YAPAY ZEKÂ TABANLI MUHASEBEDE GİZLİLİK ENDİŞELERİNİ AZALTMAK: YÖNETİŞİM VE PAZARLAMA PERSPEKTİFİ

Introduction The rapid integration of artificial intelligence (AI) technologies into accounting and financial management has transformed the traditional paradigms of data processing, auditing, and reporting. AI-based accounting applications offer substantial benefits in terms of automation, accuracy, and analytical depth. However, the increasing reliance on AI systems has also raised concerns about the protection of confidential and sensitive financial data. The confidentiality of such data—ranging from client financial statements to internal operational records—is fundamental to the accounting profession. The risk of unintentional disclosure through AI-driven tools, particularly those using cloud-based large language models (LLMs), represents a significant challenge to professional ethics, corporate reputation, and regulatory compliance. Previous studies have largely focused on the performance and efficiency outcomes of AI adoption in accounting rather than on the psychological and structural barriers, such as “fear of confidential data exposure,” that hinder adoption. This research aims to address this theoretical and empirical gap by exploring the governance, architectural, and marketing mechanisms that can effectively reduce privacy-related concerns in AI-based accounting systems. Specifically, the study examines how data governance frameworks, secure AI architectures, and transparency-oriented communication strategies contribute to building organizational and stakeholder trust. Existing literature provides limited guidance on how accounting organizations can operationalize privacy assurance through a combination of technical, managerial, and communicative practices. By synthesizing perspectives from information governance, trustworthy AI frameworks, and marketing communication theory, this study offers an integrated model that connects internal compliance with external trust formation. The scope of this study is conceptual and analytical; it does not rely on a single case but rather on cross-sectoral insights derived from the most recent global standards and empirical evidence. This research contributes to the accounting and information systems literature by (i) reframing privacy fear as a managerial and communicative challenge rather than solely a technical one, (ii) proposing a governance-based trust framework, and (iii) identifying best practices applicable to both regulatory compliance and brand-level reputation management. The limitations of the study are associated with its conceptual nature and the evolving state of global AI regulations, yet its comprehensive scope provides a strong basis for future empirical validation. Methodology The study employs a qualitative, integrative literature review methodology. This approach enables the synthesis of findings from diverse domains, including accounting ethics, data governance, information systems, cybersecurity, and marketing communication. Sources were systematically selected from peer-reviewed journals indexed in Scopus, Web of Science, and TR Dizin between 2020 and 2025, ensuring contemporary relevance. Key documents include the General Data Protection Regulation (GDPR), Turkey’s Personal Data Protection Law (KVKK), and the European Union’s AI Act Draft, as well as industry reports from Deloitte, PwC, Thomson Reuters, and Zscaler. The review followed a three-stage process: Identification: Collection of academic and industry publications on AI in accounting, data privacy, and trust management. Thematic Analysis: Classification of findings under three major dimensions—governance, architecture, and marketing/communication. Synthesis: Integration of best practices and conceptual models into a coherent framework aimed at reducing privacy-related fears. No primary data were collected; however, the analysis incorporated real-world cases, such as Samsung’s 2023 internal data leakage incident caused by AI misuse, as illustrative examples of risk perception and governance failure. Analytical reasoning was supported by triangulating academic literature with professional standards issued by AICPA, IFAC, and ISO 27001. The methodological design aligns with the qualitative interpretive paradigm, emphasizing contextual understanding over quantification. This design allows the identification of causal mechanisms and conceptual linkages between organizational behavior (trust, communication) and technological configurations (privacy-by-design systems). Findings and Discussion 1. Governance and Regulatory Alignment The study finds that robust data governance mechanisms form the foundation for reducing privacy-related fear in AI-driven accounting. Governance frameworks should incorporate data classification systems, access control hierarchies, and privacy impact assessments (DPIA). Organizations adopting AI must designate Data Protection Officers or equivalent roles to monitor compliance with both GDPR and national privacy laws. Effective governance extends beyond compliance—it establishes accountability and transparency as cultural norms. Furthermore, “privacy by design and by default” principles emerge as critical governance strategies. By embedding privacy considerations at the system design phase, organizations can prevent rather than react to data leaks. The literature emphasizes that data minimization— collecting and processing only essential information— reduces exposure risk without undermining analytical quality. Human oversight also plays an important role: human-in-the-loop governance ensures ethical decisionmaking in AI-assisted accounting workflows, especially when processing sensitive or legally binding financial transactions. 