@inproceedings{driouich-etal-2026-gaptocompliance,
title = "{G}ap{T}o{C}ompliance: A Taxonomy-Driven Agentic {AI} Framework for Automated {AI} Act Gap Analysis and Remediation",
author = "Driouich, Ilias and
Cao, Hongliu and
Thomas, Eoin",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preoțiuc-Pietro, Daniel and
Spanakis, Gerasimos and
Xu, Shanshan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nllp-1.38/",
pages = "456--467",
ISBN = "979-8-89176-494-1",
abstract = "Assessing regulatory compliance for high-risk AI systems requires systematic identification of technical gaps such as missing controls, inadequate processes, or shallow evaluation of practices. Compliance officers typically administer structured questionnaires collecting evidence of current practices, where organizations respond with descriptions of their existing processes, technical capabilities, and architectural choices. However, translating these compliance questionnaire responses into typed, actionable gap diagnoses remains unsystematic. In this paper, we present a literature-grounded taxonomy of technical compliance gaps covering areas such as accuracy, robustness, logging, and human oversight, and embed it in \textbf{GapToCompliance}, a two-stage agentic framework coordinating two specialized agents: a \textit{Gap Extraction Agent} that performs hierarchical reasoning from compliance areas to specific gap types, and a \textit{Remediation Planning Agent} that ranks remediation actions from a remediation catalog via multi-signal scoring combining semantic similarity, regulatory alignment, and effort estimation. We evaluate this framework against four automatic compliance gap detection baselines across 9 AI systems and 6 AI families. Our approach achieves a gap-type F1 of 0.831 (vs. 0.533 for the strongest baseline) and remediation P@5 of 0.872 (vs. 0.635 for retrieval methods), demonstrating promising potential for automated compliance workflows. The structured agent coordination reveals a path toward human-in-the-loop compliance systems where automated gap extraction reduces expert burden while maintaining auditability, and the taxonomy-driven design enables extension to emerging regulatory frameworks beyond the EU AI Act."
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%0 Conference Proceedings
%T GapToCompliance: A Taxonomy-Driven Agentic AI Framework for Automated AI Act Gap Analysis and Remediation
%A Driouich, Ilias
%A Cao, Hongliu
%A Thomas, Eoin
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F driouich-etal-2026-gaptocompliance
%X Assessing regulatory compliance for high-risk AI systems requires systematic identification of technical gaps such as missing controls, inadequate processes, or shallow evaluation of practices. Compliance officers typically administer structured questionnaires collecting evidence of current practices, where organizations respond with descriptions of their existing processes, technical capabilities, and architectural choices. However, translating these compliance questionnaire responses into typed, actionable gap diagnoses remains unsystematic. In this paper, we present a literature-grounded taxonomy of technical compliance gaps covering areas such as accuracy, robustness, logging, and human oversight, and embed it in GapToCompliance, a two-stage agentic framework coordinating two specialized agents: a Gap Extraction Agent that performs hierarchical reasoning from compliance areas to specific gap types, and a Remediation Planning Agent that ranks remediation actions from a remediation catalog via multi-signal scoring combining semantic similarity, regulatory alignment, and effort estimation. We evaluate this framework against four automatic compliance gap detection baselines across 9 AI systems and 6 AI families. Our approach achieves a gap-type F1 of 0.831 (vs. 0.533 for the strongest baseline) and remediation P@5 of 0.872 (vs. 0.635 for retrieval methods), demonstrating promising potential for automated compliance workflows. The structured agent coordination reveals a path toward human-in-the-loop compliance systems where automated gap extraction reduces expert burden while maintaining auditability, and the taxonomy-driven design enables extension to emerging regulatory frameworks beyond the EU AI Act.
%U https://aclanthology.org/2026.nllp-1.38/
%P 456-467
Markdown (Informal)
[GapToCompliance: A Taxonomy-Driven Agentic AI Framework for Automated AI Act Gap Analysis and Remediation](https://aclanthology.org/2026.nllp-1.38/) (Driouich et al., NLLP 2026)
ACL