An Open Governance Standard for Conversational Finance Agents

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QUICK START

git clone https://github.com/CoreFirewall/Fintech-CUI-Trust-Framework-826.git
cd Fintech-CUI-Trust-Framework-826
python setup.py

An open-source engineering governance standard defining trust boundaries for conversational AI agents in high-stakes domains. MIT Licensed.

What is this?

An Open Governance Standard for Conversational Finance Agents is an open, MIT-licensed engineering specification that defines the structural governance components a conversational AI agent must implement before being deployed in any context where its outputs could create financial obligations, regulatory exposure, or consumer harm. It is not a software product. It is a public-domain technical specification — analogous to an RFC — that any enterprise with engineering resources can implement.

This standard is engineered under the AI Native Engineering paradigm: governance is not retrofitted onto AI systems after deployment as an external audit layer. Governance mechanisms — intent classification, authorization gating, generation boundary enforcement, circuit-breaking, and audit logging — are embedded as structural components from the first line of code. They are properties of the interaction architecture, not post-hoc content filters.

The standard addresses a structural vacuum: as systems shift from graphical user interfaces (GUI) to conversational user interfaces (CUI), AI agents make decisions about what to say, what to promise, and what to authorize. No shared, industry-level specification currently defines the trust boundaries within which these agents must operate.

Architecture

User → [Agent Runtime] → [Protocol Middleware] → [LLM]
                              │
                        ┌─────┴─────┐
                        │   Input   │
                        │ Safeguard │
                        └─────┬─────┘
                              │
                        ┌─────┴─────┐
                        │  Intent   │
                        │Classifier │
                        └─────┬─────┘
                              │
           ┌──────────────────┼──────────────────┐
           │                  │                  │
    ┌──────┴──────┐   ┌──────┴──────┐   ┌──────┴──────┐
    │Authorization│   │ Generation  │   │  Insurance  │
    │  Trigger    │   │  Boundary   │   │    Fuse     │
    │  Engine     │   │  Engine     │   │   Engine    │
    └──────┬──────┘   └──────┬──────┘   └──────┬──────┘
           │                  │                  │
           └──────────────────┼──────────────────┘
                              │
                        ┌─────┴─────┐
                        │   Audit   │
                        │   Trail   │
                        │   Logger  │
                        └───────────┘

Five-Layer Architecture

The standard defines five structural layers, each enforced at a specific point in the agent's generation pipeline:

Core Mechanisms

• Authorization Trigger — A structured taxonomy of conversational intents that require explicit user authorization before an AI agent may proceed. Intents are classified along two axes: financial commitment risk (F0–F3) and regulatory sensitivity (R0–R3). The trigger fires before the agent generates any response.
• Generation Boundary — A categorical specification of content types an agent must never autonomously generate, including financial commitments, price guarantees, compliance representations, and discriminatory or deceptive outputs. Enforced at both pre-generation and post-generation stages.
• Insurance Fuse — A circuit-break mechanism that severs the agent's generation privilege in real time when conversation state reaches pre-defined compliance thresholds. Unlike Authorization Trigger, which suspends generation pending user confirmation, Insurance Fuse terminates generation authority outright and transfers control to a human operator or deterministic SOP. Trigger conditions include: cumulative conversation patterns approaching regulatory limits, user vulnerability signals, and multi-turn escalation trajectories exceeding safe bounds.
• Audit Trail — A standardized, immutable log format capturing every generation, authorization, escalation, and fuse-trigger decision with full decision-chain traceability, supporting both internal governance and third-party audit.
• Three-Tier Adoption Model — Lite (default configuration, zero customization), Standard (designated audit role), and Full (forkable for enterprise integration). The same specification serves both small organizations and large enterprises.

Core/Mapping Decoupling

A defining architectural property of this standard is the separation of core governance logic from jurisdiction-specific compliance rules. The five layers remain architecturally invariant across deployment contexts. Jurisdiction-specific requirements — retention periods, notification thresholds, additional prohibited content categories — are configured through a Compliance Mapping Layer without modifying the core specification.

Who is this for?

Any enterprise whose conversational AI agents handle interactions that touch payments, financial commitments, or regulated consumer decisions — including financial institutions, healthcare booking platforms, legal intake services, education enrollment systems, and retail delivery platforms.

Repository Structure

├── README.md           ← You are here
├── LICENSE             ← MIT
├── spec/               ← The protocol specification (the core artifact)
├── src/                ← Reference implementation
└── background/         ← Research foundation — concept paper, architecture whitepaper, research proposal

Background

• Concept Paper — Academic framing: AI governance framework and trust mechanisms
• Architecture Whitepaper — The 5-Layer reference architecture
• Research Proposal — Empirical study design: GenAI as process innovation in organizational contexts

License

MIT — see LICENSE.