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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2505.23805 |
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| _version_ | 1866912403453640704 |
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| author | Sheriff, Akram Huang, Ken Nemeth, Zsolt Nakhjiri, Madjid |
| author_facet | Sheriff, Akram Huang, Ken Nemeth, Zsolt Nakhjiri, Madjid |
| contents | This paper introduces the Adaptive Defense Agent (ADA), an innovative Automated Moving Target Defense (AMTD) system designed to fundamentally enhance the security posture of AI workloads. ADA operates by continuously and automatically rotating these workloads at the infrastructure level, leveraging the inherent ephemerality of Kubernetes pods. This constant managed churn systematically invalidates attacker assumptions and disrupts potential kill chains by regularly destroying and respawning AI service instances. This methodology, applying principles of chaos engineering as a continuous, proactive defense, offers a paradigm shift from traditional static defenses that rely on complex and expensive confidential or trusted computing solutions to secure the underlying compute platforms, while at the same time agnostically supporting the latest advancements in agentic and nonagentic AI ecosystems and solutions such as agent-to-agent (A2A) communication frameworks or model context protocols (MCP). This AI-native infrastructure design, relying on the widely proliferated cloud-native Kubernetes technologies, facilitates easier deployment, simplifies maintenance through an inherent zero trust posture achieved by rotation, and promotes faster adoption. We posit that ADA's novel approach to AMTD provides a more robust, agile, and operationally efficient zero-trust model for AI services, achieving security through proactive environmental manipulation rather than reactive patching. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes Sheriff, Akram Huang, Ken Nemeth, Zsolt Nakhjiri, Madjid Cryptography and Security Artificial Intelligence This paper introduces the Adaptive Defense Agent (ADA), an innovative Automated Moving Target Defense (AMTD) system designed to fundamentally enhance the security posture of AI workloads. ADA operates by continuously and automatically rotating these workloads at the infrastructure level, leveraging the inherent ephemerality of Kubernetes pods. This constant managed churn systematically invalidates attacker assumptions and disrupts potential kill chains by regularly destroying and respawning AI service instances. This methodology, applying principles of chaos engineering as a continuous, proactive defense, offers a paradigm shift from traditional static defenses that rely on complex and expensive confidential or trusted computing solutions to secure the underlying compute platforms, while at the same time agnostically supporting the latest advancements in agentic and nonagentic AI ecosystems and solutions such as agent-to-agent (A2A) communication frameworks or model context protocols (MCP). This AI-native infrastructure design, relying on the widely proliferated cloud-native Kubernetes technologies, facilitates easier deployment, simplifies maintenance through an inherent zero trust posture achieved by rotation, and promotes faster adoption. We posit that ADA's novel approach to AMTD provides a more robust, agile, and operationally efficient zero-trust model for AI services, achieving security through proactive environmental manipulation rather than reactive patching. |
| title | ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2505.23805 |