Security Industry Research Articles

Assessment of the level of economic security of the dairy industry of Ukraine

Object: The aim of the study is to assess the level of economic security of the Ukrainian dairy industry in order to identify threats in a timely manner. The object of the research is the economic security of the dairy industry in Ukraine. The subject of the research is the process of assessing the level of economic security of the dairy industry in Ukraine.Methods: statistical and comparative analysis, taxonomic method and golden section method.Findings: The article builds a fractal model of economic security levels. The resource, commercial and social components are allocated to assess the economic security of the supersystem of the enterprise, namely, the dairy industry of Ukraine. To assess the economic security of the supersystem of the enterprise, namely, the dairy industry of Ukraine, it is proposed to single out the resource, commercial and social components. The stages of the methodology for assessing the level of economic security of the dairy industry in Ukraine, as well as its ranges, are determined. Trends and external threats can be identified on the basis of an assessment, which will allow the enterprise to adapt and develop appropriate measures.Conclusions: The scientific and methodological approach to assessing the level of economic security of the dairy industry in Ukraine has been improved. It is calculated using the method of taxonomic analysis by a posteriori parameters within the individual components (resource, commercial, social). It was stated that there is practically no inflow of investments into the country, and further reduction threatens domestic demand. All this actualizes the solution of the problem of ensuring food security not only at the industry level, but also at the country level. It also requires the development of mechanisms to support agriculture and domestic producers. It has been determined that for the economic security of an enterprise, the supersystem is the economic security ofthe industry and the country as systems of a higher rank. They go their own way of development and are its external determinants. After all, in a country and industry with a low level of economic security, even a successful (at the beginning) enterprise will face bankruptcy if nothing changes in the surrounding meso- and macro-environment.

The Mediating Role of Work Engagement on The Relationship Between Job Security, Organizational Justice, Job Embeddedness and Turnover Intention of Pakistan's Textile Industry

The Mediating Role of Work Engagement on The Relationship Between Job Security, Organizational Justice, Job Embeddedness and Turnover Intention of Pakistan's Textile Industry

Implementation of Blockchain Technology Across Different Domains of Dentistry: A Systematic Review.

Blockchain technology has gained attention as a potential solution for improving data security, privacy, and interoperability in various industries, including healthcare. In the field of dentistry, the implementation of blockchain holds promise for transforming dental practice and management. However, a comprehensive evaluation of the existing literature regarding the implementation of blockchain technology in dental practice is lacking. This systematic review aimed to assess the current evidence on the implementation of blockchain technology in dental practice and management. A systematic literature search was conducted using major databases to identify relevant studies. The search strategy included keywords related to blockchain technology and dentistry. The investigation was performed as per the PRISMA guidelines. Studies reporting on the implementation, adoption, and outcomes of blockchain technology in dental practice and management were included. Quality assessment and data extraction were performed following predefined criteria. The initial search yielded a multitude of articles, and after applying the inclusion and exclusion criteria, six studies were included in the systematic review. The studies explored various aspects of blockchain technology implementation in dental practice, including data security, interoperability, supply chain management, and patient consent management. Furthermore, the use of blockchain-based systems showed potential benefits in enhancing supply chain management efficiency and patient consent authentication. This systematic review provided insights into the current state of blockchain technology implementation in dental practice and management. The findings suggested that blockchain technology has the potential to enhance data security, privacy, and interoperability in dental practices. However, further research and real-world implementation studies are needed to fully understand the impact of blockchain technology on dental practice and to address the existing challenges.

Employee Job Satisfaction and its Impact on Performance of Private Security Industry

This study focuses on employee job satisfaction and its impact on performance of private security industries. The study used a descriptive research design and sampled 847 security personnel’s from 35 registered private security agencies in Maharashtra. The study concludes that most of the security personnel’s not satisfied towards their job and facilities they are getting and it creates negative impact on performance of private security industry. Therefore, the study recommends the security agencies should improve performance appraisal system, motivational practices, training programmes, social responsibilities towards security personnel’s etc. There should be a use of modernized technology as per changing needs for betterment of security industries. Researcher attempted to identify the various factors of job satisfaction and its impact on the job performance.

