One control point in the agent action path, running on your own infrastructure. Ethosure Governance Inc AI stealing control? Try stealing it back! Your AI already: writes files runs shell commands and reaches into systems that you care about Your Top-of-Mind now is probably agent unpredictability. AI agents are Read more
This glossary defines 52 key terms as they are used in AI governance regulation, standards, and practice. Where a term has an authoritative definition in NIST, ISO, the EU AI Act, or the OECD, that source is cited. Where a term is used differently across frameworks, the most widely recognized Read more
Traditional machine learning models are narrow in scope: a fraud detection model flags transactions; a credit scoring model outputs a risk score. Their failure modes, while consequential, are generally bounded and measurable. Generative AI (GenAI) – large language models, image generators, code assistants, and multimodal systems – is different in Read more
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
When an organization deploys software to govern its AI agents, a natural question follows: how do you know the governance software itself is trustworthy? How do you know the version you installed is the version the developers built and signed? How do you know it does not contain a vulnerable Read more
Ask a financial institution’s chief information security officer whether they are comfortable routing AI agent governance decisions through a cloud service operated by a third party, and the answer will usually be no – or at minimum, a long conversation about data residency, vendor risk, and regulatory compliance. Ask the Read more
An AI coding agent that encounters a policy violation for the first time might need several attempts to understand and fix it. An AI coding agent that has seen the same violation three times before should not need to rediscover the solution. More broadly, an organization running AI agents across Read more
