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Artificial intelligence infrastructures concentrate large volumes of sensitive data, proprietary models and strategic operational knowledge. Protecting these assets is challenging because AI workloads must process data in clear form during training and inference. While confidential computing technologies such as Trusted Execution Environments and confidential accelerators significantly improve the protection of data during execution, they do not by themselves provide full governance of security-critical operations across complex infrastructures. In a previous work we introduced the concept of Trusted Security Governance Platforms (TSGP): programmable trust anchors designed to enforce security policies and mediate critical operations across complex digital ecosystems. This paper explores the application of this architectural model to AI infrastructures deployed in cloud environments. We argue that while the TSGP concept is generic and not specific to artificial intelligence, the security challenges posed by modern AI systems make them a particularly relevant deployment scenario for security governance architectures. We also show that AI infrastructures exhibit structural properties that facilitate the deployment of governance architectures, including reduced command surfaces and more structured execution pipelines. Finally, we describe an incremental deployment path ranging from simple trusted execution anchors to fully mediated infrastructures implementing peripheral security governance.