AI助手提升身份管理效率，实验显示准确率高48%，耗时少43%。
Randomized Controlled Trials for Conditional Access Optimization Agent
- 用随机对照试验评估AI代理在身份策略管理中的表现
- 准确率提升48%，任务时间减少43%，尤其在复杂任务中优势明显
- 适合企业安全团队、身份管理员及自动化工具研发者
AI代理正被用于自动化企业复杂工作流，但其在身份治理中的有效性证据仍有限。本文报告了首个针对Microsoft Entra中条件访问（CA）策略管理的随机对照试验（RCT）。该代理协助完成四项高价值任务：策略合并、零信任基线差距检测、分阶段上线规划和用户-策略对齐。在生产环境中，162名身份管理员被随机分配至对照组（无代理）或实验组（代理辅助），执行上述任务。结果显示，使用代理后，准确率提升48%，任务完成时间减少43%，且在保持准确率不变的前提下显著提速。最大收益出现在认知负荷较高的任务如基线差距检测中。结果表明，专用AI代理可显著提升身份管理的速度与准确性。
原文摘要 · Abstract (English)
AI agents are increasingly deployed to automate complex enterprise workflows, yet evidence of their effectiveness in identity governance is limited. We report results from the first randomized controlled trial (RCT) evaluating an AI agent for Conditional Access (CA) policy management in Microsoft Entra. The agent assists with four high-value tasks: policy merging, Zero-Trust baseline gap detection, phased rollout planning, and user-policy alignment. In a production-grade environment, 162 identity administrators were randomly assigned to a control group (no agent) or treatment group (agent-assisted) and asked to perform these tasks. Agent access produced substantial gains: accuracy improved by 48% and task completion time decreased by 43% while holding accuracy constant. The largest benefits emerged on cognitively demanding tasks such as baseline gap detection. These findings demonstrate that purpose-built AI agents can significantly enhance both speed and accuracy in identity administration.
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