Ai Governance

Context For the mid-career professional, the field of AI governance can appear opaque, often obscured by technical jargon and rapid regulatory changes. However, when viewed through the lens of traditional risk management, the requirements become clear. This article deconstructs the vague concept of ‘governance’ into six tangible skill domains. Whether you are looking to pivot your career into AI oversight or are building a team to manage these risks, understanding this breadth of necessary skills is the first step toward competence. ...

Context For mid-career professionals in risk, audit, or management, the rapid adoption of Artificial Intelligence presents a specific challenge: how to apply established governance principles to a non-deterministic technology. This note outlines where AI sits within the standard Three Lines of Defence model, helping you position your skills and oversight responsibilities effectively. Defining AI Governance AI Governance is simply the framework of accountability, authority, and control that ensures automated systems are used responsibly. It is the mechanism by which an organisation retains meaningful control over its technology. ...

Context For the mid-career professional in audit, risk, or IT, the rise of artificial intelligence presents a distinct bifurcation in career trajectories. You do not need to become a data scientist to remain relevant, but you cannot afford to remain illiterate in the mechanics of automated decision-making. This article outlines how to pivot existing your ‘Lines of Defence’ experience into the emerging discipline of AI Governance. The Emergence of a Distinct Discipline AI Governance is rapidly decoupling from general IT governance and data protection. While it shares DNA with these fields, it addresses a specific convergence of risks that traditional frameworks struggle to contain: algorithmic bias, non-deterministic outputs, and the opacity of ‘black box’ decision-making. ...

European AI Office UK’s AI Safety Institute Further reading AI Watch: Global regulatory tracker - United Kingdom

Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models share structured context - including messages, observations and actions - in a predictable and machine-readable way. Released in early 2024, MCP has been described as an “API for AI models”. However, describing it as just an API underplays its fundamental purpose: to provide a common, structured language for inter-model communication, which goes beyond a simple API call and introduces new considerations for systems design and governance. ...