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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Javier Irigoyen, Roberto Daza, Aythami Morales, Julian Fierrez, Ruben Tolosana, Ruben Vera-Rodriguez, Francisco Jurado, Alvaro Ortigosa
• Venue: arXiv: cs.CY
• Published: 2026-07-02
• Collected: 2026-07-03
• URL: http://arxiv.org/abs/2607.02197v1
• PDF: https://arxiv.org/pdf/2607.02197v1
• DOI: UNKNOWN

Plain-Language Brief

The paper provides an overview of the methods used for assessing and managing risks associated with intelligent systems, in response to the need for safe and reliable AI systems as demanded by emerging regulatory frameworks.

What the Document Says

• The importance of risk assessment for safe and reliable AI systems is emphasized due to societal requirements and emerging regulatory frameworks.
• The paper reviews the global regulatory landscape driving the need for systematic AI risk assessment.
• AI-related risks, including technical failures, ethical impacts, and social effects, are characterized in the literature.
• Key risk assessment methodologies proposed for AI systems are reviewed, focusing on general frameworks.
• Best practices and areas for further research on AI risk assessment are highlighted.

Key Concepts Mentioned

• AI
• risk assessment
• regulatory frameworks
• safe and reliable AI systems

Claims Made by the Document

• There is a need for systematic AI risk assessment due to societal requirements and emerging regulatory frameworks.

Evidence Used

• The paper discusses the importance of risk assessment in response to societal and regulatory demands.
• It reviews the worldwide regulatory landscape that drives the need for systematic AI risk assessment.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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.