SENTRY: Deterministic, Intelligent Risk Assessment for IT Change Management· SENTRY：IT 变更管理中的确定性智能风险评估
Technology change management in large financial institutions depends on risk assessments that are accurate, consistent, and auditable. In practice, many institutions still rely on self-reported questionnaires. Those questionnaires are subjective, easy to game, and poor at separating routine changes from the ones that later trigger major incidents. This paper presents SENTRY, a risk assessment platform that replaces questionnaire-based scoring with a deterministic machine learning pipeline built from gradient-boosted decision trees (XGBoost) and hybrid retrieval-augmented generation (RAG). The system combines structured operational metadata, application dependency graphs, and historical incident records with a hybrid semantic and lexical search over historical change requests. The retrieval
提出 SENTRY 平台，以自动化和确定性方式评估 IT 变更风险。
- 核心方法
- 使用 XGBoost 和混合检索增强生成 (RAG) 构建确定性机器学习管道，结合结构化操作元数据、应用依赖图和历史事故记录进行智能风险评估。
- 适合谁读
- 研究者、工程师、产品经理
- 要解决的问题
- 解决大型金融机构中 IT 变更管理依赖主观问卷评估风险的问题。
- 关键实验
- 未提供
- 主要贡献
- 提供一个自动化的、基于数据的风险评估解决方案，减少主观性和提高评估准确性。
- 意义与局限
- SENTRY 平台有望提高 IT 变更管理的风险评估效率和可靠性，减少重大事故，但在实际应用中可能面临数据质量和技术复杂性的挑战。