The exponential proliferation of hyperscale generative artificial intelligence (AI) workloads has fundamentally altered the operational and security landscapes of national sovereign computing assets. Conventional Data Center Infrastructure Management (DCIM) frameworks rely on user-space Out-of-Band (OOB) polling protocols, introducing structural latency gaps. Furthermore, they lack automated, hardware-layer data destruction capabilities, exposing strategic assets to physical and logical extraction. In this paper, we present Sovereign-DCIM, a holistic, cross-domain architecture that uniquely unifies kernel-level observability, thermodynamic AI inference, and hardware-locked cryptographic enforcement. Unlike conventional approaches that rely on empirical approximations, we establish a mathematically rigorous, hardware-agnostic analytical framework. Operating within the Linux kernel, our eBPF telemetry engine mathematically bounds data acquisition complexity to O(1), effectively eliminating user-space latency overhead. To address physical thermal inertia, we integrate time-lagged Navier-Stokes fluid dynamics into a Physics-Informed Neural Network (PINN), ensuring deterministic predictive cooling without the risk of false-positive data purges. Crucially, our architecture enforces a DFA-based Two-Man Rule cryptographic state machine, formally proven to be resilient against OOB network replay attacks. Upon confirmed compromise, the system achieves an immutable security state by bypassing the Logical Volume Manager to execute nanosecond-regime NVMe IOCTL Crypto Erase on TCG OPAL 2.0 SEDs. Sovereign-DCIM establishes the definitive software-defined security baseline for the next generation of Sovereign AI infrastructure.
D.H. Lee (Sat,) studied this question.