The YUKTAB Publisher publishes the Computertech Journal. The Editors of the Journal are members of the Faculty of Computer Science, Electronics and Telecommunications and the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. The Editorial Board consists of many renowned computer science researchers from all over the world. The first issue of the Journal was published in 2015. Currently, the Journal is published Biannually (2 issues per year), with the main goal of creating a forum for exchanging research experience for scientists specialized in different fields of computer science. Original papers are sought concerning theoretical and applied computer science problems. Example areas of interest are: * Theoretical aspects of computer science * Pattern recognition and processing * Evolutionary algorithms * Neural networks * Database systems * Knowledge engineering * Automatic reasoning * Computer network management * Distributed and grid systems * Multi-agent systems * Multimedia systems and computer graphics * Natural language processing * Soft computing * Embedded systems * Adaptive algorithms * Simulation.
The Socio-Organizational Dimension of Data Quality: People, Processes, and Culture
Data quality is commonly understood as a technical challenge requiring measurement, validation, and correction of data errors. However, this perspective overlooks the fundamental socio-organizational dimensions that shape data quality outcomes. This review examines the role of people, processes, and organizational culture in determining data quality, drawing on literature from information systems, public administration, and organizational studies. We analyze how human factors—including training, skills, motivation, and behavior—influence data quality at the point of collection and throughout the data lifecycle. We examine the importance of process design, with particular attention to how data quality requirements can be embedded into workflows rather than addressed through post-hoc correction. We analyze the role of organizational culture in shaping attitudes toward data quality and supporting or undermining governance efforts. The review synthesizes evidence from diverse sectors, including healthcare, public administration, and small and medium enterprises, identifying common patterns and sector-specific challenges. We propose a socio-organizational framework for data quality management that complements technical approaches by addressing the human and organizational factors that ultimately determine data quality outcomes.
The Future of Data Quality: Emerging Trends, Challenges, and Opportunities
The integration of artificial intelligence into scientific crowdsourcing platforms represents a paradigm shift in how scientific research is conducted, with profound implications for the relationship between human volunteers and intelligent machines. This review examines the future of human-AI interaction in scientific crowdsourcing, analyzing the evolving roles of humans and machines, the design principles that can enhance collaboration, and the broader implications for scientific knowledge production. We draw on research from human-computer interaction, computer-supported cooperative work, and science and technology studies to analyze how AI is reshaping the division of labor in citizen science and related crowdsourcing initiatives. We identify four key dimensions of human-AI interaction: (1) task allocation and division of labor; (2) communication and transparency; (3) learning and adaptation; and (4) governance and accountability. We analyze how these dimensions are evolving with advances in AI and how they can be designed to enhance the complementarity of human and machine intelligence. We also address challenges including the erosion of human autonomy, the potential for AI to reduce human engagement, and the ethical implications of algorithmic governance. We propose a framework for designing human-AI interaction in scientific crowdsourcing that balances efficiency with engagement, automation with human contribution, and algorithmic control with human autonomy.
The Future of Human-AI Interaction in Scientific Crowdsourcing
The FAIR Principles and Data Quality: Advancing Reproducibility and Reusability in Scientific Research
The FAIR principles Findable, Accessible, Interoperable, and Reusable—have emerged as a foundational framework for scientific data management, providing guidance for ensuring that data remain valuable and usable over time. This review examines the relationship between the FAIR principles and data quality, analyzing how FAIR implementation supports and enhances data quality across the research lifecycle. Drawing on literature from data science, information management, and research policy, we examine the conceptual foundations of the FAIR principles and their relationship to established data quality frameworks. We analyze the practical implementation of FAIR across scientific domains, identifying successes, challenges, and lessons learned. The review addresses the extension of FAIR to research software and the emerging concept of AI-readiness as a complement to FAIR. We propose an integrated framework that links FAIR implementation to data quality assurance and scientific reproducibility, providing practical guidance for researchers, institutions, and policymakers seeking to advance open and reproducible science.