Comparative Empirical Evidence of a Three-factor Model and a Five-factor Model Based on Anomalous Studies

The Fama-French three-factor model explains the magnitude of stock market returns by constructing three variable factors. The five-factor model adds two more factors to the three-factor model to provide a more accurate explanation of stock re-turns. These two factors have been widely used in the industry for decades since their inception. As China's securities industry has developed late, research on the stock industry has been more oriented towards empirical studies of the three-factor and five-factor models, and less research has been conducted on many financial anomalies that cannot be explained by traditional financial theory. This paper systematically describes the origins of factor anomaly research and five-factor anomaly research on the A-share market from 1997 to 2020, constructs new factors, compares and summarizes them with the old ones, and concludes that the three-factor anomaly model and five-factor anomaly model are not yet able to adequately explain A-share stocks and have larger errors when conducting empirical evidence; in contrast, the CPAM anomaly model can better conduct anomaly research, and the resulting In contrast, the CPAM is more suitable for the empirical study of anomalies and can pro-vide investors with more effective investment strategies and recommendations.

Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies

Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrated exceptional performance in generating high-quality synthetic images that closely resemble the original imprinted characters required for augmenting the limited datasets. And the evaluation metric, Fréchet inception distance, further validated the outstanding performance of the WGAN-GP and WGANDIV models, establishing them as optimal choices for dataset augmentation to enhance the accuracy and reliability of recognition systems.

Security Supply Chain Using UAVs: Validation and Development of a UAV-Based Model for Qatar’s Mega Sporting Events

Unmanned aircraft vehicles (UAVs) are now used to support security precautions in search and rescue operations to track and evaluate critical services, to provide cybersecurity measures by transporting security supply chain management (SCM) to sports events, and to aid efforts to safeguard the spectators from attacks. A drone may quickly fly over sports grounds, scan the area for potential dangers, and offer aerial footage and still photographs. Although UAVs provide benefits to their operators, there is a possibility that they may also pose cybersecurity threats. This guide offers recommendations for best security practices, intending to assist sports operators in protecting their networks, materials, and staff for Qatar’s mega sporting events. The literature comprises several theoretical frameworks and conceptual models for security supply chains. Unfortunately, there is no practical model for measuring the behavioral intentions of professional IT and security experts. Therefore, this study conducted research in two stages. In the first stage, an in-depth systematic literature review was conducted to identify the factors and themes of UAV-based SCM for security measures. In the second phase, a survey questionnaire (N = 712) was implemented, comprising the themes and items from the literature review among professional IT and security experts. Exploratory factor analysis (EFA) was carried out with IBM SPSS, and confirmatory factor analysis (CFA) was employed with IBM AMOS. This study proposed and developed a UAV-based SCM model to provide security for Qatar’s mega sporting events, which comprised five factors: traceability, security and privacy, trust, acceptability, and preparedness. This study also confirmed the validity and reliability of the newly developed scales, offering practical and proposed implications for the IT and security industries. The key findings of the study are: (1) a valid and reliable UAV-based cybersecurity framework for FIFA mega sporting events was developed; (2) five critical factors were identified, including traceability, security and privacy, trust, acceptability, and preparedness; (3) all factors were significantly and positively correlated, highlighting the complexity of managing security systems in mega sporting events.

Demographic Comparison of Information Security Behavior Toward Health Information System Protection: Survey Study.

