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Abstract

The global AI industry has passed an inflection point. What began as a set of experimental services hosted on shared cloud infrastructure has evolved, within a remarkably short period, into the operational backbone of financial systems, healthcare platforms, energy management, defense operations, and national economic competitiveness. Yet the governance frameworks, security controls, and regulatory regimes that surround this infrastructure have not kept pace. The gap between deployment velocity and protective oversight now represents one of the most consequential systemic risks in the contemporary threat landscape. This whitepaper examines three interlocking dimensions of that risk. First, AI workloads have created a fundamentally new and expanding attack surface in cloud environmentsone that extends beyond the traditional concerns of data confidentiality and access control into the integrity of model weights, the security of inference pipelines, the trustworthiness of training data, and the reliability of autonomous agent behavior. Second, the infrastructure supporting global AI is highly concentrated: three hyperscalers collectively host the majority of deployed AI workloads, and a small number of model repositories, compute providers, and AI platform vendors represent single points of failure for organizations worldwide. Third, the emergence of AI as both a weapon and a target in offensive cyber operations has introduced a qualitatively new threat dynamicone in which the compromised AI system is not merely a data breach but a potential force multiplier for adversaries.