# Institutional AI Governance Framework & Model Risk Management (MRM)
*Document Version: 1.0.3 | Classification: Internal / Due Diligence*

## 1. Executive Summary
This document outlines the Model Risk Management (MRM) architecture underlying the **DCM Digital Insight Engine**. It demonstrates the strict segregation between the deterministic quantitative models (DCM-Risk v2) and the natural language generation layer (AI Copilot v1.2). 

Our architecture follows a **Zero Hallucination** paradigm: the AI is structurally prohibited from performing autonomous mathematical estimations or external data retrieval. It acts exclusively as an interpretation and formatting layer for mathematically proven and logged scenario outputs.

## 2. Zero Hallucination Architecture

### 2.1 Separation of Concerns
The system architecture strictly isolates quantitative processing from qualitative formatting:

1. **Quant Engine (Scenario Simulator):** 
   - Receives deterministic inputs (e.g., liquidity shock -70%).
   - Calculates outputs (Baseline vs. Shocked Score, Sensitivity Variance).
   - Generates an immutable state snapshot.
2. **Data Hook (State Injection):**
   - The verified state variables are extracted and injected into strict, pre-approved prompts.
3. **Narrative Engine (AI Copilot):**
   - Receives the injected prompts.
   - Outputs rigid, institutional-grade text (restricted to predefined templates like "COMEX 60-sec Brief").

### 2.2 Trigger Limitations
The AI cannot be triggered via free-text input ("chat"). It is exclusively triggered by:
- Algorithmic anomaly detection (e.g., Variance >= 15%).
- Pre-defined, committee-approved action buttons (Explain, Summarize, Compare).

## 3. Cryptographic Auditability & Traceability

### 3.1 Immutable Log Schema
Every AI generation event is treated as a critical system action and logged within the `AuditLogger`. The log schema mandates:

- `Timestamp`: ISO 8601 precise execution time.
- `Active User`: Identity of the requestor (e.g., CRO, Risk Analyst).
- `Triggered Prompt`: Exact template called.
- `Base Scenario State`: The exact input variables defining the mathematical state.
- `Copilot Version & Engine Version`: Assures inter-annual traceability.
- `Output Hash`: A SHA-256 hash of the generated text string to prevent retroactive tampering.

### 3.2 Mock Audit Export
For external auditors (e.g., Big Four, Regulators), the platform generates a signed JSON/PDF bundle containing the chronological sequence of stress tests mathematically linked to the hashed generated narratives. This guarantees that **a given narrative report was undeniably generated from a specific mathematical snapshot**.

## 4. Change Management & Model Validation

### 4.1 Version Control
Any structural change to the scenario algorithms prompts a version increment on the **Engine** (e.g., v3.1 to v3.2). Any vocabulary, template, or prompt adjustment increments the **Copilot** (e.g., v1.2 to v1.3). Both versions are explicitly stamped on all outputs.

### 4.2 Independent Review Path
Before any deployment, the prompt library and its possible boundary outputs are subjected to a rigorous legal and compliance review. The presence of persistent disclaimers (*Generated analytical summary based on scenario outputs. Not a binding investment or liquidity recommendation.*) acts as the final legal buffer.