机器学习在用户行为审计中的应用

Application of Machine Learning in User Behavior Audit

该文通过分析运营商业务系统多年的用户操作日志数据,运用机器学习技术揭示了各应用系统在业务操作中敏感数据操作风险、业务违规操作风险及审计漏洞。针对这些问题,提出了加强数据监控、深化业务关系分析及完善事后审查等建议。希望该文可以为相关企业在用户行为审计工作中提供一定的参考,从而让智能化审计能力更好地服务于审计工作,由此实现自动化、实时化愿景,通过多维度数据分析深化风险识别,构建风险预警模型,提升应对能力,提升审计风险管理水平。

By analyzing the user operation log data of operator business system for many years,this paper uses machine learning technology to reveal the sensitive data operation risk,business violation operation risk and audit vulnerability of each application system in business operation.To solve these problems,some suggestions are put forward,such as strengthening data monitoring,deepening business relationship analysis and improving postexamination.It is hoped that this paper can provide some reference for relevant enterprises in user behavior audit,so that intelligent audit capability can better serve audit work,so as to realize the vision of automation and real-time,deepen risk identification through multidimensional data analysis,build risk early warning model to improve response ability,and improve audit risk management level.