The cyber-resilience of financial institutions: significance and applicability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The growing sophistication, frequency and severity of cyberattacks targeting financial sector institutions highlight their inevitability and the impossibility of completely protecting the integrity of critical computer systems. In this context, cyber-resilience offers an attractive complementary alternative to the existing cybersecurity paradigm. Cyber-resilience is defined in this article as the capacity to withstand, recover from and adapt to the external shocks caused by cyber risks. Resilience has a long and rich history in a number of scientific disciplines, including in engineering and disaster management. One of its main benefits is that it enables complex organizations to prepare for adverse events and to keep operating under very challenging circumstances. This article seeks to explore the significance of this concept and its applicability to the online security of financial institutions. The first section examines the need for cyber-resilience in the financial sector, highlighting the different types of threats that target financial systems and the various measures of their adverse impact. This section concludes that the “prevent and protect” paradigm that has prevailed so far is inadequate, and that a cyber-resilience orientation should be added to the risk managers’ toolbox. The second section briefly traces the scientific history of the concept and outlines the five core dimensions of organizational resilience, which is dynamic, networked, practiced, adaptive, and contested. Finally, the third section analyses three types of institutional approaches that are used to foster cyber-resilience in the financial sector (and beyond): (i) a thriving cybersecurity industry is promoting cyber-resilience as the future of security; (ii) standards bodies are embedding cyber-resilience into some of their cybersecurity standards; and (iii) regulatory agencies have developed a broad range of compliance tools aimed at enhancing cyber-resilience.
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.
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 | 0.001 | 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 | 0.000 | 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 it