Cybersecurity Enterprises Policies: A Comparative Study

Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization’s cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises’ security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.

Analysis for the Robotic process automation firm’s future development based on the business model

Robotic process automation tool provides an open-source platform that enables their customers to automate repetitive tasks to reduce their time of working, in which tool can be used in various industrial fields such as banking, healthcare, finance, etc. Our analysis is based on SWOT analysis, Porter’s five force, pistol analysis, and financial projection. We set the UiPath which is one of the leading companies in the industry as an object to analysis whether it deserves the long-term investment and what is the advantage and disadvantages of the company. Besides that, our article also illustrates the environment of the industry, whether it will support UiPath or not. Eventually, we find the UiPath is an investable company, so our team decides to use 1 billion to invest in the company in exchange for 7% equity.

A Survey on Security of the Hadoop Framework in the Environment of Bigdata

The world is becoming increasingly digital at the moment. Every day, a significant amount of data is generated by everyone who uses the internet nowadays. The data are critical for carrying out day-to-day operations, as well as assisting corporate management in achieving their objectives and making the best judgments possible based on the information gathered. BigData is the process of merging many hardware and software solutions to deal with extremely huge amounts of data that surpass storage capability. It’s possible that large amounts of data will be generated. Hadoop systems are used in a variety of areas, including healthcare, finance, and government. insurance, and social media, in order to provide a quick and cost-effective big data solution. The Apache Hadoop is a framework for storing and processing data, managing, and distributing large amounts of information over a large number of server nodes. Here are some solutions that work on top of the Apache Hadoop stack to guarantee data security. To get a complete picture of the problem, we decided to conduct an investigation into existing security solutions for Apache Hadoop security in sensitive data which is stored on a huge data platform employing distributed computing on a cluster of commodity devices. The goal of this paper is to provide knowledge of security and Big Data issues.

Enterprise Robotic Process Automation

Robotic Process Automation (RPA) is an emerging technology widely used across multiple sectors such as human resources, healthcare, finance, accounting, manufacturing, higher education and supply chain management, etc. RPA, also known as ‘software bots’, replaces manual, rule-based, repetitive tasks humans perform. These software bots are currently in a journey, evolving to be more sophisticated, mimicking human activities and enabling humans to achieve higher-valued tasks. Hence, RPA impacts the overall operational efficiency in organisations through multiple facets by its integration with employees, existing technologies and infrastructure, and business processes. It reduces the burden on IT as it does not disturb the underlying legacy systems. It increases reliability as bots can work 24x7 effectively. It is used as a time and cost reduction technology as it reduces the size of the manual workload. The tasks performed through RPA is accurate as it is less prone to errors. It increases compliances as it follows the rules and keeps audit trails. The productivity rate of organisations increase as the execution time through RPA is faster than tasks being performed by human employees. Furthermore, RPA is introduced as a low code technology that uses drag and drop functionalities with little to no programming knowledge [1].

Blockchain for Securing AI Applications and Open Innovations

Nowadays, open innovations such as intelligent automation and digitalization are being adopted by every industry with the help of powerful technology such as Artificial Intelligence (AI). This evolution drives systematic running processes, involves less overhead of managerial activities and increased production rate. However, it also gave birth to different kinds of attacks and security issues at the data storage level and process level. The real-life implementation of such AI-enabled intelligent systems is currently plagued by the lack of security and trust levels in system predictions. Blockchain is a prevailing technology that can help to alleviate the security risks of AI applications. These two technologies are complementing each other as Blockchain can mitigate vulnerabilities in AI, and AI can improve the performance of Blockchain. Many studies are currently being conducted on the applicability of Blockchains for securing intelligent applications in various crucial domains such as healthcare, finance, energy, government, and defense. However, this domain lacks a systematic study that can offer an overarching view of research activities currently going on in applying Blockchains for securing AI-based systems and improving their robustness. This paper presents a bibliometric and literature analysis of how Blockchain provides a security blanket to AI-based systems. Two well-known research databases (Scopus and Web of Science) have been examined for this analytical study and review. The research uncovered that idea proposals in conferences and some articles published in journals make a major contribution. However, there is still a lot of research work to be done to implement real and stable Blockchain-based AI systems.

