An Empirical Study on the Obstacle Factors of ISMS Certification Using Exploratory Factor Analysis
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Bibliographic record
Abstract
최근 들어 세계적으로 정보자산 유출에 대한 문제가 대두되고 있다. 국가정보원에 따르면, 2003년부터 2013년까지 총 375건의 해외 기술 유출을 적발했으며, 특히 2013년 한 해에만 49건이 적발되는 등 시간이 갈수록 증가 추세를 보이고 있다. 이는 기업에서 정보보호 관리체계를 수립 운용하고 이를 인증 받아야 할 필요성이 있음을 뜻한다. 하지만 ISMS 인증을 받기 위해서는 아직까지 이미 드러난, 혹은 아직 드러나지 않은 장애요인들이 상당히 존재하며, 관련 연구 또한 아직 부족하다. 따라서 본 연구에서는 기업이 ISMS 인증 시에 어떤 장애요인을 가지고 있는지 탐색적 요인 분석 기법을 이용하여 실증적으로 분석하였다. 연구 결과, 심사 난이도 및 기간, 컨설팅 업체 관련 요인, 인증 선행 사례 및 컨설팅 인력 자질, 내부적 요인, 인증기관 신뢰도 및 심사 비용, 인증 혜택 관련 요인과 같이 총 여섯 개의 압축된 요인을 도출하였다. In the past few years, data leakage of information assets has become a prominent issue. According to the National Intelligence Service in South Korea, they found 375 cases of data leakage from 2003 to 2013, especially 49 of cases have been uncovered in 2013 alone. These criminals are increasing as time passes. Thus, it constitutes a reason for establishment and operation of ISMS (Information Security Management System) even for private enterprises. But to be ISMS certified, there are many exposed or unexposed barriers, moreover, sufficient amount of studies has not been conducted on the barriers of ISMS Certification. In this study, we analyse empirically through exploratory factor analysis (EFA) to find the obstacle factors of ISMS Certification. The result shows that there are six obstacle factors in ISMS Certification; Auditing difficulty and period, Consulting firm related, Certification precedence case and consulting qualification, Internal factor, CA reliability and auditing cost, Certification benefit.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it