Privacy Engineering for Governed AI: Protecting High-Dimensional Data in Public and Critical Systems
Artificial intelligence depends on large and often high-dimensional datasets, creating privacy risks that are not fully addressed by conventional policy statements. Public agencies and infrastructure operators may process health records, mobility traces, sensor streams, energy-use patterns, images, financial information, and behavioural data. Even when direct identifiers are removed, combinations of attributes can permit re-identification or reveal sensitive facts. This review examines privacy as an engineering, governance, and accountability problem across the AI lifecycle. It is based on the thematic evidence of a systematic synthesis that retained 95 high-quality AI governance studies and frameworks from 2020 to mid-2025. The literature shows that privacy is one of the most frequently recognised governance dimensions, yet implementation quality varies. Many frameworks state requirements for lawful processing and protection but do not define measurable controls for data minimisation, linkage risk, inference attacks, model leakage, access management, and retention. The review compares major privacy controls, including anonymisation, pseudonymisation, differential privacy, secure access, federated approaches, encryption, data lineage, and impact assessment. It also analyses trade-offs between privacy, fairness, transparency, and model utility. A principles-controls-evidence architecture is proposed to make privacy commitments auditable. The article concludes that privacy-preserving AI requires continuous risk assessment, technically appropriate safeguards, and clear institutional responsibility rather than one-time consent or de-identification.
Machine Learning in Astronomy and Neuroscience: Lessons from Citizen Science
While biodiversity monitoring has dominated the intersection of citizen science and machine learning, significant and instructive applications have emerged in astronomy and neuroscience. These domains offer unique insights into the integration of human and machine intelligence for scientific discovery. In astronomy, citizen science projects such as Galaxy Zoo and the Milky Way Project have engaged hundreds of thousands of volunteers in classifying celestial objects, with machine learning algorithms subsequently trained on volunteer classifications to automate analysis of vast image datasets. In neuroscience, projects such as Braindr have demonstrated how citizen scientists can amplify expert-labeled medical images, with deep learning algorithms learning to replicate expert quality assessments. This review examines the applications of machine learning in astronomy and neuroscience citizen science, analyzing the technical approaches, scientific outcomes, and lessons that can be generalized to other domains. We identify key success factors including the effective division of labor between humans and machines, the importance of expert validation of citizen science data, and the potential for machine learning to enable discoveries that would be impossible through human effort alone. We also address challenges including the difficulty of training models for rare or ambiguous objects, the need for careful quality assurance, and the importance of maintaining volunteer engagement when tasks are automated. We propose a framework for integrating machine learning into citizen science across diverse scientific domains, drawing on lessons from astronomy and neuroscience.
Intelligent Risk, Fraud, and Compliance Analytics in Modern Financial Services
Financial organisations increasingly use artificial intelligence to detect fraud, assess creditworthiness, forecast investment conditions, monitor portfolios, and automate regulatory reporting. This critical narrative review examines the decision applications through which artificial intelligence creates operational value in banking, insurance, payments, capital markets, and financial technology. It compares machine learning, natural language processing, deep learning, expert systems, fuzzy logic, and hybrid approaches, while emphasising that application performance depends on data quality, governance, explainability, and continuous oversight. Evidence reported in the source systematic review shows particularly strong associations for machine learning, regulatory compliance, fraud detection, risk analytics, and credit scoring. The article discusses how artificial intelligence changes each stage of the financial decision process, from data ingestion and signal detection to recommendation, review, and reporting. It also evaluates common weaknesses, including false positives, concept drift, historical bias, fragmented datasets, opaque models, and over-automation. The review concludes that artificial intelligence can improve speed, coverage, and predictive precision, but institutions must match the model to the decision, maintain human accountability, and monitor performance across changing market and regulatory conditions.
Explainable and Ethical Artificial Intelligence for Financial Data Stewardship
The expansion of artificial intelligence in finance has created a tension between predictive efficiency and accountable decision-making. Financial institutions can now automate classification, risk assessment, customer screening, fraud detection, and compliance monitoring, yet these systems may also obscure reasoning, reproduce historical bias, threaten privacy, and weaken meaningful human control. This review examines explainability, fairness, privacy, security, auditability, and accountability as essential components of financial data stewardship. Drawing on the evidence base reported in the uploaded systematic review, it argues that ethical artificial intelligence cannot be added after model development as a separate checklist. It must be embedded in data governance, model design, validation, deployment, and institutional oversight. The article distinguishes technical explanation from meaningful explanation, discusses the limits of human-in-the-loop arrangements, and evaluates governance measures such as model inventories, documentation, access control, impact assessment, independent validation, audit trails, challenge rights, and continuous monitoring. It also outlines policy actions at international, sectoral, and organisational levels. The review concludes that responsible financial artificial intelligence requires institutions to treat legitimacy, transparency, and customer protection as performance requirements alongside accuracy and efficiency.
