Information Security Risks and Countermeasures in CPA Practices
Bibliographic record
Abstract
With proliferation of computers and accounting software, information security became a significant concern for individual CPA practitioners and CPA practices. This research is based on a survey conducted by the authors at the Second Accounting and Tax CPA seminar at California State University at Los Angeles (CSULA) in 2011. The purpose of this research is to establish metrics and assess cyber security for solo practitioners, small and mid-size CPA practices by surveying and analysing risks that the practices are facing and the countermeasures they employ. The survey reveals the perceptions and practices of CPA practitioners and employees of CPA firms, small and medium, related to cybersecurity, associated risks in practices of their organizations, and the challenges they confront in their efforts to prevent, detect, and respond to such risks. Among our key findings are: profiles of CPA practices; indexes for Weighted Risk Expectancy (WRE) and Weighted Countermeasure Expectancy (WCE) based on the risk/countermeasure significance and likelihood of occurrences; comparative analysis of WRE and WCE for CPA firms of various sizes and their averages.
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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".