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Record W2511238452

Reducing Security Incidents in a Canadian PHIPA Regulated Environment with an Employee-Based Risk Management Strategy

2016· article· en· W2511238452 on OpenAlexaboutno aff
Eduardo DeSouza, Raul Valverde

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2016
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessInformation securityInformation security managementData breachRisk managementSecurity managementWork (physics)Employee engagementEmployee researchPublic relationsKnowledge managementSecurity information and event managementComputer securityComputer scienceEngineeringFinanceCloud computing security
DOInot available

Abstract

fetched live from OpenAlex

The paper uses a case study research approach in defining how an employee based risk management strategy such as employee information security training, employee motivation, and quality assurance can be used to reduce security incidents in a Canadian PHIPA regulated environment. During the research, information security professionals and employees were asked direct questions aimed at understanding the reasons why internal data breaches are recurrent, and what are users’ perception and understanding of existing security policies, processes, and their role in protecting information in their work environment. By using a qualitative case study research design method, data was collect from a small but targeted group of information security professionals and employees within healthcare organization in Ontario. The gathered data was analyzed to identify what are the main causes of security incidents, and what organizations,in the healthcare field can do to better involve their employees for the reduction of breaches and incidents.
\nThe recommendations made by this research paper have the potential of influencing an organization’s
\norganizational culture and employee behavior. The main goal of this paper was to develop an employee based risk management strategy for enterprise level risk management focused on positively influencing employee behaviour.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2016
Admission routes1
Has abstractyes

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