Computable Trust Architecture

The Problem

Enterprise AI governance is often strongest in policy language and weakest at runtime enforcement. Organizations can define principles, approval workflows, risk registers, and audit requirements, but those controls frequently sit outside the exact moment where an AI output becomes an action.

Computable Trust Architecture addresses that gap by defining a runtime governance architecture that can compute trust, enforce policy, and produce evidence at the point of action.

Core Contribution

The paper introduces a formal architecture for making trust computable and enforceable in AI-mediated workflows. Its reference implementation, the Trust Computation System, computes a Trust Integrity Score and produces a tamper-evident Trust Certificate for governed AI outputs and actions.

The BACK Governance Model

The framework decomposes trust into four governed dimensions. These are not abstract principles. They are evaluable dimensions that contribute to the trust computation.

• Boundedness: Whether the output stays within authorized scope, identity tier, and permission boundaries.
• Attribution: Whether the output is traceable to verified, versioned, and permissioned sources.
• Compliance: Whether the output conforms to active policy, regulatory requirements, and operational constraints.
• Known: Whether the system's expressed confidence is calibrated against the reliability of its inputs.

Runtime Enforcement Pattern

TCS is designed as a governance sidecar that can operate at trust boundaries without requiring model modification, model retraining, or major orchestration changes. The sidecar evaluates context, computes trust, produces a certificate, and returns an enforcement decision.

User / Agent Request
        |
        v
Governed Context Assembly
        |
        v
Trust Evaluation Engine
        |
        +--> Boundedness
        +--> Attribution
        +--> Compliance
        +--> Known
        |
        v
Trust Integrity Score
        |
        v
Trust Certificate
        |
        v
Enforcement Decision

Why It Matters

As enterprises move from AI pilots to agentic workflows, the governance problem changes. The key question is no longer only whether a model was approved. It is whether a specific output, under a specific context, for a specific user, action class, data source, and risk tier, should be allowed to proceed.

This becomes especially important in regulated environments where organizations need auditable evidence, not just policy intent.

• Financial services and investment workflows
• Healthcare and clinical decision support
• RAG pipelines using governed enterprise data
• MCP-connected tools and agentic systems
• Enterprise AI workflows requiring audit-ready governance evidence

Reference Implementation

The white paper describes a reference implementation with deterministic trust computation, governed context assembly, Trust Certificate generation, decision enforcement, and sidecar runtime capabilities.

The implementation includes Phase 1 specification unit tests and a FastAPI sidecar demonstration across controlled financial services and healthcare governance scenarios.

Technology and Governance Concepts

Download the White Paper