Bibliographic record
Abstract
Abstract Cyber risk is one of the greatest threats facing any modern financial system; a result of increasing dependence on technology and the appeal of troves of personal data to well-equipped hackers. This article examines the governance of cyber risk in the Canadian banking system in the context of the Covid-19 crisis, which has led to a surge in cyber-attacks. It argues that the existing Canadian regime, which draws heavily on the Basel operational risk framework, is unfit to handle the unique challenges posed by cyber risk. Cyber incidents are unlike traditional operational disruptions in both their dynamism and impact, and are not adequately captured by backward-looking proxies, such as historical losses. There is also a mismatch between the traditional risk-based supervision, which relies on annual risk rating of banks, and the quickly changing cyber profile of regulated entities. Furthermore, the bilateral and institution-specific nature of such supervision leaves out the crucial systemic perspective on cyber risk. This article calls for the current quantitative paradigm, which underlies capital adequacy regulation, to be complemented with a resilience-centric approach aimed at better accommodating and learning from unpredictable cyber incidents. This shift requires revisiting traditional supervisory practices, such as extensive reliance on centralized decision-making and planning—which may prove ineffective in the face of a firm-wide cyber incident—and a dynamic approach that keeps regulation in line with emergent knowledge. The article outlines a number of strategies which can help banks and regulators navigate and adapt to the ever-changing cyber landscape.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".