Cybersecure by Design: Managing Adversarial Risk and Resilience in AI-Enabled Critical Infrastructure
The use of artificial intelligence in critical infrastructure creates a new class of cyber-physical and institutional risks. AI systems depend on extensive data pipelines, complex models, software supply chains, cloud services, interfaces, and continuous updates. These features expand the attack surface beyond traditional information technology and expose models to data poisoning, evasion, model extraction, inference attacks, manipulation of sensors, and unsafe automated responses. This review examines cybersecurity as a central component of AI governance rather than a separate technical speciality. It draws on the thematic synthesis of 95 high-quality studies and frameworks covering public administration, digital services, smart cities, and infrastructure-related applications. The evidence indicates that many governance frameworks acknowledge privacy and ethics but provide limited operational guidance for threat modelling, adversarial testing, incident response, resilience, and audit evidence. The review presents a lifecycle approach to AI security that spans design, data preparation, model development, validation, deployment, monitoring, change management, and retirement. It also explains how risk tiering can connect system impact to proportionate controls and how assurance records can support accountability. Particular attention is given to smart grids, transport, healthcare, buildings, and structural monitoring, where security failures can move quickly from digital disruption to physical or social harm. The article concludes that cybersecure AI requires integrated governance, continuous testing, and recovery planning from the beginning of system design.
Closing the Principles-to-Practice Gap: Operational AI Governance for Sustainable Digital Infrastructure
Artificial intelligence is becoming part of public administration, critical services, transport, energy systems, and other forms of digital infrastructure. Yet many governance programmes still rely on broad commitments to fairness, transparency, privacy, and accountability without specifying how those commitments should be implemented, tested, and evidenced. This review examines the transition from principle-based governance to operational assurance. It draws on the thematic findings of a large systematic synthesis that retained 95 high-quality studies and frameworks published during the rapid expansion of AI governance research from 2020 to mid-2025. The literature shows a clear imbalance: privacy and ethics receive substantial attention, while operational security, bias control, accountability mechanisms, resilience, automation, and implementation costs are handled inconsistently. The review develops a practical interpretation of a three-layer governance architecture built around principles, controls, and evidence. It explains how organisations can translate values into technical and managerial safeguards, then demonstrate performance through logs, impact assessments, testing reports, monitoring records, and audit trails. The analysis also considers the relevance of this architecture to Sustainable Development Goal 9, particularly the need for reliable, inclusive, and resilient digital infrastructure. The central argument is that trustworthy AI cannot be achieved by publishing ethical principles alone. It depends on a repeatable operating model that assigns responsibility, matches controls to risk, collects verifiable evidence, and improves continuously across the AI lifecycle.
AI-Enabled Data Governance as the Foundation of Reliable Financial Decision Systems
Artificial intelligence is reshaping financial analysis, yet the value of advanced algorithms depends heavily on the quality of the data and governance systems surrounding them. This review examines how artificial intelligence strengthens data governance and how governance maturity converts technical capability into dependable financial decisions. The discussion integrates evidence on data quality, metadata, lineage, privacy, security, regulatory compliance, and organisational accountability across banking, insurance, payments, capital markets, and financial technology. It argues that artificial intelligence should not be treated as an independent decision engine. Instead, it functions most effectively as part of a socio-technical governance architecture that connects automated classification, anomaly detection, risk analytics, and compliance monitoring with human responsibility and institutional controls. Evidence from the source review indicates strong relationships between artificial intelligence integration, governance capability, and financial outcomes, with governance acting as a central pathway through which algorithmic capacity becomes measurable decision value. The review also considers differences between early-stage and mature institutions, practical implementation priorities, policy needs, and unresolved research gaps. It concludes that trustworthy financial intelligence requires simultaneous investment in models, data stewardship, transparent controls, and continuous oversight.
AI for Data Quality: Leveraging Machine Learning for Quality Assurance
The relationship between artificial intelligence and data quality is fundamentally bidirectional: AI systems depend on high-quality data for reliable performance, while AI techniques offer powerful tools for assessing, monitoring, and improving data quality at scale. This review examines the application of machine learning and AI to data quality management, analyzing how intelligent systems can automate quality assessment, anomaly detection, data cleaning, and continuous monitoring. Drawing on literature from data management, computer science, and information systems, we review the current state of AI-driven data quality tools and their capabilities across multiple quality dimensions. We analyze the technical approaches that have proven most effective, including supervised and unsupervised learning for anomaly detection, deep learning for data imputation, and natural language processing for semantic quality assessment. We examine the gap between technological capability and organizational adoption, identifying barriers including user acceptance concerns, skill gaps, and management hesitancy. We propose a framework for integrating AI into data quality management that addresses technical, organizational, and human dimensions.
