Regulated institutions deploying AI face a structural evidence problem: the gap between what their systems claim to do and what they can prove happened at the level of an individual decision. Existing compliance infrastructure addresses this problem observationally — monitoring, dashboards, retrospective reporting — but does not close it structurally. This specification defines the Operating Model Protocol (OMP™), a deterministic routing invariant that classifies every interaction in a regulated operation to exactly one of three outcome states — AUTONOMOUS, ASSISTED, or ESCALATED — and generates a tamper-evident audit trace at the point of every decision. The routing decision is a deterministic function of a composite Confidence Score, Watchtower enforcement evaluations, and domain-specific thresholds. Given identical inputs, the protocol produces identical outputs. This invariance is the architectural basis of the regulatory claim. Each audit trace is sealed using a three-layer cryptographic integrity architecture: SHA-256 content hash, RFC 3161 trusted timestamp from an accredited third-party Timestamp Authority, and institution signature. The chain forms a Merkle structure in which modification of any historical trace invalidates all subsequent chain hashes. Per-decision accountability is therefore verifiable by any third party without access to the institution's or Veridom's infrastructure. The protocol has been independently instantiated across four regulated domains — digital credit under the Kenya CBK NDTCP framework, FCA Consumer Duty, FCA agent distribution oversight, and US legal AI supervision under ABA Rule 5.3 — with the same two invariants holding in each: deterministic classification and immutable audit trail. Domain-specific profiles for digital credit and cooperative lending are published separately.
Adebayo et al. (Fri,) studied this question.