• Convergence Security Journal
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• v.24 no.2
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• pp.9-17
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• 2024

This paper examines the security threats to enterprise Generative Artificial Intelligence systems and proposes countermeasures. As AI systems handle vast amounts of data to gain a competitive edge, security threats targeting AI systems are rapidly increasing. Since AI security threats have distinct characteristics compared to traditional human-oriented cybersecurity threats, establishing an AI-specific response system is urgent. This study analyzes the importance of AI system security, identifies key threat factors, and suggests technical and managerial countermeasures. Firstly, it proposes strengthening the security of IT infrastructure where AI systems operate and enhancing AI model robustness by utilizing defensive techniques such as adversarial learning and model quantization. Additionally, it presents an AI security system design that detects anomalies in AI query-response processes to identify insider threats. Furthermore, it emphasizes the establishment of change control and audit frameworks to prevent AI model leakage by adopting the cyber kill chain concept. As AI technology evolves rapidly, by focusing on AI model and data security, insider threat detection, and professional workforce development, companies can improve their digital competitiveness through secure and reliable AI utilization.

• The Journal of Bigdata
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• v.2 no.2
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• pp.9-16
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• 2017

Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.159-174
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• 2023

In this paper, we proposed a methodology to effectively determine the selection and prioritization of new business and innovation projects using AI technology. AI technology is a technology that can upgrade the business of companies in various industries and increase the added value of the entire industry. However, there are various constraints and difficulties in the decision-making process of selecting and implementing AI projects in the enterprise. In this paper, we propose a new methodology for prioritizing AI projects using Morphology, AHP, and TOPSIS. The proposed methodology helps prioritize AI projects by simultaneously considering the technical feasibility of AI technology and real-world user requirements. In this study, we applied the proposal methodology to a real enterprise that wanted to prioritize multiple AI projects in the HR field and evaluated the results. The results confirm the practical applicability of the methodology and suggest ways to use it to help companies make decisions about AI projects. The significance of the methodology proposed in this study is that it is a framework for prioritizing multiple AI projects considered by a company in the most reasonable way by considering both business and technical factors at the same time.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.7-17
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• 2023

Artificial Intelligence (AI) technology has already penetrated deeply into our daily lives, and we live with the convenience of it anytime, anywhere, and sometimes even without us noticing it. However, because AI is imitative intelligence based on human Intelligence, it inevitably has both good and evil sides of humans, which is why ethical principles are essential. The starting point of this study is the AI principles for companies or organizations to develop products. Since the late 2010s, studies on ethics and principles of AI have been actively published. This study focused on AI principles declared by global companies currently developing various products through AI technology. So, we surveyed the AI principles of the Global 500 companies by market capitalization at a given specific time and collected the AI principles explicitly declared by 46 of them. AI analysis technology primarily analyzed this text data, especially LDA (Latent Dirichlet Allocation) topic modeling, which belongs to Machine Learning (ML) analysis technology. Then, we conducted a Delphi technique to reach a meaningful consensus by presenting the primary analysis results. We expect to provide meaningful guidelines in AI-related government policy establishment, corporate ethics declarations, and academic research, where debates on AI ethics and principles often occur recently based on the results of our study.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.147-152
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• 2019

Infrastructure is rapidly growing as Internet business grows with the latest IT technologies such as IoT, BigData, and AI. However, in most companies, a limited number of people need to manage a lot of hardware and software. Therefore, Polestar Enterprise Management System(PEMS) is applied to monitor the system operation status, IT service and key KPI monitoring. Real-time monitor screening prevents system malfunctions and quick response. With PEMS, you can see configuration information related to IT hardware and software at a glance, and monitor performance throughout the entire end-to-end period to see when problems occur in real time.