Integration Patterns Driven by Events for Financially Sensitive Enterprise Platforms
Contemporary enterprise platforms functioning in financially sensitive contexts must ensure elevated reliability, auditability, and real-time responsiveness. Conventional synchronous integration methods frequently result in tight coupling, delay, and operational vulnerability in the context of high-volume financial transactions. Event-driven architecture (EDA) has evolved as a scalable model that facilitates loosely connected communication, asynchronous processing, and enhanced resilience in dispersed corporate systems. This paper analyses event-driven integration patterns tailored for financially sensitive enterprise platforms, including banking systems, payment gateways, government financial platforms, and enterprise resource planning (ERP) settings. It examines architectural elements including event producers, brokers, consumers, and event stores, and investigates patterns such as event notification, event-carried state transfer, event sourcing, and transactional outbox mechanisms. The study additionally examines dependability measures such as idempotent processing, assured delivery, replay capabilities, and audit-ready event logging necessary in regulated financial contexts. Furthermore, governance factors like compliance monitoring, data lineage tracking, and security procedures are assessed. The paper illustrates, via architectural diagrams, comparative tables, and analytical insights, how event-driven integration enhances scalability, operational resilience, and financial data integrity across enterprise platforms. The findings offer a systematic framework for organisations to implement when updating integration layers in transaction-critical systems, ensuring regulatory and operational reliability.
Agentic AI for Regulatory Intelligence: Scalable Compliance Systems in Multinational Tech Enterprises
The growing complexity of global regulations in sectors such as e-commerce and healthcare has made traditional manual compliance systems inefficient and difficult to scale. This paper proposes an agentic AI–driven regulatory intelligence framework that automates the compliance lifecycle across jurisdictions. The framework integrates horizon scanning, natural language processing, autonomous policy interpretation, and AI-based risk assessment to support real-time detection of regulatory changes, automated control mapping, and proactive remediation. Distributed AI agents coordinate compliance activities across departments while maintaining auditability, human oversight, and ethical governance. By transforming compliance from a static, document-heavy process into an adaptive intelligence system, agentic AI can reduce regulatory risk, strengthen operational resilience, and improve strategic decision-making at global scale.
Methods for Static Analysis of Cyber-Physical, Electronic, and Embedded Software Systems: An In-Depth Review
As electronic, cyber-physical, and embedded software systems become increasingly complex and deeply integrated with hardware, communication networks, and real-time operational constraints, ensuring software correctness and dependability has emerged as a critical engineering challenge. Static analysis has evolved into one of the most effective approaches for detecting defects, vulnerabilities, and reliability concerns before software deployment. This article presents a comprehensive review of modern static analysis methodologies and their applications in embedded, electronic, and cyber-physical systems. Core techniques including data-flow analysis, symbolic execution, abstract interpretation, model checking, and constraint-based reasoning are examined in detail. The article further explores how these methods are implemented in contemporary industrial and academic tools such as SonarQube, CodeQL, Clang Static Analyzer, Frama-C, Infer, and Coverity. Particular emphasis is placed on safety-critical domains including aerospace, automotive electronics, blockchain smart contracts, and AI/ML-enabled electronic systems. Current challenges such as scalability limitations, false positives, integration complexity, and soundness–precision trade-offs are critically discussed. Finally, emerging trends including machine-learning-enhanced analysis, hybrid static–dynamic verification, cloud-native system analysis, and automated program repair are evaluated to highlight future research directions. This review aims to provide researchers and practitioners with a structured and application-oriented understanding of static analysis for modern electronic and embedded software infrastructures.
Advanced Static Analysis Integration and Future Directions in Modern Software Engineering
Static analysis has evolved into a critical component of modern software engineering, enabling organizations to improve software reliability, security, safety, and maintainability across increasingly complex systems. This article explores the integration of static analysis into modern software development workflows, highlighting the technical and organizational challenges associated with tool adoption, configuration, scalability, precision, and usability. The study examines the core trade-offs that shape static analysis research, including the balance between scalability and precision, soundness and false positives, and usability and adoption. It further investigates emerging trends such as machine-learning-enhanced static analysis, hybrid static–dynamic approaches, automated program repair, language-specific analysis for modern programming platforms, and cloud-native system verification. The article also discusses the growing importance of static analysis in safety-critical domains, cybersecurity, blockchain systems, AI/ML pipelines, and distributed architectures. Finally, the study concludes that static analysis remains fundamental to ensuring software correctness, security, and resilience in next-generation digital infrastructures and emphasizes the need for adaptive, intelligent, and scalable verification methodologies capable of addressing future software engineering challenges.
