Assessing High-Risk Systems: An EU AI Act Verification Framework
The implementation of the AI Act and related regulations in the EU faces a significant challenge: the absence of a systematic approach for verifying legal mandates. Recent surveys indicate that this regulatory ambiguity is a considerable burden, leading to inconsistent readiness across Member States. This article proposes a comprehensive framework designed to bridge this gap by organizing compliance verification along two fundamental dimensions: the type of method (controls vs. testing) and the target of assessment (data, model, processes, and final product).
Introduction
The rapid advancement of Artificial Intelligence (AI) has opened up significant opportunities while simultaneously creating new technical, organizational, and regulatory challenges. This has intensified the need for trustworthy and well-governed systems. Recent regulatory initiatives, particularly the EU AI Act, have introduced a comprehensive set of legal obligations for high-risk AI systems. However, a persistent gap continues to exist between these normative requirements and the technical means available for demonstrating and verifying compliance.
Translating high-level principles—such as fairness, robustness, and transparency—into measurable testing and assurance activities remains a considerable challenge. This requires structured methodologies, interoperable assessment frameworks, and sustained collaboration across technical, legal, and ethical domains. The uncertainty surrounding the Act’s practical implementation is increasingly recognized as a major challenge. This uncertainty manifests at multiple levels, including interpretive, operational, and procedural uncertainties, highlighting the need for operational tools capable of translating legal expectations into verifiable activities.
Motivation
This framework aims to bridge the gap between regulators, risk managers, developers, technical testers, and certifiers, who often operate with different vocabularies and processes. Establishing a shared operational framework can facilitate communication, reduce duplication of effort, and support coordinated assurance practices across the AI lifecycle.
Focus on High-Risk AI Systems
High-risk AI systems are subject to the most extensive obligations under the Act. Prohibited practices are banned outright, while low-risk systems are primarily subject to limited transparency duties. Concentrating on high-risk systems allows for a detailed examination of how legal obligations can be decomposed into testable, verifiable elements, forming the basis for scalable compliance approaches.
Guiding Questions
This article is structured around three guiding questions:
- How can high-level legal obligations be systematically translated into operational components that are testable and verifiable?
- Which dimensions, artifacts, and assessment methods are needed to support a shared operational language among stakeholders?
- How can a unified assessment structure enhance comparability, traceability, and communication across different stakeholders?
Proposed Framework
The proposed framework is not intended to be complete or definitive but offers a structured methodology intended to evolve alongside regulatory guidance and standardization efforts. It serves as a coherent starting point that can be progressively refined as institutional capacities and practices mature.
In addition to presenting the framework conceptually, the article demonstrates its practical application through a real-world use case in the automotive sector. By applying the framework to a high-risk AI system, the paper illustrates how legal obligations can be mapped to technical and procedural controls throughout the system lifecycle.
Background
The AI Act introduces a risk-based framework with four categories: prohibited practices, high-risk systems, limited-risk systems, and minimal-risk systems. Providers of high-risk systems must comply with extensive lifecycle obligations covering risk management, data quality, documentation, transparency, and human oversight. Translating these obligations into verifiable technical criteria remains challenging.
Despite the development of numerous fairness, robustness, and transparency tests, they remain fragmented and loosely connected to regulatory obligations. Recent initiatives have begun to provide some structure, but persistent challenges remain, including misaligned terminology across legal, ethical, and technical communities and limited procedural guidance for assessments.
Methodology
The methodology used to construct the proposed framework identifies and categorizes requirements systematically, ensuring that every control and testing mechanism is traceable from high-level legal principles to concrete, technical methods. Eleven macro-categories of requirements capture the key dimensions of AI trustworthiness and compliance, based on principles defined by the European Commission’s High-Level Expert Group on Artificial Intelligence.
Assessment Dimensions
The analysis of an AI system can be organized along two dimensions of assessment: the type of assessment (controls vs. testing) and the target of assessment (data, model, processes, and final product). This comprehensive model connects organizational assurance with empirical verification across all lifecycle stages.
Mapping Requirements
This section presents the mapping between the macro-requirements of Trustworthy AI and the mechanisms that enable their implementation and verification. Each requirement category is detailed, identifying relevant legal provisions and methods for compliance assessment.
Example Application
The article illustrates the framework’s application in a real-world use case involving an AI-based system for detecting cyberattacks within connected vehicles. This example demonstrates how the framework can be used to structure assurance activities across the full lifecycle of an AI system.
Discussion and Conclusion
The framework operationalizes compliance by establishing explicit correspondences between legal requirements, recognized standards, and assessment protocols. Its stratified architecture articulates how different modes of assurance interrelate throughout the AI system lifecycle. This structure facilitates systematic self-assessment and enhances dialogue between developers and regulatory authorities.
Ultimately, the framework contributes to the longer-term goal of automating compliance verification and offers a foundation for computational systems capable of executing and interpreting appropriate assessment protocols. Future research directions include developing prioritization schemes among requirements and integrating continuous monitoring mechanisms.
This study provides an initial architectural foundation for decomposing AI Act obligations into structured, empirically testable components, seeking to bridge gaps among regulators, risk managers, developers, auditors, and certification bodies.
