Data Security Strategies for Preventing Breaches Due to Insider Threats
Bibliographic record
Abstract
Banking insider threats have been on the rise over the past decade.Information technology (IT) leaders in banks are concerned about the impact of insider threats because most of these attacks have been carried out by individuals accessing businesssensitive data, which, if exposed, could have severe business consequences.Grounded in actor-network theory, the purpose of this pragmatic inquiry study was to explore the security strategies used by IT security managers in banking industries to prevent breaches due to insider threats.Participants were six IT security managers in the banking industry in southeastern Canada who implemented security strategies to prevent insider threats.Data were collected using semi-structured in-person interviews, field notes, industry documents, security archival records and other publicly available security documents.Using thematic analysis, six themes were identified: (a) the need for security standards, procedures, and policies, (b) need for information security education and training, (c) importance of organizational security culture, (d) importance of asset management, (e) importance of identity and access management, and (f) importance of data security.A major recommendation is for IT leaders to invest more in security controls and integrate people, processes and technologies while creating a security culture within banks to help detect and prevent insider threat attacks.The implications for positive social change include the potential to improve security awareness, reduce maliciousness, and compliance with standards and procedures to prevent breaches, which may improve customer confidence in banking.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".