Developments in Practice XXXIII: A Holistic Approach to Managing IT-based Risk
Bibliographic record
Abstract
Not long ago, IT-based risk was a fairly low-key activity focused on whether IT could deliver projects successfully and keep applications up and running. But with the opening up of the organization’s boundaries to external partners, service providers, external electronic communications, and online services, managing IT-based risk has morphed into a “bet the company” proposition. Not only is the scope of the job bigger, the stakes are much higher. As companies have become more dependent on IT for everything they do, the costs of service disruption and inadequate security practices have escalated exponentially. Therefore, the job of managing IT-based risk has become broader and more complex. Whereas in the past companies have sought security through physical or technological means (e.g., locked rooms, virus scanners), there is now growing understanding that managing IT-based risk must be a strategic and holistic activity that is not just the responsibility of a small group of IT specialists, but part of a mindset that extends from partners and suppliers to employees and customers. This paper explores how organizations are addressing and coping with increasing IT-based risk. It presents the results of an in-depth discussion of this issue with 20 senior IT practitioners and the challenges facing them. It proposes a holistic view of risk and examines the characteristics and components needed to develop an effective risk management framework, presenting a generic framework for integrating the growing number of elements involved in it. Finally, it describes successful practices organizations could use for improving their risk management capabilities.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".