The health information system (HIS) functions are getting wider with more diverse users. Information security in the health industry is crucial because it involves comprehensive and strategic information that might harm human life. The human factor is one of the biggest security threats to HIS. This study aims to investigate the information security behavior (ISB) of HIS users using a comprehensive assessment scale suited to the information security concerns in health care. Patients are increasingly being asked to submit their own data into HIS systems. As a result, this study examines the security behavior of health workers and patients, as well as their demographic variables. We used a quantitative approach using surveys of health workers and patients. We created a research instrument from 4 existing measurement scales to measure prosecurity and antisecurity behavior. We analyzed statistical differences to test the hypotheses, that is, the Kruskal-Wallis test and the Mann-Whitney test. The descriptive analysis was used to determine whether the group exhibited exemplary behavior when processing the survey results. A correlational test using the Spearman correlation coefficient was performed to establish the significance of the relationship between ISB and age as well as level of education. We analyzed 421 responses from the survey. According to demographic factors, the hypotheses tested for full and partial security behavior reveal substantial differences. Education levels most significantly affect security behavior differences, followed by user type, gender, and age. The health workers' ISB is higher than that of the patients. Women are more likely than men to engage in prosecurity actions while avoiding antisecurity behaviors. The older the HIS user, the more likely it is that they will participate in prosecurity behavior and the less probable it is that they will engage in antisecurity behavior. According to this study, differences in prosecurity behavior are mostly impacted by education level. Higher education, on the other hand, does not guarantee improved ISB for HIS users. All demographic characteristics, particularly concerning user type, show discrepancies that are caused mainly by antisecurity behavior rather than prosecurity behavior. Since patients engage in antisecurity behavior more frequently than health workers and may pose security risks, health care facilities should start to consider information security education for patients. More comprehensive research on ISB in health care facilities is required to better understand the patient's perspective, which is currently understudied.

BPADTA: Blockchain-based privacy-preserving authentication scheme for digital twin empowered aerospace industry

Ensuring privacy and security in the aerospace industry's digital twin (DT) applications is of utmost importance, particularly when sensitive data collected by aerospace assets is shared with remote servers for simulation purposes. Moreover, protecting the security of DT data retrieved from cloud servers in response to data user requests presents a significant challenge. Existing literature proposes various authentication schemes; however, their high communication and computation overhead make them impractical for providing efficient security. To address these concerns, we introduce BPDATA (an improved version of Son et al.'s and Thakur et al.'s scheme), a secure authentication scheme for cloud-based DT platforms, leveraging blockchain technology to preserve privacy. BPDATA facilitates the secure sharing of sensitive data through cloud computing while ensuring data verifiability and integrity through blockchain integration. We use BAN logic and informal security analysis to demonstrate BPDATA's resilience against discrete attacks, outperforming contemporary schemes with lower computation and communication costs. Compared to the scheme proposed by Son et al., BPADTA demonstrated superior performance in terms of computation cost, requiring only 178.297 ms, and communication cost, amounting to 5184 bits. Additionally, BPADTA effectively countered attacks targeting stolen smart devices, anonymity, and untraceability. Furthermore, we employ Raspberry Pi to evaluate the performance of BPDATA, validating its efficiency and suitability for real-world implementation in the aerospace industry.

Blockchain-Enabled Internet of Things (IoT) Applications in Healthcare: A Systematic Review of Current Trends and Future Opportunities

The use of advanced computer technology in the healthcare industry has the potential to improve patient care and therapeutic results. The goal of this project is to improve data security, privacy, and decentralisation in healthcare by integrating blockchain and Internet of Things (IoT) technologies. The adoption of IoT devices makes it possible to gather and analyse patient sensory data in real–time; however centralised processing and storage present problems such as data manipulation and privacy issues. The study investigates the creation of a decentralised IoT-based e-healthcare system that takes these issues into account by utilising blockchain technology. In addition, the paper also emphasises how blockchain use has advanced smart contract technologies. Smart contracts provide safe user authentication for IoT device access, assuring responsibility, traceability, and data integrity. The study investigates the potentially game-changing applications of blockchain technology in healthcare, such as enhanced data interoperability, patient-cantered care, reduced administrative procedures, and increased transaction transparency. The report also highlights the significance of blockchain in managing pharmaceutical supply chains, considering the essential influence on patient welfare and safety. Effective management is essential in the healthcare business because supply chain interruptions or breaches can have serious implications. The present level of research in blockchain-enabled IoT applications for healthcare is examined comprehensively using the PRISMA framework and records from the Scopus database. The three most important research topics are cloud computing, fog computing, and medical services. The results highlight the important role that blockchain-enabled IoT applications have played in enhancing data security and privacy in the healthcare industry. Real-time data gathering, precise diagnoses, individualised treatments, and simplified administrative procedures are all made possible by the integration of blockchain and IoT. Additionally, scalable solutions and insightful data for healthcare decision-making are provided via fog computing, cloud computing, machine learning, and smart contracts.

