@inproceedings{choi-etal-2026-compass,
title = "{COMPASS}: A Framework for Evaluating Organization-Specific Policy Alignment in {LLM}s",
author = "Choi, Dasol and
Lee, DongGeon and
Kartono, Brigitta Jesica and
Berndt, Helena and
Kwon, Taeyoun and
Jang, Joonwon and
Park, Haon and
Yu, Hwanjo and
Kahng, Minsuk",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2139/",
pages = "46087--46133",
ISBN = "979-8-89176-390-6",
abstract = "As large language models are deployed in high-stakes enterprise applications, from healthcare to finance, ensuring adherence to organization-specific policies has become essential. Yet existing safety evaluations focus exclusively on universal harms. We present COMPASS (Company/Organization Policy Alignment Assessment), the first systematic framework for evaluating whether LLMs comply with organizational allowlist and denylist policies. We apply COMPASS to eight diverse industry scenarios, generating and validating 5,920 queries that test both routine compliance and adversarial robustness through strategically designed edge cases. Evaluating seven state-of-the-art models, we uncover a fundamental asymmetry: models reliably handle legitimate requests ({\ensuremath{>}}95{\%} accuracy) but catastrophically fail at enforcing prohibitions, refusing only 13{--}40{\%} of adversarial denylist violations. These results demonstrate that current LLMs lack the robustness required for policy-critical deployments, establishing COMPASS as an essential evaluation framework for organizational AI safety."
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<abstract>As large language models are deployed in high-stakes enterprise applications, from healthcare to finance, ensuring adherence to organization-specific policies has become essential. Yet existing safety evaluations focus exclusively on universal harms. We present COMPASS (Company/Organization Policy Alignment Assessment), the first systematic framework for evaluating whether LLMs comply with organizational allowlist and denylist policies. We apply COMPASS to eight diverse industry scenarios, generating and validating 5,920 queries that test both routine compliance and adversarial robustness through strategically designed edge cases. Evaluating seven state-of-the-art models, we uncover a fundamental asymmetry: models reliably handle legitimate requests (\ensuremath>95% accuracy) but catastrophically fail at enforcing prohibitions, refusing only 13–40% of adversarial denylist violations. These results demonstrate that current LLMs lack the robustness required for policy-critical deployments, establishing COMPASS as an essential evaluation framework for organizational AI safety.</abstract>
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%0 Conference Proceedings
%T COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs
%A Choi, Dasol
%A Lee, DongGeon
%A Kartono, Brigitta Jesica
%A Berndt, Helena
%A Kwon, Taeyoun
%A Jang, Joonwon
%A Park, Haon
%A Yu, Hwanjo
%A Kahng, Minsuk
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F choi-etal-2026-compass
%X As large language models are deployed in high-stakes enterprise applications, from healthcare to finance, ensuring adherence to organization-specific policies has become essential. Yet existing safety evaluations focus exclusively on universal harms. We present COMPASS (Company/Organization Policy Alignment Assessment), the first systematic framework for evaluating whether LLMs comply with organizational allowlist and denylist policies. We apply COMPASS to eight diverse industry scenarios, generating and validating 5,920 queries that test both routine compliance and adversarial robustness through strategically designed edge cases. Evaluating seven state-of-the-art models, we uncover a fundamental asymmetry: models reliably handle legitimate requests (\ensuremath>95% accuracy) but catastrophically fail at enforcing prohibitions, refusing only 13–40% of adversarial denylist violations. These results demonstrate that current LLMs lack the robustness required for policy-critical deployments, establishing COMPASS as an essential evaluation framework for organizational AI safety.
%U https://aclanthology.org/2026.acl-long.2139/
%P 46087-46133
Markdown (Informal)
[COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs](https://aclanthology.org/2026.acl-long.2139/) (Choi et al., ACL 2026)
ACL
- Dasol Choi, DongGeon Lee, Brigitta Jesica Kartono, Helena Berndt, Taeyoun Kwon, Joonwon Jang, Haon Park, Hwanjo Yu, and Minsuk Kahng. 2026. COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 46087–46133, San Diego, California, United States. Association for Computational Linguistics.