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.
Narnaiezzsshaa Truong (Thu,) studied this question.