Aug 2026· Denetişim· pp. 418-437· 0 citations· 28 references
TL;DR
A comprehensive and risk-based AI audit methodology that integrates governance, data security, model security, and application security dimensions into a unified control framework is proposed, offering organizations a robust tool to manage emerging AI-related risks effectively.
Abstract
The rapid proliferation of artificial intelligence (AI) systems across critical sectors has introduced significant security, privacy, and ethical challenges. Traditional information security audit frameworks remain insufficient to address the unique risks associated with AI technologies, particularly in areas such as data integrity, model robustness, and algorithmic transparency. This study proposes a comprehensive and risk-based AI audit methodology that integrates governance, data security, model security, and application security dimensions into a unified control framework. The proposed methodology is structured around key control domains, including documentation, compliance, access control, application security, and model security, supported by representative control considerations. The proposed methodology supports systematic evaluation of AI audit controls through a structured assurance-oriented framework. Additionally, a risk prioritization approach is introduced to classify controls into different criticality levels, enabling organizations to focus on high-impact vulnerabilities such as data poisoning, model inversion, prompt injection, and sensitive data leakage. The methodology is designed as a step-by-step audit process, including scope definition, control mapping, evidence collection, evaluation, and reporting. This structured approach ensures both technical and organizational aspects of AI systems are systematically assessed. The study contributes to the literature by providing a practical, measurable, and adaptable framework that aligns with regulatory requirements such as data protection laws and ethical AI principles. Overall, the proposed AI audit framework enhances the auditability, transparency, and security of AI systems, offering organizations a robust tool to manage emerging AI-related risks effectively.
Large organizations progressively adopt artificial intelligence (AI) to enhance fraud detection and ensure integrity across financial, operational, and digital systems. Despite its benefits, AI introduces challenges including data integrity risks, bias, clarity issues, and accountability gaps. Weak governance can lead to false alerts, model drift, and system vulnerabilities. This research proposes a multi-layer AI governance framework integrating technical, organizational, and controlling oversight to strengthen fraud detection and integrity assurance. The technical layer implements anomaly detection, machine learning, and explainable AI (XAI) models. The organizational layer sets up policies, accountability structures, and increase protocols, while the regulatory layer ensures compliance with GDPR, ISO standards, and AI-specific regulations. The framework adherence interoperability across layers, real-time monitoring, and adaptive responses to progressing fraud patterns. By combining ethical, procedural, and technical safeguards, the study offers a practical, scalable model that improves discovery accuracy, reduces false positives, and enhances organizational resilience and trust in AI systems. This study presents a multi-layer AI management framework that enhances fraud detection and uprightness assurance in large organizations. The framework combines technical, organizational, and regulatory layers to strengthen anomaly identification, audit completeness, and governance maturity. Adaptive thresholds and advanced AI techniques allow real-time detection with decreased alert fatigue, while blockchain and confederated learning ensure robust data integrity. Despite working challenges such as data imbalance, system latency, and explainability concerns, the framework demonstrates scalable and sustainable performance. The study offers actionable insights for policy formulation, stakeholder training, and ethical AI adoption, supporting resilient, accountable, and responsible organizational systems.
Nabeela Ehsan· Journal of Intelligent Decis...· 0 citations
The results show that XAI can improve the transparency, trustworthiness and effectiveness of AI-based cybersecurity systems, in addition to highlighting a range of privacy, adversarial robustness, scalability and evaluation challenges that warrant further research to ensure reliable deployment in the real world.
Raman Kumar· International Journal of Adv...· 0 citations
Artificial Intelligence (AI) has emerged as one of the most transformative technologies influencing organizational governance, financial oversight, and risk management practices worldwide. The integration of AI into internal audit functions has significantly altered the traditional audit landscape by enhancing operational efficiency, improving fraud detection capabilities, strengthening risk assessment procedures, and enabling real-time auditing practices. This research paper examines the transformative role of AI technologies such as machine learning, neural networks, natural language processing, robotic process automation, and predictive analytics in reshaping internal audit operations. The study explores how AI-driven systems automate repetitive audit tasks, analyze large volumes of structured and unstructured data, and improve audit accuracy while reducing operational costs. Furthermore, the paper evaluates the challenges associated with AI adoption, including ethical concerns, cybersecurity risks, data privacy issues, technological dependence, and skill gaps among auditors. A comparative analysis between traditional and AI-enabled audit practices is also presented to assess the effectiveness and efficiency of AI-based auditing systems. The study concludes that AI is not replacing internal auditors but transforming their roles into more strategic, analytical, and advisory-oriented functions. Organizations that successfully integrate AI into their audit frameworks can achieve greater transparency, stronger governance, and improved organizational resilience in an increasingly digital business environment.