A Comprehensive Model for Evaluating Titanium Industry Security in China

Currently, China is the largest consumer of titanium (Ti), yet the development of its Ti industry is limited by numerous factors, such as industrial structure imbalance. This study aimed to evaluate the security of China’s Ti industry from 2010 to 2020, seeking to identify relevant issues and propose policy strategies. Firstly, a comprehensive evaluation system for Ti industry security was established, encompassing aspects of availability, economics, and sustainability. Secondly, the entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) combination method and gray correlation method were employed to assess the safety level of China’s Ti industry chain in each year from 2010 to 2020. Additionally, the coupling degree and sensitivity were used to analyze the dimension layers and index system to determine those that negatively impact the safety level of the Ti industry chain. The analysis results reveal that the economic level exerts a significant influence on the development of the Ti industry. Accordingly, under the same level of change, while considering availability, equal attention should be provided to economic considerations for a well-rounded evaluation of the industry’s safety level.

Effect of Storage Time on the Physical, Chemical, and Rheological Properties of Blueberry Jam: Experimental Measurements and Artificial Neural Network Simulation.

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel's local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.

Adaptive 3D Reversible Data Hiding Technique Based on the Cumulative Peak Bins in the Histogram of Directional Prediction Error

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel’s local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.

Instantiation and Implementation of HEAD Metamodel in an Industrial Environment: Non-IoT and IoT Case Studies

Access to resources can take many forms: digital access via an onsite network, through an external site, website, etc., or physical access to labs, machines, information repositories, etc. Whether access to resources is digital or physical, it must be allowed, denied, revoked, or disabled using robust and coherent access control (AC) models. What makes the process of AC more complicated is the emergence of digital transformation technologies and pervasive systems such as the internet of things (IoT) and industry 4.0 systems, especially with the growing demand for transparency in users’ interaction with various applications and services. Controlling access and ensuring security and cybersecurity in IoT and industry 4.0 environments is a challenging task. This is due to the increasing distribution of resources and the massive presence of cyber-threats and cyber-attacks. To ensure the security and privacy of users in industry sectors, we need an advanced AC metamodel that defines all the required components and attributes to derive various instances of AC models and follow the new and increasing demand for AC requirements due to continuous technology upgrades. Due to the several limitations in the existing metamodels and their inability to answer the current AC requirements, we have developed a Hierarchical, Extensible, Advanced, Dynamic (HEAD) AC metamodel with significant features that overcome the existing metamodels’ limitations. In this paper, the HEAD metamodel is employed to specify the needed AC policies for two case studies inspired by the computing environment of Institut Technologique de Maintenance Industrielle (ITMI)-Sept-Îles, QC, Canada; the first is for ITMI’s local (non-IoT) environment and the second for ITMI’s IoT environment. For each case study, the required AC model is derived using the domain-specific language (DSL) of HEAD metamodel, then Xtend notation (an expressive dialect of Java) is utilized to generate the needed Java code which represents the concrete instance of the derived AC model. At the system level, to get the needed AC rules, Cypher statements are generated and then injected into the Neo4j database to represent the Next Generation Access Control (NGAC) policy as a graph. NGAC framework is used as an enforcement point for the rules generated by each case study. The results show that the HEAD metamodel can be adapted and integrated into various local and distributed environments. It can serve as a unified framework, answer current AC requirements and follow policy upgrades. To demonstrate that the HEAD metamodel can be implemented on other platforms, we implement an administrator panel using VB.NET and SQL.