A Comprehensive Taxonomy of Static Analysis Techniques for Modern Software Verification
Static analysis has become a foundational discipline in modern software verification, enabling the detection of software defects, security vulnerabilities, and reliability issues without executing programs. Over time, static analysis has evolved from simple pattern-matching approaches into sophisticated mathematical frameworks capable of supporting safety-critical and large-scale industrial systems. This article presents a comprehensive taxonomy of major static analysis methodologies, including data-flow analysis, symbolic execution, abstract interpretation, model checking, and constraint solving. The study examines the theoretical foundations, technical characteristics, practical applications, and representative tools associated with each analytical paradigm. It further explores modern industrial tools such as SonarQube, CodeQL, Clang Static Analyzer, Frama-C, Infer, and Coverity, highlighting their roles in software quality assurance, cybersecurity, embedded systems, and cloud-native infrastructures. The article also analyzes the trade-offs between scalability, precision, soundness, and computational complexity that shape tool selection and practical adoption. Finally, the study concludes that hybrid verification strategies integrating multiple static analysis approaches represent the future direction of software verification in increasingly complex and safety-critical software ecosystems.
Advanced AI Techniques for Protecting GraphQL APIs Against Injection and DoS Attacks
GraphQL's adaptability is great for speedy data retrieval, but it also brings new security risks that standard API safeguards don't always cover. Attacks like denial-of-service and data exfiltration through injection can be caused by malicious GraphQL queries that take advantage of the language's dynamic nature. When it comes to protecting against complex, context-aware assaults, current solutions like rate restriction, static analysis, and general-purpose Web Application Firewalls fall short. This study introduces a new method for detecting malicious GraphQL queries in real-time using artificial intelligence. Our approach integrates static analysis with machine learning techniques. These techniques include CNNs, Multilayer Perceptrons, and Random Forests for classification, Sentence Transformers (SBERT and Doc2Vec) for contextual embedding of query payloads, and Large Language Models (LLMs) for dynamic schema-based configuration. Our system architecture is described in depth, along with optimization solutions for production scenarios, such as ONNX Runtime and par-allel processing. We also assess how well our detection models and the system perform under stress. The results show that a number of threats, such as SQL injection, OS command injection, and XSS attacks, can be accurately detected with a high degree of accuracy, and that threats like DoS and SSRF attempts may be effectively mitigated. An effective and flexible method for strengthening the security of GraphQL APIs is developed in this study.
Machine Learning-Based Self-Healing Systems in Software Engineering: Challenges, Techniques, and Future Directions
Self-healing systems have emerged as an important advancement in software engineering by enabling software applications to automatically detect, diagnose, and recover from failures without human intervention. These systems improve reliability, minimize downtime, and enhance system resilience, particularly in complex software environments such as cloud computing, distributed architectures, and Internet of Things (IoT) platforms. The integration of machine learning techniques into self-healing systems has significantly improved their ability to predict faults, detect anomalies, and optimize recovery strategies. This study explores the application of machine learning methods, including anomaly detection, predictive maintenance, reinforcement learning, and federated learning, in the development of self-healing software systems. A comparative analysis of different algorithms is presented to evaluate their effectiveness in improving software performance and operational stability. Experimental findings indicate that machine learning-based self-healing systems substantially reduce Mean Time to Repair (MTTR), improve resource utilization, and increase fault detection accuracy. However, several challenges remain, including computational complexity, data quality, model interpretability, and deployment scalability. The study concludes that machine learning has the potential to revolutionize software reliability by enabling adaptive and autonomous recovery systems capable of operating in dynamic environments.
Integrating Site Reliability Engineering and DevOps for Scalable and Reliable Global Cloud Operations
In geographically dispersed settings, the rapid proliferation of cloud computing has revolutionized how contemporary enterprises deliver and oversee digital services. Traditional IT methodologies are challenged as enterprises increasingly require consistent, uniform, and flexible operations concurrently. This essay examines the increasing necessity of integrating two essential yet often distinct methodologies, Site Reliability Engineering (SRE) and DevOps, to optimize cloud operations at scale. Although both methodologies seek to enhance service reliability and accelerate development, their divergent strategies may result in inconsistent workflows, tool fragmentation, and associated cultural issues. The deficiencies are particularly evident in global cloud installations, where resilience, observability, and coordination are crucial. This article presents a cohesive operational strategy that integrates SRE's emphasis on dependability and automation with DevOps' agility and continuous delivery approach. The paper presents a pragmatic paradigm that reconciles technical and procedural distinctions, thereby fostering shared ownership, transparency, and a feedback-driven culture. This case study demonstrates that the cloud migration of a global corporation enhanced system stability, deployment speed, and cross-functional collaboration through a unified paradigm. The case study emphasizes discernible improvements such as reduced incident response times, enhanced change success rates, and a more robust culture of accountability and learning. This article seeks to furnish enterprises with valuable insights and assistance in developing robust, scalable cloud systems by leveraging the synergistic capabilities of SRE and DevOps.
