Recent studies have increasingly emphasized the transformative role of Artificial Intelligence (AI) and Cloud-Based Accounting Information Systems (AIS) in enhancing the reliability of financial reporting and strengthening cybersecurity in the digital era. Sociotechnical Systems Theory (STS) serves as a foundational lens for understanding the integration of these technologies, advocating for the joint optimization of both social (human, organizational) and technical (AI, cloud) subsystems (Twyford & Abbas, 2023). From a technical standpoint, AI enhances reliability by automating repetitive tasks, detecting fraud, and facilitating real-time decision-making based on big data analytics (Yan & Ji, 2024). Cloud-based AIS further supports this by enabling remote access, scalability, and collaborative financial reporting, which contributes to increased transparency and consistency (Vo Van et al., 2024; Wahhab et al., 2024). However, technical advancement alone is insufficient.

STS highlights the need for participatory system design, user training, and ethical governance to ensure effective adoption and minimize risks such as algorithmic bias, system misuse, or organizational resistance (Lacmanovic & Skare, 2025; Ruissalo et al., 2022). In terms of cybersecurity, the literature indicates that AI-driven fraud detection systems and secure cloud infrastructures improve resilience against cyber threats, but their effectiveness depends heavily on organizational maturity, governance policies, and the implementation of sociotechnical safeguards (Abu-Dabaseh et al., 2025; Mauri & Damiani, 2022). Thus, aligning the technological capabilities of AI and cloud systems with ethical, behavioral, and regulatory dimensions is essential to achieving reliable and secure accounting practices in the digital landscape.

Artificial Intelligence (AI) is a field of computer science focused on creating systems that perform tasks requiring human intelligence, such as learning, reasoning, and problem-solving (Darda & Pendse, 2025; L. Zhang et al., 2021). Key technologies include machine learning, deep learning, natural language processing, computer vision, and robotics, enabling AI to analyze data, interact with language, and operate autonomously (Akpabio et al., 2024; Gehlot & Rana, 2024). AI is widely used in healthcare, education, transportation, industry, and online services, improving efficiency, accuracy, and personalization (Desmal et al., 2023; Jindal et al., 2021). However, AI also raises ethical concerns like bias, privacy, and job displacement, prompting the need for transparent and responsible use (Kamila & Jasrotia, 2025; Sethy et al., 2023). As AI evolves, especially with integration into emerging technologies and the pursuit of Artificial General Intelligence (AGI), its relevance across sectors will continue to grow, demanding careful attention to its societal impacts (Jagatheesaperumal et al., 2022; Meher, 2024).

Cloud-based accounting information systems (AIS) use cloud computing to improve the efficiency, scalability, and security of financial processes (Ding, 2024; Gade & Rao, 2022). These systems reduce infrastructure costs by eliminating the need for physical servers and allow real-time data access for faster decision-making (Thaher, 2024). They offer flexibility, enabling businesses to scale operations and support remote access for enhanced collaboration (Asatiani & Penttinen, 2015; Lutfi, 2022). Advanced encryption and regular updates provided by service providers also strengthen data security and reliability (Ding, 2024; Yalamati, 2024). For small and medium-sized enterprises (SMEs), cloud-based AIS reduce the need for external auditors and support better financial control (Vo Van et al., 2024; Zebua & Widuri, 2023). However, implementing cloud-based AIS requires careful planning, system integration, and staff training to avoid data migration issues (Khoruzhy et al., 2023; Thaher, 2024). Businesses must also ensure compliance with financial regulations and secure sensitive data (Chen & Xu, 2024; Yalamati, 2024). The integration of technologies such as blockchain and artificial intelligence further enhances these systems by automating processes and improving financial accuracy (Alkan, 2022; Ionescu, 2021). Going forward, making cloud-based AIS more accessible to SMEs will be key to fostering financial efficiency and sustainable growth (Noordin et al., 2024; Vo Van et al., 2024).

