[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83638-en":3,"doc-seo-83638-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83638,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond","The emergence of risk-based regulatory frameworks for AI elevates the need for rigorous risk assessment to deliver safe and reliable AI systems. This paper provides an overview of AI risk assessment and management methodologies, starting from the worldwide regulatory landscape and explaining why systematic risk assessment is required. It surveys AI-related risk categories reported in the literature, from technical failures to ethical and social impacts, then reviews general risk assessment frameworks, best practices, and methodological gaps for future research.","Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond  \nJavier Irigoyen∗ , Roberto Daza∗†, Aythami Morales∗‡, Julian Fierrez∗ , Ruben Tolosana∗ , Ruben Vera-Rodriguez∗ , Francisco Jurado†, and Alvaro Ortigosa†  \n∗ BiometricsAI, Universidad Auto´noma de Madrid (UAM), Spain  \n†GHIA, Universidad Auto´noma de Madrid (UAM), Spain  \n‡ Universidad de Las Palmas de Gran Canaria (ULPGC), Spain  \nCorresponding author: [javier.irigoyen@uam.es](javier.irigoyen@uam.es)  \narXiv :2607 .02 197v 1 [ cs .CY] 2 Jul 2026  \nAbstract—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. Index Terms—AI Act, AI Risks, Responsible AI, Safe AI.  \nI. INTRODUCTION  \nThe proliferation and integration of Artificial Intelligence (AI) technologies in multiple sectors has led to significant innovations and improvements in efficiency, decision-making, and automation. Despite these advantages, the widespread use of AI also introduces considerable risks, such as biased outcomes, privacy violations, lack of transparency, and cybersecurity vulnerabilities. As AI systems become increasingly autonomous, these risks pose significant legal, ethical, and operational challenges for organizations and society at large. Consequently, there is a growing emphasis from regulatory bodies, policymakers, and academia on systematically assessing and managing AI-associated risks, exemplified by the recent entry into force of the European Union’s Artificial Intelligence Act (AI Act) .  \nThe AI Act represents a landmark regulatory approach that aims to create a standardized framework to identify and mitigate risks posed by AI systems. Its risk-based approach categorizes AI applications according to potential harm, mandating stringent assessment requirements for high-risk AI applications. Although such regulations provide a foundational structure for risk governance, there remains a need for practical methodologies to implement these regulatory requirements effectively within organizations.  \nThis research was supported by Ctedra ENIA UAM-VERIDASen IA Responsable (NextGenerationEU PRTR TSI-100927-2023-2), M2RAI (PID2024-160053OB-I00, MICIU/FEDER), TRUST-ID (PID2025- 173396OB-I00, MICIU/AEI and the EU) and PowerAI+ (SI4/PJI/2024-00062, Comunidad de Madrid and UAM) . Javier Irigoyen is supported by an FPI fellowship from MINECO/FEDER.  \nRecent academic literature has offered information on  \nmethodologies and frameworks for AI risk assessment, including contributions on interpretability, fairness, robustness, and sustainability. Despite these advances, current approaches often remain theoretical or domain-specific, lacking comprehensive empirical validation in diverse organizational contexts. Furthermore, existing research frequently addresses individual risk dimensions in isolation or lacks integration into a coherent, universally applicable framework.  \nOur paper addresses these critical gaps by systematically reviewing existing AI risk assessment approaches, synthesizing insights from the literature, regulatory requirements, and practical implementations within organizations. Through our comprehensive review, our aim is to provide clarity on best practices and shortcomings of current methodologies, ultimat","cbCaimBZahtuCRAs","https://ap.wps.com/l/cbCaimBZahtuCRAs","pdf",285451,3,1,6,"English","en",105,"# Introduction\n## Introduction to ethical guidelines and trustworthy AI in the European context\n### General principles of trustworthy AI\n### Operationalization of principles into specific requirements","[{\"question\":\"What principles and operational requirements define “trustworthy AI” in the European context?\",\"answer\":\"Trustworthy AI is structured around three principles: lawful, ethical, and robust. These principles are operationalized into seven life-cycle requirements, including human agency and oversight and technical robustness.\"}]",1784189428,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"overview-of-risk-assessment-and-management-for-intelligent-systems-under-the-ai-act-and-beyond","",{"@graph":36,"@context":77},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/overview-of-risk-assessment-and-management-for-intelligent-systems-under-the-ai-act-and-beyond/83638/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What principles and operational requirements define “trustworthy AI” in the European context?","Question",{"text":75,"@type":76},"Trustworthy AI is structured around three principles: lawful, ethical, and robust. These principles are operationalized into seven life-cycle requirements, including human agency and oversight and technical robustness.","Answer","https://schema.org",{"og:url":51,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":84},[85,89,93,97,102,106,111,114,119,122,126],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]