AI Governance
AI governance creates the authority, accountability, and evidence required to operate AI at scale.
As AI systems gain access to business processes and enterprise applications, organizations need clear answers about what each system can do, who owns it, and how its actions are recorded. Strong governance makes those answers available before a problem occurs and keeps them current as systems, permissions, and responsibilities change. The articles here focus on the operating controls that allow organizations to expand AI while maintaining clear ownership and a reliable record of what happened.
7 articles
The Token Tax
The Hidden Cost of Giving AI the Wrong Responsibility
AUG 24, 2026
You Are Deploying AI Faster Than You Can Prove What It Did
Companies describe agentic AI as a capability in their marketing and as an exposure in their SEC filings. The filings are the more informative document.
JUL 9, 2026
You Use 2 Percent of Your Access. Your Agent Will Use All of It.
The permissions nobody exercises were harmless while only people held them.
JUN 4, 2026
A Chatbot's Token Opened 700 Companies.
Nobody was phished. The attacker used a credential that belonged to an AI integration, and most organizations cannot revoke one.
MAY 21, 2026
The Exam Has Errors. The Grader Changes Its Mind.
Every AI decision you have approved rests on an evaluation. Three findings say the evaluation is shakier than the model.
APR 23, 2026
One Thousand Identical Questions. Eighty Different Answers.
Temperature zero exists to make a model repeatable. It does not, and the reason is not randomness.
APR 9, 2026
$670,000 for the Tools You Did Not Buy.
One in five breaches now involves AI nobody approved. The account is the problem, not the model.
JAN 29, 2026
About this topic
What governance has to prove
Effective governance connects the authority given to an AI system with the actions it takes in production. An organization should be able to show what the system was approved to do, which identity and permissions it used, what actions it took, and who was accountable for the outcome.
That evidence becomes more important as AI moves deeper into business operations. Systems change, permissions expand, credentials persist, and evaluations age. Governance provides a way to manage those changes while keeping deployed AI aligned with the authority the organization intended to grant.
Build ownership into the operating model
Every production AI system should have a defined owner, an approved scope of authority, and a reliable record of its actions. The organization should also know who can change its permissions, stop it, revoke its access, and determine whether its current behavior still matches its approved purpose.
Repeatability also matters when AI contributes to consequential decisions. Organizations need to know where variation is acceptable, where consistent execution is required, and which controls ensure that important actions remain within defined boundaries.
How to use these articles
Map each production AI system to an owner, a defined scope of authority, the identities and permissions it uses, and the evidence it produces. Then test whether those controls still reflect how the system operates today.
Pay particular attention to changes that occur after deployment: expanded access, new integrations, outdated evaluations, unclear ownership, or actions that cannot be reconstructed. Strong governance gives the organization the confidence to expand AI because authority and accountability remain clear as adoption grows.