SENTRY: Deterministic, Intelligent Risk Assessment for IT Change Management
Score
72将检索增强与确定性ML结合用于IT变更风险评分，提供了可操作的架构创新。
方法描述完整，实验指标充分，并讨论了架构权衡，学术严谨性较高。
直接解决金融行业IT变更管理的实际痛点，且技术栈成熟易落地。
论文编号为2608，内容反映较新的AI应用方向，但具体发布时效不确定。
来源为arXiv预印本，属于权威学术平台，但尚未经同行评审。
Base 80.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
The method balances determinism and explainability with strong experimental results, though single-domain validation and potential overfitting warrant further observation.
AI commentary
「This paper is worth attention because it embeds RAG and XGBoost into regulated processes in a restrained, auditable way, rather than piling on large models. It offers a practical reference for fintech, IT operations leaders, and risk teams, showing a non-over-engineered approach that significantly improves risk detection.」
Quick overview
IT change management in large financial institutions relies on risk scoring, but traditional questionnaire-based assessments are subjective, easy to game, and poor at distinguishing routine changes from high-risk ones. This paper presents SENTRY, a platform that replaces questionnaire scoring with a deterministic machine learning pipeline built on XGBoost gradient-boosted decision trees and hybrid retrieval-augmented generation (RAG). The system integrates structured operational metadata, application dependency graphs, and historical incident records, and uses hybrid semantic and lexical search over historical change requests. The retrieval step captures risk signals from unstructured change text and compresses them into a single scalar feature before model inference, keeping the model deterministic and enabling per-prediction explainability via SHAP values. Evaluated on enterprise-scale change data, SENTRY achieves a ROC AUC of 0.87, 85% overall accuracy, and detects high-risk changes at roughly 3.25 times the rate of the existing process. The paper also discusses architectural trade-offs and implications for machine learning in regulated change management.
- SENTRY replaces questionnaire-based risk scoring with XGBoost and hybrid RAG in a deterministic ML pipeline
- Compresses unstructured change request text into a scalar feature via hybrid retrieval, preserving explainability
- Achieves ROC AUC 0.87 and 85% accuracy on enterprise-scale data, with 3.25x high-risk detection rate
- Examines architectural trade-offs for ML in regulated change management
Full text
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