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What is AI Risk Management

Definition

AI risk management is the systematic identification, assessment, and mitigation of the risks posed by AI systems, including bias, errors, security, privacy, and compliance risks, so AI is deployed responsibly with controls proportionate to its potential for harm.

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Identifies and mitigates risks like bias, error, and misuse
Applies controls proportionate to each AI system’s potential harm
Supports compliance with emerging AI regulation
Protects the organisation from AI-related legal and reputational damage

Real World Example

Before deploying a hiring model, a company runs AI risk management, assessing bias, failure modes, and compliance, and adds human review and monitoring controls scaled to the high stakes of employment decisions.

FAQs

What risks does AI risk management address?

Bias and fairness, errors, security, privacy, transparency, and compliance risks across the AI lifecycle.

How are AI risks assessed?

By evaluating each system's potential impact and likelihood of harm, then applying proportionate controls and monitoring.

Why is AI risk management increasingly important?

Growing AI use and regulation make systematic, accountable risk management essential to deploy AI responsibly.