OMP™ — Operating Model Protocol: A Deterministic Routing Invariant for Tamper-Evident AI Decision Accountability in Regulated Industries

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-20
DOI
https://doi.org/10.5281/zenodo.19140948
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
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article

OMP™ — Operating Model Protocol: A Deterministic Routing Invariant for Tamper-Evident AI Decision Accountability in Regulated Industries

Tolulope Adebayo, Apalowo Oluropo, Makanjuola Festus
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

OMP™ — Operating Model Protocol: A Deterministic Routing Invariant for Tamper-Evident AI Decision Accountability in Regulated Industries

Tolulope Adebayo, Apalowo Oluropo, Makanjuola Festus
article en

Abstract

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.

Zenodo (CERN European Organization for Nuclear Research)
Daido University (JP)
Industry, innovation and infrastructure
Openalex Percentile: Top 16%
Ethics and Social Impacts of AI
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