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Record W2018051412 · doi:10.5430/afr.v2n3p123

Information Security Risks and Countermeasures in CPA Practices

2013· article· en· W2018051412 on OpenAlexvenueno aff
Ludwig Slusky, Rick Hayes, Richard R. Lau

Bibliographic record

VenueAccounting and Finance Research · 2013
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsnot available
Fundersnot available
KeywordsCountermeasureBusinessExpectancy theoryInformation securityBest practiceAccountingActuarial scienceComputer securityEconomicsManagementComputer scienceEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.007
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.357
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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