A Prospective Examination of Security Vulnerabilities in Link-Traversal-Based Query Processing
The increasing social and economic influence of Big Data platforms has intensified the demand for decentralized alternatives. However, retrieving and querying information in decentralized environments requires fundamentally different techniques whose characteristics and limitations are not yet fully understood. Link-Traversal-based Query Processing (LTQP) is an emerging query paradigm that enables querying across decentralized data networks by allowing client-side engines to discover information through the traversal of links between distributed documents. Since decentralized systems operate without centralized control, they are inherently exposed to various security risks. Consequently, LTQP engines must be resilient against threats targeting the host system, the query execution process, and the personal data of users. This study presents a comprehensive analysis of potential security vulnerabilities associated with LTQP. Drawing inspiration from security concerns identified in related domains, ten major security threats relevant to LTQP are identified and examined. Each vulnerability is discussed alongside practical examples and possible mitigation approaches. Furthermore, this work provides recommendations for LTQP engine developers and data publishers aimed at reducing exposure to these risks. Through this contribution, the study addresses several uncertainties surrounding secure querying in decentralized ecosystems. Beyond security, additional research remains necessary to identify the foundational components required for truly decentralized data processing environments.
Enhancing CI/CD Pipelines Via Parallel Build Graphs and Dependency Optimization
Contemporary software development necessitates swift, dependable, and scalable deployment methodologies, with Continuous Integration and Continuous Deployment (CI/CD) pipelines constituting the cornerstone of this delivery framework. As codebases and organisational frameworks become increasingly intricate, conventional sequential pipelines fail to satisfy the latency and efficiency demands of agile teams. This study examines the acceleration of CI/CD pipelines via the implementation of parallel build graphs and dependency optimisation methods. It explores the conceptual transition from linear execution models to Directed Acyclic Graph (DAG)-based pipelines, wherein parallel job execution optimises resource utilisation. Additionally, the study analyses how sophisticated dependency methods, such as build caching, change-based execution, and version pinning, save duplicate computations and enhance determinism. The discourse is based on architectural patterns, actual tool implementations, performance benchmarking, and observability approaches that collectively provide resilient, scalable, and responsive CI/CD systems. The results underscore the significance of integrating structural parallelism with astute dependency management to attain high-performance continuous delivery operations.
Automated Data Retention Policy Enforcement in SQL Server Environments: An AI-Governed Lifecycle Management Framework
There are a vast number of interconnected transaction level facts that are contained within relational databases. These facts can collectively (or separately) represent an infinite number of business records. We provide a framework in this article that allows for the definition of business records that are stored in a database system as queries, followed by the specification of retention policies which are applied to those searches. When it comes to creating and executing records retention rules for relationally stored documents, we also emphasise many critical challenges that policy makers and database administrators need to take into consideration.
An Analysis of AI's Function in Medical Aspects
Applications of artificial intelligence (AI) have revolutionised healthcare. This study conducts a comprehensive literature review to elucidate the role of AI in healthcare, concentrating on the following key areas: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) additional administrative applications. The influence of AI is evident in the identification of clinical conditions through medical imaging and diagnostic services, the management of the coronavirus disease 2019 (COVID-19) outbreak via early diagnosis, the provision of virtual patient care utilising AI-driven tools, the administration of electronic health records, the enhancement of patient engagement and adherence to treatment plans, the alleviation of administrative burdens on healthcare professionals (HCPs), the discovery of novel pharmaceuticals and vaccines, the detection of prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. This scientific proposal encounters several technological, ethical, and social problems, such as privacy, safety, autonomy, costs, information and permission, accessibility, and effectiveness, in the integration of AI into healthcare. The regulation of AI applications is essential for patient safety and accountability, as well as for increasing healthcare professionals' confidence in fostering acceptance and improving substantial health outcomes. Effective governance is essential to accurately tackle regulatory, ethical, and trust challenges while promoting the adoption and application of AI. The COVID-19 pandemic has catalysed a revolution in healthcare via the notion of AI, potentially advancing the fulfilment of future healthcare requirements.
