Emerging Technologies and Cyber Risk: How do we secure the Internet of Things (IoT) environment?
Bibliographic record
Abstract
Cloud computing and the Internet of Things (IoT) have transformed businesses, enabling agile and costeffective IT infrastructure. The challenge is that these new opportunities create a co-mingled architecture which is difficult to secure. The complexity of this architecture is magnified with the IoT. Based on interviews with executive leadership teams and boards of directors facing these new environments, we developed the over-arching research question: How do we secure increasingly dynamic architecture in an environment while supporting and creating agile business growth? We then narrowed this down to more specific questions dealt with in this study. The research involved an in-depth exploration of this problem using a survey instrument and multiple qualitative methods involving business leaders from 59 companies between 2017 – 2018. Based on this analysis, we developed an information security framework for executives in this new environment that builds on previous work. This framework is called the Extended Risk-Based Approach and provides businesses with an approach for securing an enterprise amidst the IoT and agile architecture. Importantly, the data analyzed suggests that this approach is critically needed to address the rapidly growing complexity of enterprise architecture and the digital world we live and work.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".