МЕТОДОЛОГІЯ ЗБОРУ ОБРОБКИ ЗБЕРІГАННЯ ТА КЛАСИФІКАЦІЇ ДАНИХ У ВІДПОВІДНОСТІ ДО ВИМОГ SOC2 TYPE2

Purpose: This study aims to explore the critical role of data security in continuous improvement within Industry 4.0 settings. It focuses on identifying how robust data security practices enable organizations to enhance operational efficiency, foster innovation, and protect sensitive information assets. Additionally, the research highlights the interplay between technological advancements, regulatory compliance, and proactive risk management in achieving sustainable organizational growth. Design/methodology/approach: The research adopts a mixed-method approach to investigate the role of data security in continuous improvement within Industry 4.0. A comprehensive literature review was conducted to identify key theoretical frameworks and best practices related to cybersecurity and continuous improvement. The study also incorporates case analysis of Industry 4.0 technologies, such as IoT, AI, and big data analytics, highlighting their integration with data security strategies. By analyzing real-world applications and leveraging predictive analytics and compliance audits, the research demonstrates how secure data practices can enhance organizational performance and foster innovation. Findings: The study identifies that data security is an indispensable component of continuous improvement in Industry 4.0. Secure data practices enhance decision-making, promote operational resilience, and enable proactive risk mitigation. Moreover, they support compliance with regulatory frameworks, such as GDPR and ISO 27001, while fostering a culture of innovation and trust among stakeholders. The findings also reveal significant challenges, including technological complexity, resource constraints, and rapidly evolving cyber threats. Research limitations/implications: The research is limited by the availability of empirical data on specific Industry 4.0 applications. Future studies could expand on the practical implementation of data security measures across diverse industries and explore the economic implications of continuous improvement strategies. Additional research on emerging technologies, such as blockchain and quantum computing, could further enrich the understanding of secure data management. Practical implications: The study provides actionable insights for businesses seeking to integrate data security into their continuous improvement processes. It emphasizes the importance of investing in advanced security technologies, workforce training, and compliance frameworks to enhance organizational resilience. These recommendations are particularly relevant for enterprises navigating the complexities of Industry 4.0. Originality/value: This paper contributes to the literature by linking data security directly with continuous improvement in Industry 4.0. It offers a novel perspective on the strategic importance of secure data practices, supported by both theoretical insights and practical applications. The findings are valuable to researchers, policymakers, and industry leaders focused on sustainable growth and technological innovation.

This study provides a comprehensive examination of data privacy and security within the realm of environmental research, highlighting the evolving challenges and innovative solutions in safeguarding sensitive environmental data. Employing a systematic literature review and content analysis, this research scrutinizes peer-reviewed articles, legal documents, and policy papers published from 2014 to 2024. The study's objectives were to explore fundamental principles, assess key developments and cutting-edge solutions, and understand the impact of international standards and regulatory bodies on data protection in environmental studies. Key findings reveal a growing emphasis on robust data governance frameworks, driven by advancements in digital technologies and the global diffusion of regulations such as the GDPR. Innovations in encryption and anonymization techniques have emerged as critical tools in enhancing data security. However, the study identifies a pressing need for interdisciplinary collaboration and stakeholder engagement to navigate the complex landscape of data privacy and security effectively. The study concludes that the future of environmental research is intricately linked to the strategic integration of data privacy and security measures. It recommends a proactive approach to data protection, emphasizing the importance of compliance with international regulations, the adoption of privacy-by-design principles, and the development of education and training programs. Future research should focus on advancing data security technologies and exploring the socio-ethical implications of data use in environmental studies, ensuring the ethical stewardship of sensitive information. Keywords: Data Privacy, Environmental Research, Data Security Solutions, International Regulations.

Compliance in business processes has become a fundamental requirement given the constant rise in regulatory requirements and competitive pressures that have emerged in recent decades. While in other areas of business process modelling and execution, considerable progress towards automation has been made (e.g., process discovery, executable process models), the interpretation and implementation of compliance requirements is still a highly complex task requiring human effort and time. To increase the level of “mechanization” when implementing regulations in business processes, compliance research seeks to formalize compliance requirements. Formal representations of compliance requirements should, then, be leveraged to design correct process models and, ideally, would also serve for the automated detection of violations. To formally specify compliance requirements, however, multiple process perspectives, such as control flow, data, time and resources, have to be considered. This leads to the challenge of representing such complex constraints which affect different process perspectives. To this end, current approaches in business process compliance make use of a varied set of languages. However, every approach has been devised based on different assumptions and motivating scenarios. In addition, these languages and their presentation usually abstract from real-world requirements which often would imply introducing a substantial amount of domain knowledge and interpretation, thus hampering the evaluation of their expressiveness. This is a serious problem, since comparisons of different formal languages based on real-world compliance requirements are lacking, meaning that users of such languages are not able to make informed decisions about which language to choose. To close this gap and to establish a uniform evaluation basis, we introduce a running example for evaluating the expressiveness and complexity of compliance rule languages. For language selection, we conducted a literature review. Next, we briefly introduce and demonstrate the languages’ grammars and vocabularies based on the representation of a number of legal requirements. In doing so, we pay attention to semantic subtleties which we evaluate by adopting a normative classification framework which differentiates between different deontic assignments. Finally, on top of that, we apply Halstead’s well-known metrics for calculating the relevant characteristics of the different languages in our comparison, such as the volume, difficulty and effort for each language. With this, we are finally able to better understand the lexical complexity of the languages in relation to their expressiveness. In sum, we provide a systematic comparison of different compliance rule languages based on real-world compliance requirements which may inform future users and developers of these languages. Finally, we advocate for a more user-aware development of compliance languages which should consider a trade off between expressiveness, complexity and usability.