Privacy Risk Scoring for User-Facing Enterprise Web Applications: A Machine Learning Model for Sensitive Data Exposure Detection
The current article examines the regulating factors of analytics, particularly about citizen behaviors and transactions, which are contingent upon the operational realm of an organization (corporate, public sector/government, or academic). We contend that ambitions and purposes vary per domain, despite digital spaces increasingly converging these domains. We assert that citizens' expectations and implicit consent for data exploitation need the perception of a fair balance of advantages, which must be visible and justified to the public. We emphasize that in the corporate domain, the majority of analytics does not pertain to identification, targeted marketing, or direct engagement with individual people; rather, it facilitates strategic decision-making, with the data being effectively anonymized. We examine the nature of models used in analytics via three domains, including 'black-box' modeling that is beyond human verification, and the need of monitoring the provenance and functionality of these models. We also analyze the recent development of personal data, whereby some behaviors or tokens that identify people (unique but non-random) are partly and collectively held by other interconnected persons. We evaluate the capacity of strongly and weakly regulated industries to enhance access or inhibit innovation. We advocate for explicit and comprehensive definitions of 'data science and analytics', steering clear of the restrictive assertions made by individuals in certain technical sub-sectors or sub-themes. Ultimately, we analyze instances of unethical and abusive conduct. We advocate for the imposition of ethical duty on professional data scientists to prevent future misuse.
Applications and Ethical Challenges of ChatGPT in Healthcare and Medical Practice
Artificial intelligence has rapidly transformed many sectors, including healthcare and medicine. Among recent developments, large language models such as ChatGPT have gained significant attention for their ability to generate human-like responses and assist in a variety of medical and healthcare tasks. ChatGPT has the potential to support healthcare professionals and patients by providing information, assisting in medical education, contributing to disease diagnosis, supporting pharmaceutical research, and improving the interpretation of medical images such as radiology and cellular imaging. In addition, the technology can assist patients by providing preoperative information, facilitating communication, and helping individuals better understand complex medical terminology. Despite these advantages, the integration of ChatGPT into healthcare systems also raises several concerns, including data privacy, ethical considerations, reliability of AI-generated information, and the need for human oversight in clinical decision-making. Issues such as bias in training data, lack of emotional understanding, and the potential misuse of AI-generated content must also be carefully addressed. This article reviews the potential applications of ChatGPT in healthcare and medicine while highlighting its benefits, limitations, and ethical challenges. The study emphasizes the importance of responsible adoption, appropriate regulatory frameworks, and collaboration between healthcare professionals and artificial intelligence experts to ensure that AI technologies are used safely and effectively in medical practice.
Artificial Intelligence in Data Governance: Enhancing Efficiency, Compliance, and Decision-Making for Data Governance Analysts
The exponential growth of data generated by digital platforms, enterprise systems, and emerging technologies has significantly increased the complexity of data governance within modern organizations. Data Governance Analysts are tasked with ensuring that data assets are accurate, secure, compliant with regulations, and ethically managed throughout their lifecycle. Traditional data governance approaches, which often rely on manual rules, static policies, and periodic audits, are increasingly inadequate in handling large-scale, dynamic, and heterogeneous data environments. In this context, Artificial Intelligence (AI) has emerged as a transformative solution that enhances the effectiveness and efficiency of data governance practices. AI technologies such as machine learning, natural language processing, and intelligent automation enable Data Governance Analysts to monitor, analyze, and manage data assets in real time. These technologies support automated data classification, metadata management, anomaly detection, and predictive risk assessment, thereby reducing human error and operational overhead. AI-driven governance tools can identify data quality issues, detect policy violations, and flag compliance risks at an early stage, allowing organizations to respond proactively rather than reactively. Moreover, AI facilitates the governance of unstructured and semi-structured data, which has traditionally been difficult to manage using conventional methods.Despite its advantages, the integration of AI into data governance frameworks also presents significant challenges. Issues related to algorithmic bias, lack of transparency, privacy concerns, and evolving regulatory requirements must be carefully addressed to ensure responsible AI deployment. Data Governance Analysts play a critical role in bridging the gap between technical AI systems and organizational governance objectives by establishing ethical guidelines, validation mechanisms, and oversight structures. This article examines the role of AI in supporting Data Governance Analysts, highlighting its applications, benefits, and limitations. It also discusses future trends such as explainable AI and automated policy enforcement, emphasizing the need for balanced governance strategies that combine technological innovation with ethical and regulatory accountability.
