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The AI governance landscape has shifted significantly since we published the first version. This update reflects that: AI has moved from a graveyard of failed projects to a jungle of systems deployed everywhere, often without oversight. The challenge is n

Deeploy launched the EU AI Act Hub, a free, continuously updated knowledge base that explains the EU AI Act in plain language. It offers a risk self-assessment tool, a full compliance timeline from 2024 to 2030, deep-dive articles across nine topics, and

AI agents are being deployed at a pace that most organisations' governance structures were never designed to handle. Gartner projects the average large enterprise will run more than 150,000 AI agents by 2028. Each of those agents queries internal systems,

Deeploy's MCP Server enables every AI agent in an organisation to register itself, ship its traces, and live under the same controls as the rest of the AI estate. Enterprises have handed out OpenAI and Claude licenses broadly to enable teams to experiment

Most organizations already have company-wide licenses for general-purpose AI (GPAI), tools like ChatGPT, Copilot, or Claude that function as personal assistants or agents for employees across the business. That's exactly where governance gets complicated.

Most organizations scaling AI face the same problem: AI systems are spreading across teams, business units, and lifecycle stages, but there's no single place to see what's actually happening. Are governance controls being followed? Which models are high-r

AI governance standards like the NIST AI Risk Management Framework and the AIUC-1 certification provide critical guidance for building trustworthy AI systems, but translating their requirements into operational practice can be a challenge. Organizations n

AI governance isn't a one-time exercise. Models drift, regulations evolve, and business contexts change. Without systematic review processes, AI use cases become compliance blind spots. This creates risk, especially under regulations like the EU AI Act th

In AI operations, deployment it's when models move from controlled experimentation to real-world impact. Yet, in many organizations, deployment decisions can still happen without formal approval workflows. That gap creates risks for model reliability, tra

Understanding AI regulations is only half the challenge. The real test comes when organizations translate those high-level requirements into practical, measurable governance controls that teams can actually implement.This is where AI governance moves from