Financial reporting reliability refers to the accuracy and trustworthiness of financial information, ensuring it reflects the true financial position of an entity (Maines & Wahlen, 2006). This reliability is essential for stakeholders who depend on financial reports for decision-making (Al-Ramahi & Binsaddig, 2024). The adoption of IFRS improves transparency and comparability but may initially reduce accrual reliability due to implementation challenges (Lai et al., 2013; Quoc et al., 2024). High audit quality, effective audit committees, and strong internal controls enhance reporting reliability by minimizing errors and manipulation (Ibrahim et al., 2024; Lustrilanang et al., 2023; Xi et al., 2019). Regulatory frameworks, like anti-money laundering measures, also support financial integrity (Abdulkareem Al yasiri et al., 2024). However, subjectivity in estimates and changes in accounting standards may still create inconsistencies (Erb & Pelger, 2015; Lugovskу & Kuter, 2020). Overall, reliable reporting is vital for investor confidence, market stability, and regulatory compliance.

Cybersecurity is the practice of protecting systems, networks, and data from unauthorized access and cyberattacks, ensuring confidentiality, integrity, and availability (Firmansyah, 2024; Tiwari et al., 2023). Key concepts include authentication, encryption, access control, and threat detection (Firmansyah, 2024; Tiwari et al., 2023). It is vital for safeguarding sensitive information, preventing data breaches, and maintaining organizational trust (Kapoor et al., 2024; Paliwal, 2023). Common threats include malware, phishing, ransomware, DDoS attacks, and insider threats (Kumar & Mandoria, 2024; Shinde et al., 2024). To mitigate these, organizations use tools like IDS/IPS, endpoint protection, and incident response plans, supported by user education (Malasowe et al., 2024; Tiwari et al., 2023). Emerging challenges include quantum computing, regulatory compliance, and the need for skilled professionals, while AI and machine learning are increasingly used for proactive defense (Bhosale et al., 2023; Chandre et al., 2024; Ullah et al., 2024). Continuous innovation and awareness are essential to ensuring cybersecurity resilience (Kumar Jain et al., 2024).

The Systematic Literature Review (SLR) method in this study refers to the PRISMA guide which broadly provides stages of research selection, starting from the initial discovery of articles through the Scopus database. The following are the filtering stages carried out to obtain a collection of sources according to the criteria of this study.

Table 1. Inclusion and Exclusion Criteria in Source Selection

From the criteria that have been determined, it is continued with the stage of selecting the number of sources that have been obtained by the provisions of the criteria that have been mentioned in the table so that they become samples in this study. These stages can be described through the following PRISMA diagram.

Figure 1. PRISMA diagram.

The majority of studies (61%) emphasized the beneficial impact of integrating AI and cloud technologies into accounting information systems. These positive impacts were primarily centered around three domains. First, the implementation of AI significantly improved the reliability of financial reporting by reducing human error and automating complex tasks such as real-time reconciliation, fraud detection, and predictive analysis. Second, AI-enhanced cloud systems strengthened cybersecurity through tools such as real-time threat detection, encryption algorithms, and blockchain integration. Third, operational efficiency was improved as cloud-based infrastructures enabled system scalability, regulatory compliance, and cost savings through centralized maintenance and updates. These findings align well with Sociotechnical Systems Theory, which highlights the importance of harmonizing human and technological subsystems to achieve optimal performance. Organizations that succeeded in this integration reaped benefits not only in system functionality but also in trust and transparency in financial reporting.

However, 11 studies (39%) pointed to notable challenges and risks associated with these technologies. Cybersecurity vulnerabilities were a recurring theme, particularly the risk of adversarial attacks and reliance on third- party platforms that may not be fully secure. Ethical concerns such as data privacy, algorithmic bias, and lack of transparency in AI decision-making were also prevalent. Additionally, technical and human resource barriers such as integration difficulties with legacy systems and resistance from users due to limited AI literacy were cited as major obstacles. These negative findings illustrate the sociotechnical misalignments that can occur when technological systems outpace organizational readiness or regulatory frameworks.

