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Assurance Case Structure for Safety Functions Enabled by Artificial Intelligence

Authors:

Julieth Patricia Castellanos Ardila, Sasikumar Punnekkat

Research group:

Dependable Software Engineering

Publication Type:

Conference/Workshop Paper

Venue:

13th International Workshop on Next Generation of System Assurance Approaches for Critical Systems

Abstract

Artificial intelligence (AI) technologies are being adopted in high-risk systems where malfunctions may have severe consequences. However, AI’s non-deterministic, adaptive, and opaque behavior challenges assumptions of predictability and control, creating uncertainty about whether AI systems will consistently avoid harm. This challenge is recognized by regulatory frameworks, especially the European Union’s (EU) AI Act, which establishes mandatory assurance requirements for high-risk AI systems. Commonly, EU regulations are implemented via harmonized standards, which operationalize assurance expectations. At the same time, delays in the availability of harmonized standards under the EU AI Act, together with recent EU simplification initiatives, reinforce the need for pre-harmonization approaches relying on currently available standardization guidance and assurance practices. This paper addresses safety argumentation for safety functions enabled or supported by AI, i.e., functions necessary to achieve or maintain a safe state. Our major research contribution is twofold. First, we propose AIFAL (AI Function Assurance Level), a conceptual construct that is built upon the AI classification guidance established in ISO/IEC TR 5469. Second, we define a modular assurance case structure that embeds AIFAL within the safety argument through claims, strategies, and contextual assumptions. The structure is expressed using Goal Structuring Notation (GSN). We also illustrate the structure by applying it to the justification of an AI-enabled coordination function for System-of-Systems (SoS) operations at a construction site. Together, these contributions support standards-informed safety arguments that integrate AI-specific assurance concerns

Bibtex

@inproceedings{Castellanos Ardila7458,
author = {Julieth Patricia Castellanos Ardila and Sasikumar Punnekkat},
title = {Assurance Case Structure for Safety Functions Enabled by Artificial Intelligence},
month = {September},
year = {2026},
booktitle = {13th International Workshop on Next Generation of System Assurance Approaches for Critical Systems},
url = {http://www.es.mdu.se/publications/7458-}
}