Management of Cybersecurity through Internal Communication
Bibliographic record
Abstract
Organizations increasingly experience threats to their organization&s;s cybersecurity such as data theft, manipulation, and fraud. However, the topic is rarely discussed in the field of communication management. Defined as a set of guidelines, technologies, and training that provide protection of an organization&s;s data and of its computer and digital communication infrastructure, cybersecurity is perceived as a critical topic for communications professionals. This chapter discusses findings from a survey on perceptions about cybersecurity from 1,046 communication professionals in the United States and Canada, conducted as part of North American Communications Monitor (NACM) 2020–2021. This survey provides comprehensive understandings of communication professionals&s; personal and organizational experiences related to cyberattacks or data threat; internal information management to enhance cybersecurity; and perceptions of likelihood of attacks from cybercriminals. Moreover, the chapter shares various insights applicable for organizations&s; internal communication to better manage emerging threats to cybersecurity. Internal communication presents a number of opportunities to prevent and mitigate crises related to cyberattacks and data thefts. The survey findings revealed the importance of internal communication and employee engagement for cybersecurity issues. Based on these findings, practical implications for educators and practitioners will be further discussed at the end.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".