EIOC‑P: Profiling Dynamics extends the EIOC discipline into the frontier where human‑layer governance meets machine‑layer and model‑layer inference. Profiling is reframed as a multi‑substrate governance phenomenon: a system’s ability to read, classify, steer, reconstruct, or covertly leverage another system’s behavior. This preprint introduces a unified architecture for understanding profiling across four myth‑tech layers—Herald, Siren, Mirror, and Shadow—and across three operational substrates: human, machine, and model. The work formalizes profiling as both a drift amplifier and a drift detector, depending on who wields epistemic access and under what constraints. It provides a governance‑of‑governance structure for regulating profiling through eligibility logic, stewardship logic, and ritual logic. A set of public‑safe scenarios illustrates profiling dynamics in cybersecurity, AI/ML, and organizational contexts. EIOC‑P anchors the conceptual bridge between human‑layer EIOC and the emerging AI‑to‑AI landscape, where models infer, negotiate, and manipulate one another’s uncertainty patterns.