Lifecycle-Integrated Security for AI-Cloud Convergence in Cyber-Physical Infrastructure
S M Zia Ur Rashid, Deepa Gurung, Sonam Raj Gupta, Suman Rath

TL;DR
This paper introduces a comprehensive lifecycle-based security framework for AI-Cloud convergence in cyber-physical systems, integrating multiple standards and demonstrating its effectiveness through a detailed case study.
Contribution
It synthesizes existing security and governance frameworks into a unified architecture and validates it with a practical case study on grid security.
Findings
Unified architecture covers data, model supply chain, and runtime governance.
Effective multi-layer defenses prevent complex cyber-physical attacks.
Framework aligns with multiple standards and regulatory requirements.
Abstract
The convergence of Artificial Intelligence (AI) inference pipelines with cloud infrastructure creates a dual attack surface where cloud security standards and AI governance frameworks intersect without unified enforcement mechanisms. AI governance, cloud security, and industrial control system standards intersect without unified enforcement, leaving hybrid deployments exposed to cross-layer attacks that threaten safety-critical operations. This paper makes three primary contributions: (i) we synthesize these frameworks into a lifecycle-staged threat taxonomy structured around explicit attacker capability tiers, (ii) we propose a Unified Reference Architecture spanning a Secure Data Factory, a hardened model supply chain, and a runtime governance layer, (iii) we present a case study through Grid-Guard, a hybrid Transmission System Operator scenario in which coordinated defenses drawn…
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Taxonomy
TopicsSmart Grid Security and Resilience · Adversarial Robustness in Machine Learning · Software-Defined Networks and 5G