Quantum Technologies and the Engineering of Josephson JunctionBased Sensors: Disruptive Innovation as a Strategic Differential for National Security and Defense

The so-called area of quantum technologies is a new and disruptive technological field, essentially from physics and engineering, with the ability to affect almost all human activities. They are based on the properties of quantum mechanics like quantum tunneling, quantum entanglement, and quantum superposition. Examples of areas of application of these emerging technologies are quantum computing, quantum communications, quantum sensing, quantum cryptography, quantum internet, quantum imaging, quantum metrology, quantum biology, among others. These technologies are of the interest of both the defense and security industry, and military and governmental actors. For military applications, and after a strong advanced engineering process, these technologies introduce new capabilities, improving effectiveness and increasing precision, thus leading to the so-called quantum warfare, wherein new military strategies, doctrines, policies, and ethics should be established. In particular, these military applications of quantum technologies can be described for various warfare domains like land, air, space, electronic, cyber, and underwater warfare and also for intelligence, surveillance, target acquisition and reconnaissance. Among those applications mentioned before, quantum computing is expected to have a number of important uses such as optimization and machine learning, associated to both artificial intelligence and blockchain. Quantum computers are perhaps best known for their expected ability to carry out Shor’s algorithm, which can be used to factorize large numbers and is an important process in the securing of data transmissions, essential for military applications. In this review of our own work, we show the physics and some of the applications of the Josephson junction which plays and important and essential role in the fabrication of quantum computers with superconducting qubits. Nowadays, applications of Josephson junction devices go from the most sensitive sensor to measure magnetic flux, called S.QU.I.D. (from Superconducting Quantum Interference Device) to the core of quantum computers, the quantum bits. The Josephson junction is an old quantum engine for a rising new world with incredible disruptive technological possibilities in engineering, especially in military applications where it plays a fundamental strategic differential for National Security and Defense.

Research on Urban Monitoring Network Management Platform based on AI Data Stream Processing

In recent years, with the upsurge of the continuous development of high-tech information industry, the security industry has also made a new leap in technology, products, industry and so on. The traditional single small-scale monitoring and management software obviously cannot meet the actual needs of users in terms of system scale and functional requirements. This paper puts forward a network management platform for urban monitoring based on AI data stream processing. With the help of comprehensive processing and analysis of urban complex information, through network remote monitoring means, information supervision and public opinion trends are used to control social environmental security, timely warning and response to unexpected accidents, so as to ensure that governance departments can fully control and prevent possible problems such as road traffic, population distribution and disaster emergency.

Investigation of barriers and mitigation strategies to blockchain technology implementation in construction industry: an interpretive structural modeling approach.

Blockchain technology has emerged as a promising solution for enhancing transparency, efficiency, and security in various industries, including construction. However, implementing blockchain in the construction industry faces several barriers that hinder its widespread adoption. To fill this gap, this study is aimed at identifying and discussing the key barriers to implementing blockchain in the construction industry. The study employs a four-phase methodology. In the first phase, barriers are identified through a comprehensive literature review and supplemented with semi-structured interviews with different stakeholders, identifying critical barriers. The second phase involves data collection from ten construction experts, while the third phase utilizes the interpretive structural modeling (ISM) technique for data analysis. In the fourth phase, ten effective strategies are formulated to tackle the identified barriers and their interdependencies. The results reveal the overall structure of barriers and classify them into four groups based on driving and dependence power using the cross-impact matrix multiplication applied to the classification (MICMAC) technique. The identified barriers include a lack of collaboration in consortium formation, insufficient capacity and performance, and regulatory uncertainty, which hinders blockchain implementation in the construction industry. The research findings provide valuable information for policymakers on the overall structure with barriers and implementation in India and other countries with similar situations. Additionally, the management implications of the results were analyzed to facilitate the effective implementation of measures to overcome the obstacles and increase the level of blockchain adoption in the construction industry.