A Study of Use Cases for Smart Contracts Using Blockchain Technology

Blockchain technology is an emerging and rapidly growing technology in the current world scenario. It is a collection of records connected through cryptography. They play a vital role in smart contracts. Smart contracts are present in blockchains which are self-controlled and trustable. It can be integrated across various domains like healthcare, finance, self-sovereign identity, governance, logistics management and home care, etc. The purpose of this article is to analyze the various use cases of smart contracts in different domains and come up with a model which may be used in the future. Subsequently, a detailed description of a smart contract and blockchain is provided. Next, different case-studies related to five different domains is discussed with the help of use case diagrams. Finally, a solution for natural disaster management has been proposed by integrating smart contract, digital identity, policies and blockchain technologies, which can be used effectively for providing relief to victims during times of natural disaster.

A Blockchain-Based Federated Learning

Conventional machine learning (ML) needs centralized training data to be present on a given machine or datacenter. The healthcare, finance, and other institutions where data sharing is prohibited require an approach for training ML models in secured architecture. Recently, techniques such as federated learning (FL), MIT Media Lab's Split Neural networks, blockchain, aim to address privacy and regulation of data. However, there are difference between the design principles of FL and the requirements of Institutions like healthcare, finance, etc., which needs blockchain-orchestrated FL having the following features: clients with their local data can define access policies to their data and define how updated weights are to be encrypted between the workers and the aggregator using blockchain technology and also prepares audit trail logs undertaken within network and it keeps actual list of participants hidden. This is expected to remove barriers in a range of sectors including healthcare, finance, security, logistics, governance, operations, and manufacturing.

A Literature Review on Device-to-Device Data Exchange Formats for IoT Applications

Internet of Things (IoT) has emerged as a disruptive innovation that not only promises to transform every aspect of human activity but is driving revolutions in many fields, including but not limited to: healthcare, finance, agriculture, quality control and retail. For communicating data between connected devices in IoT ecosystem, various data exchange formats are presently in use. Determining the appropriate data format for an application plays a pivotal role in order to ensure efficient communication and reducing overheads. The intent of this literature review is to document and analyse literature relating to data exchange formats. The paper contains a systematic review of several academic research papers focused on performance evaluation and comparison of data formats. We reviewed the predominant data formats used for the purpose of data interchange and discussed the strengths, weaknesses and suitable application areas for the same.

An integrated approach to assessing the efficiency of health financing in the Russian regions

Subject. The article presents theoretical and historical considerations on the emergence of the contemporary healthcare system in Russia, developments in models and forms of public healthcare protection as public and economic constructs change. Objectives. The study substantiates versatile methodological approaches to evaluating the efficiency of healthcare finance regionally. Methods. Based on the integrated method of factor analysis, the methodological approach evaluates some components of efficiency, i.e. available budgetary finance and resources, social and medical efficiency. To evaluate each type of efficiency, we determines socio-economic, medical and demographic indicators split into blocks. Results. We examined the model of the contemporary one-source finance of the Russian healthcare in regions and provided a detailed account of all cash flows and influx. The article analyzes the existing methodological approaches to evaluating the efficiency of healthcare finance, marks a wide spectrum of relevant issues pointed out by contemporary researchers. The technique was tested with the case of the healthcare systems in the Perm Krai, Sverdlovsk Oblast and Samara Oblast, and the comparative approach to evaluating the regional healthcare and the national indicators. Thus, we found critical points and challenging areas in the healthcare systems of the analyzable regions. Conclusions and Relevance. The methodological approach to evaluating the efficiency of regional healthcare finance we propose is versatile and applicable to the constituent entities of the Russian Federation.