Calibrated Trust in AI for Security Operations: A Conceptual Framework for Analyst–AI Collaboration
Bibliographic record
Abstract
Artificial intelligence (AI) is increasingly integrated into security operations to support threat detection, alert triage, and incident response. However, miscalibrated trust in AI systems—manifesting as either over-reliance or undue skepticism—can undermine both operational effectiveness and human oversight. This paper presents a conceptual framework for calibrated trust in AI-driven security operations, emphasizing analyst–AI collaboration rather than fully autonomous decision-making. The framework synthesizes key dimensions including transparency, uncertainty communication, explainability, and human-in-the-loop controls to support informed analyst judgment. We discuss how calibrated trust can mitigate automation bias, reduce operational risk, and enhance analyst confidence across common security workflows. The proposed framework is intended to guide the design, deployment, and evaluation of trustworthy AI systems in security operations and to serve as a foundation for future empirical validation.
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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 | 0.031 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".