Framework

What Is AI Risk Management?

AI risk management is the systematic process of identifying, assessing, and controlling risks that arise from developing and deploying AI systems. It extends traditional enterprise risk management disciplines to address AI-specific risk categories, including hallucination, bias, opacity, adversarial manipulation, and the rapidly evolving regulatory landscape.

Definition

AI Risk Management, the discipline of identifying, assessing, treating, monitoring, and reporting risks specific to artificial intelligence systems within an organisation's broader enterprise risk framework.

AI risk management extends standard ISO 31000 risk methodology to AI-specific characteristics: model behaviour that shifts over time, training data quality, vendor concentration, agent autonomy, emergent capabilities, and conduct exposure. ISO/IEC 23894 is the AI-specific companion to ISO 31000. The NIST AI RMF and ISO/IEC 42001 both organise around risk-management functions.

Source: ISO/IEC 23894:2023; NIST AI RMF

Why AI risk is different from general technology risk

AI systems introduce risk characteristics that conventional technology risk frameworks were not designed to handle. They can fail without producing an error message, producing plausible but wrong outputs that are difficult to detect without domain expertise. They can exhibit bias that was not designed in and may not be immediately apparent. They are not fully explainable in the way conventional software is. And they can behave differently in production than in testing, because real-world data distributions differ from training data.

The risk management cycle applied to AI

1Inventory

Identify all AI systems in use across the organisation, including shadow AI. You cannot manage risk you cannot see.

2Classify

Categorise each system by risk level: what decisions does it influence, what harm could result if it fails, who is affected.

3Assess

For each system, conduct a structured risk assessment: technical risks (accuracy, bias, reliability), legal risks (privacy, liability, regulatory), and operational risks (dependency, vendor lock-in, incident response).

4Control

Implement controls proportionate to risk: human oversight mechanisms, testing and monitoring, access controls, incident response pathways, and policy constraints on use.

5Monitor

Review AI system performance regularly. Models drift. Environments change. Risks that did not exist at deployment may emerge. Governance must be continuous, not point-in-time.

6Improve

Update governance frameworks in response to incidents, regulatory changes, new AI deployments, and improvements in available controls and standards.

Key AI risk management frameworks

Framework	Origin	Structure	Scope
NIST AI RMF	United States	Four functions: Govern, Map, Measure, Manage	Voluntary; widely adopted in the US and globally
ISO 42001:2023	International	AI Management System standard, Plan, Do, Check, Act	Certifiable; used for third-party assurance
AI6 (NAIC)	Australia	Six essential practices for AI governance and deployment	Voluntary; primary Australian government guidance since Oct 2025
EU AI Act	European Union	Risk-based regulation with mandatory conformity assessment for high-risk AI	Mandatory for organisations operating in EU markets

Free governance assessment The AIRA framework