Enabling Secure AI Adoption Through Strategic Data Governance in SMEs and Large Organizations
In the current digital landscape, characterized by the rapid expansion of Artificial Intelligence and data-intensive technologies, data governance has evolved into a strategic foundation for sustainable organizational growth rather than a purely regulatory function. As enterprises increasingly rely on AI-driven systems to improve efficiency, innovation, and decision-making, the reliability, security, and ethical handling of data have become critical determinants of success. This study presents a synthesized overview of existing research, industry best practices, and widely adopted governance frameworks to explore how effective data governance supports secure and responsible AI adoption across different organizational contexts. The analysis highlights that although core data governance principles—such as accountability, transparency, and data quality—are universally relevant, their implementation varies significantly between Small and Medium-sized Enterprises and large organizations. Large enterprises often face challenges related to fragmented data landscapes, complex organizational hierarchies, and slow decision-making processes. In contrast, SMEs frequently struggle with limited financial, technological, and human resources, which restrict their ability to deploy comprehensive governance frameworks. The introduction of AI further intensifies these challenges by raising concerns related to data privacy, ethical use, bias, and regulatory compliance, particularly in sensitive domains such as healthcare and wearable technologies. The findings emphasize that a one-size-fits-all approach to data governance is ineffective in the AI era. Instead, organizations must adopt flexible, metrics-driven governance models that combine regulatory rigor with operational agility. Such hybrid approaches enable secure AI deployment while maintaining trust, accountability, and resilience. The study also underscores the growing importance of emerging decentralized technologies, including blockchain, in strengthening transparency and trust across complex, multi-stakeholder data ecosystems.
AI-Driven Data Governance: Advancing Security and Regulatory Compliance in Enterprise Systems
Artificial intelligence is transforming data governance by enabling organizations to better protect data, address regulatory requirements, and manage enterprise information within unified environments. In recent years, data volumes have increased dramatically, while regulatory demands have become more stringent, complicating challenges related to data validation, security, and compliance with frameworks such as GDPR, HIPAA, and CCPA. This paper explores how organizations can integrate AI into data governance strategies as an automation mechanism for anomaly detection and enhanced security. AI-driven data management leverages machine learning, natural language processing, and predictive analytics to address critical organizational challenges. These technologies allow enterprises to automatically discover, classify, and index data, assess risks, and monitor usage patterns in real time. Furthermore, AI significantly strengthens data protection by employing anomaly detection models that can rapidly identify threats such as unauthorized access and malicious activities. The study also examines how AI supports regulatory compliance by improving audit processes, ensuring data accuracy, and enforcing compliance standards. AI-enabled policy automation restricts excessive data usage and prevents prohibited data sharing, thereby reducing the likelihood of violations, penalties, and breaches. Additionally, AI systems can anticipate future compliance risks by analyzing historical and current compliance trends. Through case studies and practical examples, the paper highlights key benefits of AI-based data governance, including improved data accuracy, reduced human intervention, and enhanced operational efficiency. At the same time, it addresses challenges such as ethical concerns, algorithmic bias, and the need for continuous system updates to remain effective in a rapidly evolving regulatory landscape. Overall, the study emphasizes the transformative role of AI in enterprise data governance, demonstrating its potential to enhance security, strengthen compliance, and unlock greater value from organizational data. Consequently, it recommends that enterprises adopt AI-driven governance frameworks while remaining mindful of the associated challenges in today’s dynamic data environment.
Two Decades of Progress in Master Data Management and Data Governance: Developments and Innovations
Over the last twenty years, Master Data Management (MDM) and Data Governance (DG) have evolved dramatically, progressing from foundational roles in data quality and regulatory compliance to strategic enablers of enterprise agility and digital innovation. This review consolidates the development of MDM and DG from 2000 to 2024, analyzing 112 peer-reviewed articles, industry reports, and real-world implementation studies. Employing a mixed-method approach—combining thematic analysis, bibliometric mapping, and keyword frequency evaluation—the study identifies critical milestones, technological advancements, and shifting organizational practices. Results indicate a consistent movement from conceptual frameworks toward AI-driven, cloud-native governance models, with increased focus on decentralization, automation, and deriving business value from data. The study also highlights the alignment of MDM and DG with organizational objectives, facilitated by innovations such as data mesh, data fabric, and real-time metadata management. By providing a consolidated perspective on the historical evolution and emerging trends of data governance ecosystems, this review offers actionable insights for researchers, practitioners, and policymakers navigating the rapidly changing data-driven enterprise environment.