Abstract:AI models are increasingly deployed in cloud-native environments to support scalable and automated services. However, while platforms such as Kubernetes provide strong infrastructure orchestration, security mechanisms specifically designed to protect deployed AI models remain limited. This paper presents security measures for AI models deployed in Kubernetes clusters. The proposed architecture integrates Kubeflow-based MLOps to automatically detect adversarial attacks during the inference phase and trigger defense mechanisms that preserve the model's accuracy and reliability. Specifically, a Fast Gradient Sign Method (FGSM) attack is applied at inference time, and a Projected Gradient Descent (PGD)-based adversarial training defense is automatically deployed when a degradation in accuracy is detected. The experimental results indicate that the deployed defense robustifies the model, significantly recovering accuracy relative to the degradation caused by the attack.
| Comments: | Accepted at the 1st Workshop on Secure and Intelligent Data Spaces (SIDS 2026), co-located with the 27th IEEE International Conference on Mobile Data Management (MDM 2026) |
| Subjects: | Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.15249 [cs.CR] |
| (or arXiv:2605.15249v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15249 arXiv-issued DOI via DataCite (pending registration) |
From: Stavros Bouras Mr [view email]
[v1]
Thu, 14 May 2026 12:45:36 UTC (2,840 KB)
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