2. Architectural Solutions and Technical Safeguards At the technological level, several architectural configurations demonstrate strong potential in mitigating privacy concerns. The Retrieval-Augmented Generation (RAG) model allows AI systems to access internal databases dynamically without embedding confidential data within the model itself, thus maintaining institutional data sovereignty. Similarly, on-premise deployments—where AI models operate within a company’s secured network infrastructure—eliminate dependency on third-party cloud providers, thereby minimizing external exposure risks. Another significant finding concerns zero data retention (ZDR) policies, which ensure that input data and user prompts are never stored after processing. This approach directly addresses the user’s core fear: “Will my data remain somewhere beyond my control?” In addition, encryption and multi-factor authentication were identified as essential baseline security mechanisms. Together, these architectural measures translate technical privacy protection into tangible organizational trust. 3. Communication and Marketing Dimensions The analysis reveals that privacy fear is not only technical but also perceptual. Hence, marketing and communication strategies play a crucial role in mitigating it. Transparent communication about AI usage, limitations, and safeguards fosters confidence among clients and regulatory bodies. Leading organizations such as Microsoft, Cisco, and PwC have adopted Trust Centers—dedicated web platforms displaying their data protection policies, certifications, and audit results. In the accounting context, firms are encouraged to establish similar AI Trust Portals that outline how data are processed, stored, and secured. Moreover, incorporating privacy assurances into marketing messages—such as “AI with 100% data confidentiality”—can reinforce a firm’s reputation for ethical technology use. Independent assurance reports, such as AI Trust Audits by third-party certifiers, further enhance credibility. The study also emphasizes internal communication: employees should receive continuous training on the ethical use of AI systems to prevent accidental disclosures. This approach transforms privacy from a compliance burden into a shared corporate value. 4. Integration Across Dimensions A key insight is that governance, architecture, and marketing are interdependent components of a unified trust ecosystem. Technical protection mechanisms are effective only when communicated transparently, and communication remains credible only when backed by concrete technical and regulatory compliance. Hence, reducing privacy fear requires a systemic alignment among technology, ethics, and organizational communication. Future Research Directions Despite its comprehensive conceptual scope, this study has certain limitations. It does not empirically test the proposed framework, and therefore, future research should employ quantitative and mixed-method approaches to validate the model. For example, surveys measuring accountants’ or clients’ perceived privacy risks before and after implementing governance-based AI frameworks could provide empirical support. Additionally, future studies could explore cross-cultural variations in privacy perceptions across jurisdictions with differing data protection laws. Comparative analyses between regions under GDPR, CCPA (California Consumer Privacy Act), and KVKK could yield valuable insights. Another promising avenue involves longitudinal case studies examining how organizations evolve their privacy communication strategies over time. The integration of behavioral economics and cognitive psychology into AI governance studies could further elucidate how trust and fear dynamics influence technology adoption in the accounting profession. Conclusion and Contributions This study concludes that privacy-related fear in AI-based accounting cannot be resolved through technological innovation alone. Instead, a multi-dimensional strategy encompassing governance, architecture, and marketing— is essential for ensuring sustainable and trustworthy AI adoption. From a theoretical perspective, the research contributes to the literature by reframing data privacy fear as an interdisciplinary construct that intersects information systems, accounting ethics, and marketing communication. It introduces a governance-based trust framework that connects regulatory compliance with stakeholder perception management. From a practical perspective, the study provides actionable recommendations for accounting firms and technology providers: Establish data governance committees and conduct regular privacy impact assessments. Adopt RAG, on-premise, and zero-retention models to preserve institutional data sovereignty. Develop transparent communication channels, including Trust Centers and independent assurance reports, to demonstrate accountability. Incorporate privacy and ethical assurance messages into branding and client relations strategies. Ultimately, the study advances a holistic understanding of how privacy fears can be alleviated through alignment between technology, management, and communication. By embedding these principles, accounting organizations can uphold both efficiency and ethical responsibility— laying the foundation for a trustworthy, AI-enabled future in financial services.