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Record W4389638892 · doi:10.34190/ecrm.22.1.1490

Navigating the Intersection of Innovation and Cybersecurity: A Framework

2023· article· en· W4389638892 on OpenAlexaff
Danielle Botha-Badenhorst

Bibliographic record

VenueEuropean Conference on Research Methodology for Business and Management Studies · 2023
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsCanadian Society of Intestinal Research
Fundersnot available
KeywordsMaturity (psychological)BusinessComputer securityRevenueProcess (computing)Risk analysis (engineering)Computer scienceFinancePolitical science

Abstract

fetched live from OpenAlex

Reliance on digital technologies for innovation management is unavoidable in current contexts. While digital processes and business models have been prioritised as key factors to drive innovation and value creation within firms, cybersecurity concerns are still rife. Increased levels and severity of cybersecurity breaches (CSBs) have had adverse effects on trust, caused significant revenue losses, and inflicted reputational damage on many firms. Further exacerbating these concerns is an observation made in the Global Risks Report of 2022, the World Economic Forum: cybersecurity measures taken by businesses are becoming increasingly obsolete. Many firms face severe consequences without implementing strategic objectives to limit the threats posed by CSBs. Cybersecurity breaches (CSBs) have a significant long-term impact on firm-level innovation and investment decisions. However, many firms are reluctant to examine or enhance their existing cybersecurity practices because of concerns that they may limit their innovation ability. Determining a method to limit CSBs and retain capabilities to perform necessary innovative processes is a delicate balance, with trade-offs to be considered within each process. This paper aims to address the delicate balance between limiting CSBs and preserving the ability to undertake necessary innovative processes. Building upon the Cyber Security Maturity and Innovation matrix introduced by Nelson and Madnick (2017), this paper expands the framework by providing specific suggestions for each quadrant. The matrix classifies firms into different quadrants based on their reliance on innovation and their assessment of cyber risk. We then detail measures to improve cybersecurity maturity for firms in each quadrant, incorporating the National Institute of Standards and Technology (NIST) Cybersecurity Framework Version 1.1 (CSF) as a reference. By making well-informed decisions and implementing appropriate measures, firms can effectively mitigate CSB risks while continuing to drive innovation and create value. This expanded framework serves as a valuable tool for firms seeking to align their cybersecurity practices with their innovation objectives, in accordance with the NIST CSF.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.452
GPT teacher head0.497
Teacher spread0.044 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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