Learning-Based IRS-Assisted Secure Transmission for Mine IoTs.

Mine Internet of Things (MIoT) devices in intelligent mines often face substantial signal attenuation due to challenging operating conditions. The openness of wireless communication also makes it susceptible to smart attackers, such as active eavesdroppers. The attackers can disrupt equipment operations, compromise production safety, and exfiltrate sensitive environmental data. To address these challenges, we propose an intelligent reflecting surface (IRS)-assisted secure transmission system for an MIoT device which enhances the security and reliability of wireless communication in challenging mining environments. We develop a joint optimization problem for the IRS phase shifts and transmit power, with the goal of enhancing legitimate transmission while suppressing eavesdropping. To accommodate time-varying channel conditions, we propose a reinforcement learning (RL)-based IRS-assisted secure transmission scheme that enables MIoT device to optimize both the IRS reflecting coefficients and transmit power for optimal transmission policy in dynamic environments. We adopt the deep deterministic policy gradient (DDPG) algorithm to explore the optimal transmission policy in continuous space. This can reduce the discretization error caused by traditional RL methods. The simulation results indicate that our proposed scheme achieves superior system utility compared with both the IRS-free (IF) scheme and the IRS randomly configured (IRC) scheme. These results demonstrate the effectiveness and practical relevance of our contributions, proving that implementing IRS in MIoT wireless communication can enhance safety, security, and efficiency in the mining industry.

Survey: An Overview of Lightweight RFID Authentication Protocols Suitable for the Maritime Internet of Things

The maritime sector employs the Internet of Things (IoT) to exploit many of its benefits to maintain a competitive advantage and keep up with the growing demands of the global economy. The maritime IoT (MIoT) not only inherits similar security threats as the general IoT, it also faces cyber threats that do not exist in the traditional IoT due to factors such as the support for long-distance communication and low-bandwidth connectivity. Therefore, the MIoT presents a significant concern for the sustainability and security of the maritime industry, as a successful cyber attack can be detrimental to national security and have a flow-on effect on the global economy. A common component of maritime IoT systems is Radio Frequency Identification (RFID) technology. It has been revealed in previous studies that current RFID authentication protocols are insecure against a number of attacks. This paper provides an overview of vulnerabilities relating to maritime RFID systems and systematically reviews lightweight RFID authentication protocols and their impacts if they were to be used in the maritime sector. Specifically, this paper investigates the capabilities of lightweight RFID authentication protocols that could be used in a maritime environment by evaluating those authentication protocols in terms of the encryption system, authentication method, and resistance to various wireless attacks.

Integrated management of safety and security (IMSS) in the nuclear industry – Organizational culture perspective

The study is inspired by the change in the risk landscape caused by the development of digitalization and automation in the high-risk industry. The increasing convergence of process-safety, physical security, and cybersecurity risks can lead to major accidents. Integrated management of safety and security (IMSS) is a necessary means of preventing and preparing for accidents. The objective of this paper is to get new insights into the current state of IMSS and related challenges in the nuclear industry. The data includes the International Atomic Energy Agency (IAEA) and World Institute for Nuclear Security (WINS) reports, articles on digitalization, IMSS, and interviews with safety and security experts from two power companies and the Radiation and Nuclear Safety Authority in Finland. The paper compares the results with those in Seveso installations. The methods are thematic and qualitative content analysis. Theoretical framework consists of organizational culture and management perspectives. The paper provides new meanings to the ways in which IMSS is currently implemented. The paper shows the IAEA structural support to IMSS, differences in IMSS implementation in the nuclear industry, and organizational cultural aspects that constrain the IMSS. The latter include the subordination of security to safety, the assumption that organizational culture automatically integrates safety and security, the lack of co-identification and co-assessment of safety and security risks, which prevents a better understanding of systemic risks. The conclusion is that the current state of IMSS is not adequate to address converging, systemic risks, and coordination of safety and security aspects requires more attention.