Enterprise AI. Without the Governance Gap
The policy paper for enterprises adopting AI across every business unit, written to preserve the roles that own the outcomes, not to slow down the work.
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Showing 3 of 33The AI Tool Register
Governs how AI enters the organization. Establishes the difference between sanctioned and shadow AI, the record the organization keeps of what it has sanctioned, the two gates every AI system passes, the evaluation criteria and contractual requirements applied before approval, the response when unsanctioned AI is found, and the retirement framework that closes the lifecycle. The chapter's trace to accountability runs through the second corollary: an organization that cannot say what it has deployed must still answer for what is deployed.
The Trust and Harness Framework
Establishes the four-tier classification system that determines how much weight an operator may place on AI output, what verification each tier requires, and what work each tier may and may not perform. The framework is the operational bridge between the Accountability Principle of Chapter 1 and the per-tool, per-task decisions employees make every day.
Stakeholders and Authority
Names the people, roles, and authority structures this policy protects, empowers, and constrains. Establishes who owns the policy itself, who enforces it, and how it relates to existing risk, audit, legal, and compliance functions. Without this chapter, the principles of Chapter 1 and the scope of Chapter 2 have no enforceable referent.
David Moskowitz
Technology Executive · IT Strategy · AI Advisor
A technology executive with thirty years spanning enterprise IT strategy, mission-critical operations, and executive business cases for commercial and federal agencies. Founder of a fan data capture platform that worked across 5 continents, he builds and deploys AI to production systems daily, and writes this paper from that practical experience, for the executives accountable when AI acts inside the organization.
What You'll Find Here
A comprehensive ecosystem for enterprise AI governance.
Table of Contents
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Practitioner Rigor
Written by a single accountable author with thirty years of enterprise practice. Every principle grounded in documented, real-world AI failure patterns rather than theory.
Published Chapter by Chapter
The paper is released and revised one chapter at a time. New and updated chapters appear as the work progresses. Subscribe to be notified on release.
Open Collaboration
Readers are invited to contribute real-world stories, questions, and edge cases from their own AI deployments, shaping the framework with practice, not just theory.