In conclusion, the PRISMA diagram validates the methodological robustness of this literature review and reflects a well-balanced spectrum of findings. While the integration of AI and cloud-based AIS offers transformative potential for enhancing financial reporting reliability and cybersecurity, it also introduces complex risks that must be addressed through ethical oversight, technical adaptability, and human-centered design. This underscores the necessity of applying a sociotechnical lens to the ongoing digital transformation of accounting practices, ensuring that technological advancement is matched by institutional preparedness and stakeholder engagement.

Table 2. Previous research on Artificial Intelligence & Cloud-Based Accounting Information Systems

The integration of Artificial Intelligence (AI) and cloud-based Accounting Information Systems (AIS) has transformed financial reporting, improving its reliability and adaptability in the digital age. From the lens of Sociotechnical Systems Theory (STS), this advancement must be viewed not only through technological innovation but also in terms of how these tools interact with human, organizational, and regulatory components.

AI technologies such as machine learning, natural language processing, and robotic process automation reduce manual errors, enhance data processing consistency, and improve fraud detection (Awad et al., 2025; Kuaiber et al., 2024). From a technical subsystem perspective, these capabilities significantly enhance the efficiency and accuracy of information flows, which is critical for financial reporting. However, STS emphasizes that technology alone cannot ensure reliability; it must be harmonized with the human (social) subsystem, including accountants, managers, and auditors, whose trust in AI systems and ability to interpret AI outputs are vital (Rahahleh et al., 2021; Askary et al., 2018).

AI also facilitates predictive analytics and strategic forecasting, offering powerful decision-support capabilities (Shakdwipee et al., 2023; Türegün, 2025). Yet, in line with STS, the full value of these tools is realized only when the organizational culture and skills of professionals are aligned with these innovations. Effective training, cross-functional collaboration, and a shared understanding of how AI works are necessary to bridge the gap between technical potential and actual organizational impact (Al-Sammarraee & Alshareeda, 2021).

Cloud-based AIS, on the other hand, represent the technical infrastructure that enables real-time data processing and global access, boosting the timeliness and transparency of financial reports (Alkan, 2022; Ionescu, 2022). STS suggests that such systems must be embedded within a broader organizational workflow that supports flexible work environments, decentralization, and responsive decision-making. This is especially important for SMEs, where cloud systems can democratize access to high-quality reporting tools (Cazazian, 2022). However, social subsystems must evolve as well adopting new governance models, data stewardship roles, and trust mechanisms to manage the shared nature of cloud environments.

Moreover, when integrated with blockchain and encryption technologies, cloud-based AIS enhance data security and integrity (Alkan, 2022). In STS terms, these technologies form part of the technical subsystem that supports risk management, but their effectiveness also depends on how well users adhere to protocols, understand security risks, and collaborate to maintain system resilience.

The synergy between AI and cloud-based AIS creates a dynamic sociotechnical ecosystem where advanced analytics and automation co-exist with human judgment and governance. This alignment strengthens financial reporting reliability by balancing technical performance (e.g., automation, speed, accuracy) with social values (e.g., accountability, transparency, adaptability) (Ali et al., 2024).

However, STS highlights that misalignment between subsystems can introduce new risks. For instance, AI systems may introduce algorithmic bias or opacity, leading to ethical and interpretability concerns if not matched with appropriate human oversight and regulation (Kuaiber et al., 2024; Shakdwipee et al., 2023). Similarly, cloud migration can create dependency on third-party vendors, requiring new roles and policies to ensure continuity and compliance.

Implementing these systems also demands significant organizational change, including restructuring job roles, retraining staff, and redefining workflows. STS stresses that ignoring the social impacts—such as employee resistance, ethical dilemmas, or loss of human oversight can undermine the very reliability these technologies aim to enhance (Clerkin & McConville, 2022; Minz, 2024).

Ethical challenges such as data privacy, transparency in AI decision-making, and the displacement of accounting roles must be addressed through inclusive governance frameworks that emphasize explainability, fairness, and social accountability (Bani Ahmad, 2024; Yadav et al., 2023). These concerns reflect STS's central principle that technical systems are embedded in broader social contexts, and their success depends on how well they are integrated into those contexts.

