticality, release mutations, and stress scenarios are explicitly generated as fixed-seed synthetic context. A LightGBM detector is trained on 105,204 flows; 105 release candidates are evaluated in 12 pre-release twin scenarios and 12 independent held-out deployment scenarios, each replaying 6,000 flows. The twin readiness margin has a Spearman correlation of 0.978 with its deployment counterpart. At a matched pass count of 47 candidates, RA-DT reduces the unsafe-release escape rate from 20.4% for an aggregate-F1 gate to 2.04%, improves pass precision from 78.7% to 97.9%, and matches an expected-risk gate while providing explicit tail and tenant-level guarantees. The results support release certification as a multi-objective risk problem rather than a single-metric model test.
Keywords: Digital twin; Release readiness; SASE; Zero Trust; multi-tenancy; CvaR; Network intrusion detection.
APA Citation: Jianbo Ding, Dai Teng (2026). A Risk-Aware Digital Twin Framework for Release-Readiness Validation of Multi-Tenant Zero Trust SASE Systems. Transactions on Computer Science and Intelligent Systems Research, 13(1), 247-259. https://doi.org/10.62051/f19vvd53

References

  1. Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020, August). Zero trust architecture. NIST Special Publication 800 207. https://doi.org/10.6028/NIST.SP.800 207
  2. Chandramouli, R., & Butcher, Z. (2023, September). A zero trust architecture model for access control in cloud native applications in multi cloud environments. NIST Special Publication 800 207A. https://doi.org/10.6028/NIST.SP.800 207A
  3. Cybersecurity and Infrastructure Security Agency. (2023, April). Zero trust maturity model (Version 2.0).
  4. Ward, R., & Beyer, B. (2014). BeyondCorp: A new approach to enterprise security. ;login:, 39(6), 6–11.
  5. Syed, N. F., Shah, S. W., Shaghaghi, A., Anwar, A., Baig, Z., & Doss, R. (2022). Zero Trust Architecture (ZTA): A comprehensive survey. IEEE Access, 10, 57143–57179. https://doi.org/10.1109/ACCESS.2022.3174679
  6. Phiayura, P., & Teerakanok, S. (2023). A comprehensive framework for migrating to zero trust architecture. IEEE Access, 11, 19487–19511. https://doi.org/10.1109/ACCESS.2023.3248622
  7. Moubayed, A., Refaey, A., & Shami, A. (2019). Software Defined Perimeter (SDP): State of the art secure solution for modern networks. IEEE Network, 33(5), 226–233. https://doi.org/10.1109/MNET.2019.1800324
  8. DeCusatis, C., Liengtiraphan, P., Sager, A., & Pinelli, M. (2016). Implementing zero trust cloud networks with transport access control and first packet authentication. In Proceedings of the IEEE International Conference on Smart Cloud (pp. 5–10). https://doi.org/10.1109/SmartCloud.2016.22
  9. Cybersecurity and Infrastructure Security Agency. (2026, June). The journey to zero trust: Using secure access service edge in a modern TIC 3.0 solution.
  10. Almasan, P., Pastor, A., Barlet Ros, P., Yannuzzi, M., & Cabellos Aparicio, A. (2022). Network digital twin: Context, enabling technologies, and opportunities. IEEE Communications Magazine, 60(11), 22–27. https://doi.org/10.1109/MCOM.001.2200012
  11. Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
  12. Tao, F., & Zhang, M. (2017). Digital twin shop floor: A new shop floor paradigm towards smart manufacturing. IEEE Access, 5, 20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069
  13. Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems (pp. 85–113). Springer. https://doi.org/10.1007/978 3 319 38756 7_4
  14. Moustafa, N., & Slay, J. (2015). UNSW NB15: A comprehensive data set for network intrusion detection systems. In Proceedings of the Military Communications and Information Systems Conference (pp. 1–6). https://doi.org/10.1109/MilCIS.2015.7348942
  15. Moustafa, N., & Slay, J. (2016). The evaluation of network anomaly detection systems: Statistical analysis of the UNSW NB15 data set and the comparison with the KDD99 data set. Information Security Journal: A Global Perspective, 25(1 3), 18–31. https://doi.org/10.1080/19393555.2015.1125974
  16. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems 30 (pp. 3146–3154).
  17. Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value at risk. Journal of Risk, 2(3), 21–41. https://doi.org/10.21314/JOR.2000.038
  18. Duchi, J. C., & Namkoong, H. (2021). Learning models with uniform performance via distributionally robust optimization. The Annals of Statistics, 49(3), 1378–1406. https://doi.org/10.1214/20 AOS2004
  19. Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning (Vol. 70, pp. 1321–1330). PMLR.
  20. Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., & Lakshminarayanan, B. (2019). Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift. In Advances in Neural Information Processing Systems 32.
  21. Rabanser, S., Günnemann, S., & Lipton, Z. C. (2019). Failing loudly: An empirical study of methods for detecting dataset shift. In Advances in Neural Information Processing Systems 32.
  22. Saito, T., & Rehmsmeier, M. (2015). The precision recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLOS ONE, 10(3), e0118432. https://doi.org/10.1371/journal.pone.0118432
  23. Kazemian, P., Varghese, G., & McKeown, N. (2012). Header space analysis: Static checking for networks. In Proceedings of the 9th USENIX Symposium on Networked Systems Design and Implementation (pp. 113–126).
  24. Khurshid, A., Zou, X., Zhou, W., Caesar, M., & Godfrey, P. B. (2013). VeriFlow: Verifying network wide invariants in real time. In Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (pp. 15–27).
  25. Mai, H., Khurshid, A., Agarwal, R., Caesar, M., Godfrey, P. B., & King, S. T. (2011). Debugging the data plane with Anteater. In Proceedings of the ACM SIGCOMM (pp. 290–301). https://doi.org/10.1145/2018436.2018470
  26. Al Shaer, E., & Hamed, H. (2004). Discovery of policy anomalies in distributed firewalls. In Proceedings of the IEEE INFOCOM (Vol. 4, pp. 2605–2616).
  27. Jia, Y., & Harman, M. (2011). An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering, 37(5), 649–678. https://doi.org/10.1109/TSE.2010.62
  28. National Institute of Standards and Technology. (2012, September). Guide for conducting risk assessments. NIST Special Publication 800 30 Revision 1. https://doi.org/10.6028/NIST.SP.800 30r1
  29. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall.
  30. Ding, J., Shen, Z., & Liu, W. (2026). Game theoretic cost sensitive adversarial training for robust cloud intrusion detection against GAN based evasion attacks. Applied Sciences, 16(8), 3944. https://doi.org/10.3390/app16083944
  31. Zhao, W., Chen, T., Yang, J. S., & Qiu, L. (2026). AutoML Pipeline: A RAG enhanced code generation framework with pre validation for cloud native machine learning workflows. IEEE Access, 14, 41932–41945. https://doi.org/10.1109/ACCESS.2026.3673923
  32. Mo, T., Zhang, C., Zou, J., Guo, Z., & Rhee, M. (2026). Self evolving AI agents with dual memory for automated software testing and bug localization. IEEE Access, 14, 111086–111102. https://doi.org/10.1109/ACCESS.2026.3713401
  33. Wang, B., Wang, Z., Zhao, W., Zhang, F., & Shang, W. (2026). DRL Adapt: Deep reinforcement learning for adaptive routing convergence optimization in large scale networks. IEEE Open Journal of the Communications Society, 7, 849–863. https://doi.org/10.1109/OJCS.2026.3687441