Current power sensitive data security protection adopts classification and grading protection. Company classification and grading are mainly in formulating specifications. Data classification and grading processing is carried out manually, which is heavy and time-consuming, while traditional data identification mainly relies on rules for data identification, the level of automation and intelligence is low, and there are many problems in recognition accuracy. Data classification and classification is the basis of data security protection. Sensitive data identification is the key to data classification and classification, and it is also the first step to achieve accurate data security protection. This paper proposes an intelligent identification and tracking technology of sensitive data for electric power big data, which can improve the ability of data classification and classification, help the realization of data classification and classification, and provide support for the accurate implementation of data security capabilities.

The construction and management of exhibition informatization is the main task and choke point during the process of Chinese exhibition industry’s transformation and promotion. There are three key points expected to realize a breakthrough during the construction of Chinese exhibition informatization, and the three aspects respectively are adopting service outsourcing to construct and maintain the database, adopting advanced chest card technology to collect various kinds of information, developing statistics analysis to maintain good cutomer relations. The success of Chinese exhibition informatization mainly calls for mature suppliers who can provide construction and maintenance of database, the proven technology, a sense of data security, advanced chest card technology, the ability of data mining and analysis and the ability to improve the exhibition service basing on the commercial information got from the data analysis. Several data security measures are expected to apply during the process of system developing, including the measures of the terminal data security, the internet data security, the media data security, the storage data security and the application data security. The informatization of this process is based on the chest card designing. At present, there are several types of chest card technology: bar code chest card; two-dimension code card; magnetic stripe chest card; smart-chip chest card. The information got from the exhibition data will help the organizers to make relevant service strategies, quantify the accumulated indexes of the customers, and improve the level of the customer’s satisfaction and loyalty, what’s more, the information can also provide more additional services like the commercial trips, VIP ceremonial reception.

Secure data transmission is essential for securing sensitive data generated in huge volumes in today’s growing digital era. In today’s growing digital world, secure data transfer is essential for securing private information generated in enormous volumes. Data security strategies, called Cyber Security Systems (CSS), are susceptible to errors and other risks that might damage the data’s integrity. While standard Error-Correcting Codes (ECCs) function well for more standard communication errors, they cannot meet the complex rules of cryptographic functions. In order to boost the CSS size, this article provides a detailed approach that develops ECCs from network graph parameters. The error flexibility, computational speed, and use of bandwidth power of the CSS have been designed to be addressed by the graph-based ECCs. The investigation proposes an approach that analyses network graphs and employs information to develop ECCs, which are then integrated into the existing CSS. Compared with standard models, the one recommended performed more successfully when assessed under a range of error scenarios and cryptanalytic attacks.

The energy problem has gained great attention from the public in recent years. To construct a building energy consumption monitoring system is an effective way to find out the situation of the building energy consumption and go forward to make better decisions to solve the energy problem. Many related technologies and methods have been proposed, and this paper will focus on the data organization of the monitoring system. Firstly, a method to carry on data classification according to the features of the data is introduced, and then the tree structure is used for the data organization and data processing. The data is organized in ordinary tree structure in the database and on the webpages. While in data processing, it will be transformed to Binary tree. Meanwhile, the tree structure is also useful to solve the data security problem. After basic testing in the project of the building energy consumption monitoring system construction, this way of data organization has been shown to be effective.

Data classification is a supervised learning strategy that analyzes the organization and categorization of data in distinct classes. Generally, a training set, in which all objects are already associated with known class labels, is used in classification methods. The data classification algorithms work on this set by using input attributes and builds a model to classify new objects. In other words, the algorithm predicts output attribute values. Output attribute of the developed model is categorical (Roiger & Geatz, 2003). There are many applications of data classification in finance, health care, sports, engineering and science. Data classification is an important problem that has applications in a diverse set of areas ranging from finance to bioinformatics (Chen & Han & Yu, 1996; Edelstein, 2003; Jagota, 2000). Majority data classification methods are developed for classifying data into two groups. As multi-group data classification problems are very common but not widely studied, we focus on developing a new multi-group data classification approach based on mixed-integer linear programming.

Chapter 6 - Safe Cosmetics and Regulatory Compliance: From Burden to Opportunity (Cosmetics as Vectors for Bioterrorists?)

