An Appraisal in Internal Control Frameworks Implementation of COSO, ISO 27001 and NIST for Opportunities in Industry 5.0
Bibliographic record
Abstract
Industry 5.0 represents a paradigm shift in industrial evolution, emphasising human-machine collaboration through sophisticated cognitive systems. This transformation creates new opportunities and challenges for organisations implementing established frameworks like COSO, NIST, and ISO 27001 for compliance, risk management, and governance. While Industry 5.0 builds upon Industry 4.0's foundation, it focuses explicitly on enhancing cognitive capabilities and human-centric approaches. Cognitive technologies enable the automation of complex tasks such as document review, data analysis, and real-time decision support, leading to improved efficiency, cost reduction, and accuracy. However, integrating these technologies presents significant challenges, particularly in data quality management and algorithmic bias mitigation. This study comprehensively analyses internal management practices in Industry 5.0 organisations through a systematic literature review and conceptual analysis. The research examines academic literature on human-machine collaboration performance management and evaluates key frameworks' applicability in this new paradigm. Given the increasing complexity of automation and interconnectivity in modern enterprises, robust internal controls have become critical for organisational success. This paper aims to guide organisations in adopting Industry 5.0 technologies while effectively integrating cognitive systems and human oversight, ultimately achieving enhanced efficiency, personalisation, and sustainability.
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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 | 0.106 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".