Ultimately, from the Sociotechnical Systems Theory perspective, the integration of AI and cloud-based AIS enhances the reliability of financial reporting not merely through technological superiority but through the balanced co-evolution of social and technical subsystems. Achieving trustworthy, transparent, and sustainable financial practices in the digital era requires socio- organizational readiness, ethical stewardship, and continuous adaptation not just advanced tools.

Cloud-based accounting information systems (CAIS) play a critical role in safeguarding financial data against an evolving landscape of cyber threats. In today’s digitized financial environment, their function extends beyond storage and processing to encompass dynamic, adaptive security frameworks. Equipped with advanced encryption protocols such as RSA and AES, CAIS ensure that financial data remains secure both in transit and at rest (Gopalsamy & Karthick, 2019; Shchyrba et al., 2025). Multi-factor authentication and role-based access controls further fortify system security by limiting access to authorized users only (Shchyrba et al., 2025).

From the perspective of Sociotechnical Systems (STS) Theory, which views organizations as composed of interacting social and technical subsystems, the security effectiveness of CAIS is contingent not just on the technological safeguards in place, but also on the social context in which these systems are embedded. While encryption, intrusion detection systems (IDS), and AI-powered monitoring tools represent the technical subsystem, their success relies heavily on human behavior, organizational policies, and regulatory alignment—the social subsystem.

CAIS platforms are increasingly developed in alignment with global cybersecurity regulations, such as those enforced by the Financial Conduct Authority (FCA) and the Monetary Authority of Singapore (MAS), to ensure robust data protection (Shchyrba et al., 2025). This regulatory compliance strengthens institutional trust and reinforces an organizational culture that values cybersecurity as a shared responsibility. According to STS theory, this reflects the principle of joint optimization, where both technical capabilities and social structures must be designed in harmony to achieve optimal performance. Operationally, CAIS reduce overhead costs by centralizing updates and security maintenance in the cloud (Ding, 2024; Qureshi et al., 2024). However, these efficiencies can only be realized if the workforce is adequately trained to engage with new access protocols, interpret system alerts, and respond to cyber incidents. For example, AI-powered anomaly detection is only valuable when users are trained to act upon alerts quickly and effectively. This interdependence reflects the STS emphasis on mutual adaptation, where human roles evolve alongside technological changes.

In addition, CAIS’s integration with real-time threat detection tools and automated monitoring systems enables organizations to detect cyber anomalies early and mitigate breaches before they escalate (Kim et al., 2019; Felix et al., 2024). Yet, STS theory warns that over-reliance on automation without human oversight can create blind spots. Ethical and procedural challenges arise when AI-based systems make opaque decisions, emphasizing the need for explainability and governance within the social subsystem to ensure accountability.

Emerging solutions such as the integration of blockchain technology further enhance CAIS security by offering immutable, transparent records of financial transactions (Zadorozhnyi et al., 2021). When implemented within a sociotechnical framework, blockchain strengthens both the technical infrastructure and the institutional trust mechanisms that underlie financial reporting. For instance, blockchain can secure transaction integrity, but only if organizational practices and auditor roles are adjusted to interpret and validate these new forms of digital records.

Despite their potential, CAIS are not without vulnerabilities. Issues such as unauthorized access, insider threats, and misconfigured cloud settings often result from gaps in the social subsystem including poor governance, insufficient training, or unclear responsibility for cybersecurity roles (Al-Nsour et al., 2021). STS theory emphasizes that mitigating these risks requires continuous alignment of technical advancements with human capabilities, ethical norms, and institutional structures.

In conclusion, cloud-based AIS contribute significantly to the protection of financial data through advanced technological defenses and compliance frameworks. However, from a Sociotechnical Systems Theory perspective, their true effectiveness depends on the harmonious integration of secure technologies with competent, well-trained users, robust organizational policies, and a strong culture of accountability. Only through the joint optimization of both technical tools and social practices can CAIS fulfill their potential as secure, resilient pillars of financial data protection in the digital era.

