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April 17, 20260 citationsOpen Access

Substrate-Layer AI Audit Methodology v1.0: A Seven-Step Canonical Sequence for Governance-Grade Agentic System Auditing

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NTNarnaiezzsshaa Truong

Key Points

  • The aim is to define a systematic approach for auditing AI and agentic systems while ensuring reproducibility and compliance.
  • Defines a seven-step audit sequence
  • Integrates multiple frameworks including SGIS and CEPS
  • Produces hash-anchored evidence at each step
  • Ensures jurisdiction-portability
  • Maps compliance with Colorado AI Act and EU AI Act
  • Establishes a reproducible audit process
  • Generates clear evidence artifacts for verification
  • Facilitates compliance across multiple jurisdictions
  • Provides a framework-agnostic approach to auditing
  • Enhances the reliability of AI governance evaluations

Abstract

This document specifies a canonical seven-step audit sequence for governance-grade evaluation of AI and agentic systems. The methodology provides a deterministic, reproducible workflow for auditors operating across organizational, technical, and agentic layers. It integrates the Substrate Governance Instrument Suite (SGIS), the Colorado Evidence Packet Schema (CEPS), the Emotional-Layer Integrity and Operator Capture (EIOC) framework, Agent Lineage and Provenance (ALP) verification, and AI Indicators of Compromise (AIOC) analysis into a unified audit spine. Every step produces hash-anchored evidence artifacts. The final determination is reproducible by a second auditor from the evidence bundle alone. This methodology is designed to be jurisdiction-portable. Colorado AI Act and EU AI Act compliance mappings are noted at each step where applicable, but the spine itself is framework-agnostic.

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Cite This Study

Narnaiezzsshaa Truong (2026) studied this question.

synapsesocial.com/papers/69e1d0165cdc762e9d859194https://doi.org/10.5281/zenodo.19600208
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