Audit Framework for Knowledge-Centric AI Systems

1. Purpose

This document defines a pragmatic, minimum-viable audit and governance framework for organizations deploying knowledge-centric AI systems in regulated or high-trust environments.

It is intentionally lightweight, but structurally rigorous. The framework is not designed to introduce additional bureaucracy, nor to prescribe a specific technology stack.

Its purpose is to establish defensible architectural principles that enable organizations to deploy AI systems responsibly while retaining control, accountability, and auditability.

The primary goal is defensibility:
toward auditors, regulators, customers, partners, and internal governance bodies.
By focusing on explicit knowledge structures, clear responsibility boundaries, and inspectable system behavior, the framework provides a practical foundation for trustworthy AI operations under real-world regulatory and operational constraints.
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Table of Contents:

1. Purpose
2. Core Principle
3. Knowledge Strategy
4. Epistemic Boundaries
5. Knowledge Strategy Versus Data Governance
6. Risk Profile
7. Representation Substrate (Layered Knowledge Graph)
8. Domains as Modular Knowledge Graphs
9. Layer–Domain Separation of Concerns
10. Stackable Layers
11. Knowledge Coordinates
12. Architectural Effect
13. Why Layers ? Domains
14. Reasoning and Critique Instrument
15. Auditor-Facing Guarantees

Annex A – Common Failure Modes of Black-Box AI
Annex B – High-Risk AI (EU AI Act Positioning)
Annex C – Alignment With ISO 27001, SOC2, NIS2, GDPR
Annex D – Why Fully AI-Generated Code Cannot Be Used in an End-to-End Audit Trail

1. Core Statement
2. Structural Reasons
3. Allowed Use (Within This Framework)
4. Auditor-Facing Position

Annex E – DORA (Digital Operational Resilience Act)

1. Scope
2. Key DORA Requirements and Framework Mapping