Every meaningful technology market starts the same way: practitioners invent a solution to a real problem, vendors give it a dozen different names, and then an analyst firm publishes a report that hands the category a single, durable label. That moment just arrived for AI agent governance. In its November 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
Governance problems in software organizations often share a root cause: rules defined in one place are enforced differently in another, not enforced at all in a third, and unknown to a fourth. A security policy lives in a Confluence page; the Git hook that supposedly enforces it was written two Read more
Git hooks and agent hooks catch problems at commit time and during active tool calls. But a complete governance picture for AI agents also needs visibility into the traffic flowing between the agent and the AI provider – the prompts being sent, the responses coming back, the tool calls being 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
Conversations about AI governance often stall because the people in the room have different jobs, different concerns, and different definitions of what a solution looks like. The CISO is thinking about access controls. The chief risk officer is thinking about bounded, demonstrable governance. The compliance officer is thinking about audit Read more
The problem with most approaches to AI agent governance is that they operate on the wrong side of the moment. They describe what should happen, monitor what did happen, or filter the text surrounding an action. None of them sit in the action path and intervene before the consequence is Read more
If you map the tools your organization uses to manage AI agent risk, you will find good coverage at the edges. Governance platforms document policies. Security scanners test code before it ships. Monitoring tools log activity after the fact. What the map reveals – if you draw it honestly – Read more
AI agents are useful because they act. They write code, run commands, query databases, and make changes to systems – often faster than any human reviewer can follow. That speed is the whole point. But it creates a problem most organizations are only beginning to confront: if an agent does Read more
