Intelligence-driven Risk Management in Information Security Systems
Bibliographic record
Abstract
The task of making decisions in information security, when faced with unclear probabilities and unforeseen consequences of events in the constantly evolving cyber threat landscape, has gained significant importance. Cyber threat intelligence equips decision-makers with essential information and context to comprehend and predict future threats, hence minimizing ambiguity and enhancing the precision of risk assessments. Addressing uncertainty in decision-making demands the adoption of a new methodology led by threat intelligence (TI) and a risk analysis approach. This is a crucial aspect of evidence-based decision-making. Our proposed solution to this difficulty involves the implementation of a TI-based security assessment methodology and a decision-making strategy that takes into account both known unknowns and unknown unknowns. The proposed methodology seeks to improve decision-making quality by utilizing causal graphs, which provide an alternative to current methodologies that rely on attack trees, hence reducing uncertainty. In addition, we analyze strategies, methods, and protocols that are feasible, likely, and credible, enhancing our capacity to anticipate enemy actions. Our proposed approach offers practical counsel to information security leaders, enabling them to make well-informed decisions in uncertain circumstances. This paper presents a novel approach to tackling the problem of making decisions in uncertain situations in the field of information security. It introduces a methodology that can assist decision-makers in navigating the complexities of the ever-changing and dynamic world of cyber threats.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| 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".