BÜYÜK DİL MODELLERİ “ORTAK DENETÇİ” GİBİ HAREKET EDEBİLİR Mİ?

Year 2026, Issue: 34, 174 - 184, 16.02.2026

Hakan Emekci

https://doi.org/10.58348/denetisim.1782835

https://izlik.org/JA63JC78EA

Abstract

Bu çalışma, büyük dil modellerinin (BDM) denetim iş akışlarına "eş denetçiler" olarak entegrasyonunu inceleyerek, kanıt izlenebilirliğini, yönetişimi ve insan hesap verebilirliğini sağlayan çerçevelere entegre edilmesinin gerekliliğini vurgulamaktadır. Yapay zeka destekli denetime artan ilgiye rağmen, önceki çalışmalar BDM'nin teknik yeteneklerini denetim standartları ve düzenleyici uyumluluk gereklilikleriyle sistematik olarak birleştirmemiştir. Denetim doktrini, yapay zeka yönetişim çerçeveleri ve doğal dil işleme araştırmalarını sentezleyen bir anlatı literatür taraması yoluyla, çalışma böyle bir entegrasyonun nasıl sağlanabileceğini incelemektedir.
BDM'ler, mesleki yargının yerini almak yerine, güvence süreçlerini geliştiren denetlenebilir destek sunmaktadır. Hibrit erişim, politika kısıtlamalı üretim ve kriptografik köken gibi özellikleri bir araya getirerek, önerilen mimari hem olgusal güvenilirliği hem de düzenleyici uyumluluğu ele almaktadır. Bulgular, etkili bir BDM uygulamasının standartlarla sıkı bir uyum gerektirdiğinin altını çizmektedir. Sonuç olarak araştırma, denetimde güvenilir yapay zekânın sağlam teknik güvenlik önlemlerine, yönetişim yapılarına ve sürekli insan gözetimine bağlı olduğunu doğrulamaktadır.

Keywords

Büyük Dil Modelleri (BDM) , Ortak Denetçi Mimarisi , Kanıt İzlenebilirliği , Yapay Zeka Yönetişimi

CAN LARGE LANGUAGE MODELS ACT AS “CO-AUDITORS”?

https://izlik.org/JA63JC78EA

Abstract

This study explores the integration of large language models (LLMs) into audit workflows as "co-auditors," emphasizing the necessity of embedding them within frameworks that ensure evidence traceability, governance, and human accountability. Despite growing interest in AI-augmented auditing, prior work has not systematically bridged LLM technical capabilities with audit standards and regulatory compliance requirements. Through a narrative literature review synthesizing audit doctrine, AI governance frameworks, and natural language processing research, the study examines how such integration can be achieved.
Rather than substituting professional judgment, LLMs offer auditable support that enhances assurance processes. By incorporating hybrid retrieval, policy-constrained generation, and cryptographic provenance, the proposed architecture addresses both factual reliability and regulatory compliance. The findings underscore that effective LLM deployment requires strict alignment with standards. Ultimately, the research confirms that trustworthy AI in auditing depends on robust technical safeguards, governance structures, and sustained human oversight.

Keywords

Large Language Models (LLMs) , Co-Auditor Architecture , Evidence Traceability , Artificial Intelligence (AI) Governance

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