AI risk management

AI risk management extends traditional enterprise risk frameworks into AI-specific territory. The risks associated with AI systems are distinct from, though they interact with, cybersecurity, operational, legal, and reputational risk categories that organizations already manage.

Key AI risk categories include:

• Data risk: training on improperly authorized, biased, or low-quality data.
• Model risk: producing incorrect, biased, or harmful outputs.
• Operational risk: AI systems failing or behaving unexpectedly in production.
• Compliance risk: violating the GDPR, the EU AI Act, or other applicable regulation.
• Reputational risk: AI-driven decisions that harm customers or erode trust.

As AI regulations proliferate globally, AI risk management has shifted from a voluntary best practice to an emerging legal requirement. The NIST AI Risk Management Framework (AI RMF), published in 2023, provides a widely-adopted voluntary structure. Organizations deploying AI in high-risk contexts, particularly those involving personal data, consequential decisions, or autonomous action, need documented risk management processes that they can evidence to regulators.