The integration of Artificial Intelligence (AI) into cloud-based Accounting Information Systems (AIS) has significantly advanced the efficiency and accuracy of financial reporting. However, this integration also introduces a range of vulnerabilities particularly in cybersecurity, data privacy, algorithmic bias, and legal compliance that must be critically examined through the lens of Sociotechnical Systems (STS) Theory.

From a technical standpoint, AI-integrated AIS are susceptible to sophisticated cyber threats, including adversarial attacks that manipulate AI inputs to generate false outputs or unauthorized access (Bani Ahmad, 2024). Their reliance on interconnected platforms, including third-party APIs, introduces additional attack vectors that can compromise data integrity (Mustafa et al., 2024). These technical vulnerabilities cannot be fully addressed in isolation. STS theory asserts that system reliability depends on the interdependence of both technical tools and the social systems that govern their use. For example, while advanced encryption and multi-factor authentication are necessary defenses, their effectiveness depends on user compliance and awareness, reflecting the critical role of the human subsystem.

AI also introduces ethical and privacy concerns, especially when trained on large, heterogeneous datasets that can lead to unauthorized disclosures of sensitive information or perpetuate algorithmic bias (Rodriguez-Barboza et al., 2024). STS theory emphasizes the importance of ethical alignment between technology and organizational values. If the social structures such as ethics guidelines, data governance policies, or user training are underdeveloped, even the most advanced AI can generate biased or non-transparent outputs that undermine trust and accountability (Zhang et al., 2023; Moreira et al., 2025).

The issue of explainability highlights another STS principle: joint optimization. Many AI models embedded in AIS operate as “black boxes,” making their decision-making processes opaque to users and regulators. This lack of transparency undermines the auditing and regulatory oversight functions essential to financial systems. Technical solutions such as explainable AI (XAI) must be matched by social practices such as revised audit methodologies, regulatory literacy, and stakeholder engagement to foster understanding and accountability across the system.

Legal and regulatory frameworks, including the European Union's Artificial Intelligence Act (AIA), are also placing increased pressure on organizations to ensure compliance in AI usage (Bertolini, 2024). STS theory suggests that organizational responses must go beyond adopting compliant technologies. They must cultivate adaptive social subsystems that include trained legal teams, compliance officers, and culturally embedded accountability structures to navigate rapidly evolving regulations.

From a systems integration perspective, challenges such as data inconsistency, legacy infrastructure incompatibility, and lack of interoperability are not purely technical barriers they are also symptoms of inadequate communication and coordination across departments (Türegün, 2025). STS theory identifies this as a misalignment between social workflows and technical system design, which can lead to breakdowns in system performance, delayed financial reporting, or increased error rates.

To mitigate these multidimensional vulnerabilities, organizations must adopt strategies that reflect the sociotechnical complexity of AI-integrated AIS.

Technological safeguards including end-to-end encryption, anomaly detection, blockchain-based audit trails, and AI-driven threat detection should be accompanied by robust social practices. These include regular ethics training, reskilling programs, transparent data governance policies, and inclusive stakeholder dialogue (Choudhary et al., 2023; Mathai & Mathew, 2024).

Moreover, adopting continuous monitoring through both technical means (e.g., automated security updates, real-time audits) and human oversight (e.g., independent third-party audits, ethical review boards) ensures system adaptability and resilience. This dual oversight aligns with the STS principle of equifinality, which allows organizations to achieve the same goal system security through multiple coordinated pathways.

To summarize, the vulnerabilities posed by AI-integrated cloud AIS are not merely technical in nature but stem from the complex interplay between people, technology, and organizational systems. Applying Sociotechnical Systems Theory reveals that achieving trustworthy, secure, and efficient financial reporting requires joint optimization of both technological tools and human- centered practices. Effective mitigation strategies must integrate cybersecurity infrastructure, legal compliance, ethical governance, and workforce engagement to build resilient, transparent, and accountable financial ecosystems in the digital era.