AI risk management is the coordinated work of identifying, assessing, treating and monitoring risks that arise from developing, acquiring, deploying or using artificial intelligence systems. It connects decisions about an AI system to the people, processes and controls that can prevent harm or respond when conditions change.

It extends enterprise risk management (ERM), rather than replacing it. Existing disciplines such as legal, security, privacy, operational resilience and internal audit remain relevant. AI adds questions that may not fit neatly into established technology controls: Is the system suitable for this purpose? How reliable is its behaviour in changing conditions? Who is accountable for its outputs, and what happens if human review fails?

Layers of work

A useful programme operates at more than one level. Strategic work sets direction, evaluates whether AI is needed, establishes risk tolerance and assigns decision rights. Operational work assesses each use case and its complete system, including models, data, suppliers, interfaces and human tasks. Resilience work prepares for disruptions or high-impact events that do not fit existing classifications. These layers connect board-level choices with testing and day-to-day controls.

Risk management is continuous. A review made before deployment can become stale when the model, data, intended users or surrounding process changes. Organisations need defined triggers for reassessment, records of decisions, routes for incidents and the ability to pause or withdraw a system. The level of effort should reflect the potential impact, exposure and uncertainty of each use.

How the main frameworks differ

EW-AiRM™ is an open, enterprise-wide framework that joins strategic, operational and resilience layers and is designed to augment existing ERM. The NIST AI Risk Management Framework is voluntary guidance centred on managing risks across AI system lifecycles, with functions to Govern, Map, Measure and Manage. ISO/IEC 42001 specifies requirements for an AI management system that an organisation can implement and certify against. The EU AI Act is legislation: it creates legal duties and restrictions for defined AI practices and risk categories. These have different purposes, so organisations may use several together rather than treating them as substitutes.

A practical starting point is to create an inventory of AI use, name a responsible owner for each deployed system, record purpose and impact, and connect each material risk to a control and an escalation route. Framework language helps structure this work, but does not make a decision on an organisation’s behalf. Accountability still depends on people with authority, evidence and time to act.