Human-Governed AI Boundaries for Regulated Gate Decision Support (RGDS)
This repository defines explicit governance covenants for the use of AI in regulated, phase-gated decision environments.
It is published as part of an independent case study on decision defensibility and human accountability in complex, regulated delivery contexts (e.g., biopharma, life sciences, and other compliance-driven industries).
This repository is intentionally principles-first and non-operational.
How to read this document
- Read What This Repository Is (and Is Not) to understand scope.
- Read Why This Exists to understand motivation.
- Read Governance Stance (Core Principles) end-to-end.
This document is intended to be read linearly.
- a governance reference defining explicit boundaries for AI assistance
- a human-governed framework for constrained AI use in regulated decisions
- compatible with audit, quality review, and phase-gate approval expectations
- designed to operate alongside decision systems such as RGDS
- an autonomous or agentic AI system
- an AI implementation or tooling repository
- a delivery playbook or enforcement mechanism
- regulatory or legal advice
No AI described here is permitted to silently decide, approve, defer, or accept risk.
In regulated environments, failures rarely stem from lack of intelligence.
They stem from:
- unclear ownership
- implicit assumptions
- undocumented risk acceptance
- decisions that cannot be reconstructed later
Public industry discussions—including openly available webinars, articles, and conference materials hosted by firms such as Syner-G—have consistently surfaced these challenges in the context of AI adoption:
- decision paralysis at phase gates
- fragmented evidence across functions
- late discovery of misalignment
- risk-averse stakeholders unsure how AI fits into regulated workflows
This repository responds to those publicly discussed challenges by formalizing a governance posture in which AI is:
- optional
- constrained
- auditable
- subordinate to human judgment
AI systems may support analysis, but cannot:
- initiate decisions
- approve, reject, or defer outcomes
- override human judgment
- act autonomously or agentically
All decisions remain human-owned.
When used, AI outputs must be:
- intentionally invoked by a human
- reviewable and editable
- attributable to a specific decision context
- explicitly approved or rejected by a named human owner
There is no path for silent or implicit influence.
This repository defines AI governance boundaries only.
Decision systems such as RGDS (Regulated Gate Decision Support) define how decisions are recorded, evaluated, and owned.
This separation ensures that:
- decisions remain valid without AI
- AI use remains inspectable and reversible
- governance can evolve independently of delivery tooling
Every governed decision must remain defensible if all AI outputs are removed.
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Non-Agentic AI Contract
Formal statement of explicit prohibitions and required human ownership -
What AI Will Not Do
Executive- and client-facing clarification designed to reduce adoption risk -
Service-Line Governance Overview
How constrained AI assistance fits into regulated consulting and delivery contexts
Internal enforcement mechanisms, operational playbooks, and delivery-specific procedures are intentionally out of scope for this public repository.
This repository is written for:
- Program and delivery leaders in regulated environments
- Quality, governance, and risk stakeholders
- Executives responsible for phase-gate approvals
- Consultants and analysts designing AI-assisted workflows
It assumes familiarity with regulated delivery—not machine-learning research.
This repository is a reference governance artifact, not a production system.
It is published to support transparency, discussion, and defensible design—not to prescribe implementation.