Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond

2026-07-02

AI Summary

This study addresses the growing global demand for risk-based AI regulation by systematically identifying, analyzing, and integrating the multifaceted risks of artificial intelligence across technical, ethical, and societal dimensions. Through a comprehensive literature review and framework analysis, it establishes the first systematic alignment between major international regulatory frameworks and AI risk typologies from academic research, thereby constructing a structured risk taxonomy. The work clarifies key dimensions and limitations of existing risk assessment methodologies, distills best practices, and identifies critical research gaps. By doing so, it provides a robust theoretical foundation and strategic guidance for the development of standardized, actionable AI risk management tools aligned with evolving regulatory expectations.

Abstract

The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this imperative, this paper presents an overview of AI risk assessment (identification and analysis) and management methodologies. It begins by reviewing the worldwide regulatory landscape that drives the need for systematic AI risk assessment. Then we characterize the spectrum of AI-related risks identified in the literature, from technical failures to ethical and social impacts. Subsequently, it reviews key risk assessment methodologies proposed for AI systems, focusing on general frameworks. The paper highlights best practices and illuminates methodological gaps, highlighting areas for further research on AI risk assessment.

Problem

• AI risk assessment
• risk management
• regulatory frameworks
• AI safety
• systematic risk analysis

Innovation

• AI risk assessment
• risk management
• regulatory compliance
• ethical AI
• methodological gaps