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

For a provider of high-risk AI, Article 9 describes a continuing risk-management system as part of the applicable high-risk requirements. Deployers have separate duties concerning use, oversight and monitoring. Other systems still deserve a proportionate assessment of operational and legal risks, even if that specific provider regime does not apply. Link controls to the actual harm identified, assign an owner and review whether the control remains effective after changes to the tool or the work.

Legal references reviewed 4 October 2026. High-risk provisions have phased application and transition rules. Sources: Commission timetable, deployer duties, impact-assessment scope and literacy guidance.