• Journal of Information Technology Services
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• v.20 no.2
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• pp.57-76
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• 2021

Today's ERP system has become a core system of IT·SW that not only supports and manages enterprise resources efficiently, but also encompasses major business tasks. In other words, the ERP system is an essential strategic element for the survival of a company as a powerful means to innovate the management of an organization. This study analyzed the impact on the utilization of IT·SW from the perspective of the entire organization's process without limiting the performance evaluation of the ERP system itself, which is a core system of a company. The measurement data for evaluating the performance of the ERP system was the 2018 domestic company IT·SW utilization survey result report (subject to survey : 3,017 domestic companies with 10 or more employees). Based on this data, this study analyzed the impact of the ERP system on the entire enterprise's IT·SW utilization. In particular, attention was paid to whether there would be a difference in the use of IT·SW if the type of ERP system introduction was changed through the improvement of the business process of the company. Multiple regression analysis was performed using the statistical package SPSS 25. As a result, among the ERP system introduction types, the greatest degree of (+)influence on the company's IT·SW utilization is when the ERP package SW or ASP service is used as it is. Although the difference is insignificant, the second case was to build an ERP system through self-development or outsourcing, followed by customizing the package SW or system through self-development or outsourcing. Through the results of this study, it is expected that the organization will improve the business process and use the standard ERP package SW as it is without modification, thereby effectively enhancing the use of IT·SW of the company and leading to management performance.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.22 no.7
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• pp.30-40
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• 2004

인터넷 및 웹 응용 서비스 프로그램의 확산은 기업의 비즈니스 환경을 급격하게 변화시키고 있다. 또한 기업들은 다양한 e-비즈니스의 창출을 통해서 보다 나은 고객 서비스의 제공과 기업의 생산성 향상을 꾀하고 있다. 이러한 요구사항에 부합하며 제시된 기업 정보화 지원 솔루션 중 하나가 e-비즈니스 응용 프로그램 통합 (이하 eAI)이다. 기존에도 다양한 형태의 응용 프로그램 통합이 추구되었지만 대부분의 경우 기업 내부의 응용 프로그램들을 통합하는 수준이었다. eAI는 기존 EAI(Enterprise Application Integration)와 B2Bi(Business-to-Business Integration)를 하나로 통합한 시스템으로, 통합 대상을 기업 내부에 머무르지 않고 기업 외부의 응용 프로그램까지 포함한다. 즉 eAI는 기업 내ㆍ외부의 상이한 플랫폼 상에 산재되어 있는 복수의 응용 프로그램 및 비즈니스 프로세스를 효율적으로 통합해 기업 정보화의 효과를 극대화하는 것을 지원하는 솔루션이라고 정의할 수 있다.

• Journal of Families and Better Life
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• v.30 no.4
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• pp.13-24
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• 2012

This study attempts to examine the effect of family friendly work policies on the work-family balance of employed married women with young children. While previous research has investigated the effects of family friendly work policies, the effects has often been confounded with the effects of other covariates such as worker's and workplace's characteristics. In this study, we try to distinguish the effects of the family friendly work policies from those of other covariates. We draw a sample of 131 employed married women with children under age 12 from the $2^{nd}$ National Korean Family Survey. We compare the level of work-famiy balance of the women by the type of workplace: public sector, large enterprise, medium enterprise, and small enterprise. The results of this study show that some of the differences in the work-family balance of the women working in the different type of workplace can be attributed to socio-demographic background of the women and the work characteristics of workplace. There is, however, an effect of family friendly policies on the work-family balance between those who work in public sector and in medium enterprise after controlling the effects of the covariates.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.202-207
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• 2023

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1283-1292
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• 2022

At present, several artificial intelligence projects in the healthcare and medical field are competing with each other, and the interfaces between the systems lack unified specifications. Thus, this study presents an artificial intelligence platform for healthcare and medical fields which adopts the deep learning technology to provide algorithms, models and service support for the health and medical enterprise applications. The suggested platform can provide a large number of heterogeneous data processing, intelligent services, model managements, typical application scenarios, and other services for different types of business. In connection with the suggested platform application, we represents a medical service which is corresponding to the trusted and comprehensible tracking and analyzing patient behavior system for Health and Medical treatment using Internet of Behavior concept.