For most enterprises today, the primary source of AI risk is not internal AI development. It is the AI embedded in the software they have already purchased. Microsoft 365 Copilot, Salesforce Einstein, ServiceNow’s AI features, HR platforms with automated screening, and financial tools with embedded prediction models all represent AI Read more
Every governance framework eventually confronts the same practical question: who, exactly, does what? Without an operating model – a defined structure specifying which functions are responsible for which AI governance activities – even a well-written AI policy becomes aspirational prose. This chapter maps the institutional architecture that makes governance real. Read more
This chapter distills the guidance from NIST, ISO/IEC 42001, Deloitte, PwC, McKinsey, and the regulatory requirements covered throughout this primer into a practical, sequenced checklist. You do not need to complete this in 90 days. You do need to start. Step 1: Build Your AI Inventory (Days 1–30) You cannot Read more
The build-versus-buy question comes up in every technology investment cycle. For AI agent governance, the question is particularly fraught because the problem is novel enough that many organizations are uncertain what a good solution looks like – and because the temptation to build is strong when internal teams can see, Read more
This document describes a scoped six-to-eight-week design-partner pilot with Ethosure. The goal is to take one AI agent already running in your environment, instrument it with Ethosure’s enforcement layer, and produce an auditor-ready evidence pack. Three objectives drive the pilot: (1) validate technical fit – confirm the enforcement layer integrates Read more
