What is AI risk management?

The five categories of AI risk

AI risk management addresses five distinct risk categories. Technical risks arise from the AI system itself: errors, hallucinations (confident but wrong outputs), model drift (performance degradation over time), and failure to perform as described by the vendor. Operational risks arise from how AI is used: over-reliance on AI outputs without adequate human verification, decision accountability gaps where no human is accountable for an AI-influenced outcome, and process failures where AI is integrated into workflows without adequate controls. Legal risks include EU AI Act non-compliance, liability for decisions influenced by AI, and intellectual property issues with AI-generated content. Reputational risks arise when AI failures become visible to clients, the media, or regulators. Data risks include exposing sensitive client data through AI tools, using personal data in AI systems without an adequate legal basis, and breaching confidentiality obligations through AI use.

AI risk management and the EU AI Act

The EU AI Act structures AI risk management requirements around its risk classification system. Prohibited AI practices represent risks so severe they are banned entirely. High-risk AI systems require a formal risk management system — not just a risk assessment, but an ongoing process that covers the entire lifecycle of the AI system, from design through deployment through monitoring and decommissioning. For limited-risk and minimal-risk systems, the Act does not prescribe a specific risk management process, but good governance practice still requires organisations to assess and manage the risks these systems create. For Irish organisations, AI risk management is best approached as an integrated discipline that addresses both EU AI Act obligations and the organisation's own risk appetite — because the Act's requirements are a floor, not a ceiling.