F. Raidah, M. Jobair, Md. Halimuzzaman et al.· American Journal of Financia...· 0 citations
The growing difficulties of security challenges and governance demands in a contemporary societies like Lagos has so much created the need or choice for innovative technological remedies or immediate solutions that can strengthen institutional effectiveness and public accounting. In Nigeria, specifically in Lagos State, rapid urbanisation, population growth, and increasing security threats have exposed many limitations in conventional security management and governance monitoring systems. Furthermore, emerging intelligent digital technologies are increasingly being explored as tools for improving surveillance coordination and transparency, particularly in public administration. This study therefore investigates the extent to which artificial resources can enhance or aid security architecture and improve democratic accountability in Lagos State. The study adopts a descriptive survey research design while data will be collected from security personnel, public administrators, and selected residents with the use of well-structured questionnaire. The research examines key variables such as the level of adoption of Artificial Intelligence (AI)-driven security institutions, and citizen's trust in governance processes. Descriptive and inferential statistical method will equally be used to analyse the data collected. Findings are expected to strengthening public confidence and accountability mechanisms. Also, to reveal significant improvements of AI- enabled systems on early threat detection, operational coordination, and transparency in security reporting. However, challenges such as inadequate infrastructure, data privacy risk, and limited technical capacity may constrain the effective implementation. The study concludes that integrating AI into security governance can improve democratic accountability, responsiveness, transparency, and citizen-centred service delivery when supported by clear regulations, effective stakeholders’ collaboration, and sustained investment in digital capacity building. It recommends the development of comprehensive policy guidelines, training, and strengthened legal frameworks to ensure responsible and accountable use of AI technologies in Nigeria's security sector.
Adekoyejo Ojo Adeniyi, A. Samson, Waliu Akibu Oluwaleke· Aminu Kano Academic Scholars...· 0 citations
To improve cybersecurity across industries, Cyber Threat Intelligence (CTI) is becoming increasingly crucial. This systematic review explores how CTI practices are evolving in response to advancements in Artificial Intelligence (AI), particularly in the context of Large Language Models (LLMs). We examined 61 peer-reviewed studies using the PRISMA methodology, which demonstrates a strict selection procedure founded on specified inclusion, exclusion, and quality standards. This approach aligns with the scope of similar systematic reviews in the field of cyber threat intelligence. The review provides a comparative synthesis of CTI research capabilities across threat detection and prediction, attribution, forecasting, and automated reporting. We classify these approaches into three categories: conventional methods, those enhanced by AI and Machine Learning, and those based on LLMs. Our findings indicate that LLMs offer significant advantages in contextual reasoning, processing unstructured threat intelligence, and generating actionable mitigation plans. However, challenges such as model explainability, data privacy, system interoperability, and standardization impede their integration into operational environments. In addition to highlighting the potential and practical limitations of LLMs in CTI, this study identifies research gaps and proposes methods to create scalable, secure, and flexible CTI systems that support real-time cyber defense.
Hilalah Alturkistani, Abdul Ghafar Jaafar, S. Chuprat et al.· International journal of res...· 0 citations
Modern financial platforms are increasingly offering banking, payments, wealth management, and other financial services through shared multi-tenant architectures. However, with the sharing of data among tenants, significant security concerns arise that may not be completely addressed by traditional controls. This paper introduces a framework for the enterprise architecture which combines tenant isolation, identity management, API gateway enforcement, secure identifiers, immutable audit logs, and AI-driven risk detection. Unlike previous approaches, which mainly focused on AI model creation, this framework views AI as an assistive overlay that enhances rather than substitutes for deterministic architectural controls. We have enumerated nine cross-tenant exposure vectors, outlined a layered architecture with eight levels of control, and demonstrated, using scenario-based metrics, the added security value in the use of AI-driven anomaly detection and policy violation analysis. The paper also provides implementation guidance, data privacy considerations, and adoption planning for financial platform architectures. The proposed framework brings together enterprise security architecture and AI-driven risk intelligence to enable financial systems to be resilient across multiple tenants.
Kannan Meiappan· AI and Machine Learning Adva...· 0 citations
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