An integrated view of human, organizational, and technological challenges of IT security management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this study is to determine the main challenges that IT security practitioners face in their organizations, including the interplay among human, organizational, and technological factors. Design/methodology/approach The data set consisted of 36 semi‐structured interviews with IT security practitioners from 17 organizations (academic, government, and private). The interviews were analyzed using qualitative description with constant comparison and inductive analysis of the data to identify the challenges that security practitioners face. Findings A total of 18 challenges that can affect IT security management within organizations are indentified and described. This analysis is grounded in related work to build an integrated framework of security challenges. The framework illustrates the interplay among human, organizational, and technological factors. Practical implications The framework can help organizations identify potential challenges when implementing security standards, and determine if they are using their security resources effectively to address the challenges. It also provides a way to understand the interplay of the different factors, for example, how the culture of the organization and decentralization of IT security trigger security issues that make security management more difficult. Several opportunities for researchers and developers to improve the technology and processes used to support adoption of security policies and standards within organizations are provided. Originality/value A comprehensive list of human, organizational, and technological challenges that security experts have to face within their organizations is presented. In addition, these challenges within a framework that illustrates the interplay between factors and the consequences of this interplay for organizations are integrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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