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The Governance-Aware Retriever Framework (GnARF) enables public organizations to align generative AI deployment with democratic values, ensuring secure, auditable, and sovereign data management while mitigating risks like vendor lock-in and data breaches. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

May 26, 2026Public Organization Review0 citationsOpen Access

AI Sovereignty: Redundant Use of Large Language Models for Public Sector Resilience

PLPaul F. LangerUniversity of Applied Sciences Worms SHStefan HaagUniversity of Applied Sciences Worms

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Abstract

Abstract Public institutions using generative Artificial Intelligence (AI) face risks like vendor lock-in, data breaches, and opacity, threatening digital sovereignty. This study introduces the Governance-Aware Retriever Framework (GnARF) to bolster institutional resilience. It integrates five core components: (1) Query Model Allocation (QMA) to reduce vendor dependency; (2) Response Extraction (3) Retrieval-Augmented Generation (RAG) to ground outputs; (4) Decision Logging for transparency and (5) a Personally Identifiable Information (PII) Filtering for privacy compliance. This modular framework enables public organizations to align AI deployment with democratic values, ensuring secure, auditable, and sovereign data management.

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