Securing Inclusive Digital Environments: An Adaptive Approach to ISO 27001 for Assistive Technologies in SMEs
Bibliographic record
Abstract
The integration of Assistive Technologies (AT) within Small and Medium-sized Enterprises (SMEs) is pivotal for fostering inclusive digital environments, particularly for neurodiverse workforces. While AT empowers individuals with disabilities to overcome systemic barriers to employment, it concurrently introduces unique cybersecurity and privacy risks due to the sensitive nature of the user data it handles. This report underscores the critical need for robust information security in such contexts. This report demonstrates how ISO 27001 serves as a foundational framework for achieving a balance between stringent security requirements and essential accessibility needs. Drawing from a detailed case study of a Canadian SME, the report highlights key adaptive strategies, including customized security training, collaborative risk management, and the crucial role of co-creating security policies with neurodiverse employees. These practices illustrate that security and accessibility are not mutually exclusive objectives but rather complementary goals that, when aligned, significantly enhance both compliance and operational efficiency. The analysis reinforces that a proactive, user-centric approach to information security is vital for protecting sensitive AT data, strengthening organizational resilience, and ultimately paving the way for a more equitable and inclusive digital future.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".