Microservice architecture (MSA) is a mainstream architectural style due to its high maintainability and scalability. In practice, an appropriate microservice‐oriented decomposition is the foundation to make a system enjoy the benefits of MSA. In terms of decomposing monolithic systems into microservices, researchers have been exploring many optimization objectives, of which modularity is a predominantly focused quality attribute. Security is also a critical quality attribute, that measures the extent to which a system protects data from malicious access or use by attackers. Considering security in microservices‐oriented decomposition can help avoid the risk of leaking critical data and other unexpected software security issues. However, few researchers consider the security objective during microservice‐oriented decomposition, because the measurement of security and the trade‐off with other objectives are challenging in reality. To bridge this research gap, we propose a security‐optimized approach for microservice‐oriented decomposition (So4MoD). In this approach, we adapt five metrics from previous studies for the measurement of the data security of candidate microservices. A multi‐objective optimization algorithm based on NSGA‐II is designed to search for microservices with optimized security and modularity. To validate the effectiveness of the proposed So4MoD, we perform several experiments on eight open‐source projects and compare the decomposition results to other three state‐of‐the‐art approaches, that is, FoSCI, CO‐GCN, and MSExtractor. The experiment results show that our approach can achieve at least an 11.5% improvement in terms of security metrics. Moreover, the decomposition results of So4MoD outperform other approaches in four modularity metrics, demonstrating that So4MoD can optimize data security while pursuing a well‐modularized MSA.

In the fast-changing digital logistics environment in today's times, data security and privacy are now a necessity to ensure operational integrity. Businesses are more vulnerable to data breaches, illegal usage, and regulatory noncompliance as they rely more on digital platforms for tracking, documentation, and consumer interactions. With a focus on common threats, existing security solutions, and their impact on consumer confidence and corporate productivity, this study explores the significance of data protection in logistics. Major issues include antiquated systems, low staff understanding, and inadequate encryption are identified by the research, which is based on qualitative analysis through case studies, expert interviews, and industry reports. Best practices are to install multi-layered security measures, have regular audits, train employees, and conform to standards such as GDPR and ISO. For maintaining data integrity, businesses need to implement strong cybersecurity frameworks, embed AI-based monitoring tools, promote digital accountability, and collaborate with reliable tech vendors providing end-toend encryption. Finally, infusing good data security practices in digital operations is crucial for compliance, customer protection, and sustainable growth in the logistics industry.

In recent years, with the development of new energy industry and digital transformation, a large amount of business data is stored and transmitted, which contains important business secrets and user privacy information, so data security has become an important task. New energy business data security is a comprehensive task that requires a comprehensive consideration of factors such as technology, management and personnel training. By adopting appropriate security measures, the confidentiality, integrity and availability of new energy business data can be protected. With the rapid development of the Internet of Things, cloud computing and sensor technology, a large number of data characterized by a large number of types and strong timeliness has emerged, and the concept of big data has emerged. Big data is usually unable to be stored and calculated with conventional software within a certain time frame, and the adoption of new technical architectures can make big data play a greater value. In order to realize the secure storage of static big data and ensure the high storage efficiency, a scheme of secure storage of static big data based on lightweight encryption and homomorphic encryption algorithm is proposed. For unstructured data, a parallel encryption scheme based on elliptic curve lightweight encryption algorithm is designed. With the proposed dual-channel storage model, the data to be stored can be encrypted and stored from two storage channels at the same time. The dual-channel storage mode can increase the data storage speed and make up for the time consumption caused by the data encryption process. The reliability of the algorithm is verified by experiments.

Do data security measures, privacy regulations, and communication standards impact the interoperability of patient health information? A cross-country investigation

In the process of implementing data leakage prevention project, power grid enterprises encounter unclear data life cycle security, it is difficult to implement the data classification and classification system, and the formulation of DLP strategy relies too much on individual subjective decisions, aiming at these problems, the author proposes a set of methodology which conforms to the life cycle security of grid data, the design of data classification and classification standards in line with power grid enterprises can objectively guide the data classification and classification work of power grid enterprises and the formulation of DLP strategy, improve the efficiency of data leakage prevention work, and make the data leakage prevention work more standardized and standardized.

Gene mining is a critical data mining process that enables the identification of genetic predispositions and disease risks by analyzing Deoxyribonucleic Acid-DNA data. Within this process, using emerging high tech such as artificial intelligence (AI) and Quantum Computers-QC emerge as innovative technologies that enhance analytical power. AI algorithms and data mining techniques facilitate the discovery of genetic patterns, while QC enable highly accurate analyses even in complex and large datasets. These advancements allow for a faster, more in-depth and reliable examination of DNA data, providing substantial contributions to genetic research. However, the sensitive nature of DNA data necessitates stringent measures for personal privacy and data security. In this context, Blockchain Technologies-BCT offers an effective solution for the secure storage, anonymization and controlled sharing of DNA data exclusively with authorized entities. The distributed and immutable structure of Blockchain Technology-BCT safeguards data while AI and quantum technologies contribute speed and precision to gene mining. This article examines the contributions of AI, data mining and QC to gene mining and underscores the importance of BCT in preserving personal privacy. This study examines the contributions of AI, QC, and BCT in gene mining, emphasizing the importance of BCT in ensuring data privacy and security. In particular, while providing a high level of security for the protection of personal and sensitive data such as DNA, the integrated use of these technologies plays a critical role in establishing new standards for data privacy.