在使用STRIDE的保险聊天机器人中引发安全威胁和漏洞
Zilungile Bokolo1, Olawande Daramola2
1Department of Information Technology, Cape Peninsula University of Technology, Cape Town, South Africa.
Scientific reports
|August 2, 2024
概括
保险聊天机器人面临着重大的安全风险. 这项研究使用了STRIDE建模来识别欺骗和拒绝服务等漏洞,这对于提高金融服务中聊天机器人数据安全至关重要.
科学领域:
- 网络安全 网络安全
- 信息系统信息系统信息系统
- 金融技术 (金融科技)
背景情况:
- 聊天机器人越来越多地用于客户关系管理 (CRM),特别是在金融服务领域.
- 聊天机器人广泛访问敏感的公司和客户数据,这给数据安全和隐私控制带来了重大挑战.
- 聊天机器人的数据泄露可能会对机构及其客户造成严重的财务和声誉损害.
研究的目的:
- 识别保险部门聊天机器人的数据安全漏洞和威胁.
- 将STRIDE (伪造,改,拒绝,信息披露,拒绝服务,提升特权) 威胁建模框架应用于保险聊天机器人.
- 为改善保险行业内聊天机器人的安全姿势提供见解.
主要方法:
- 在南非的一家保险组织中进行了一项案例研究.
- 从利益相关方收集数据,以了解聊天机器人的使用情况和操作.
- 对已识别的聊天机器人使用案例进行了基于STRIDE的分析,以引出安全威胁和漏洞.
主要成果:
- 与伪造,拒绝服务和特权升级相关的安全漏洞被发现与保险聊天机器人高度相关.
- 发现的最重要的安全威胁是改,特权提升和欺骗.
- 该研究强调了在保险领域聊天机器人安全方面的具体关注领域.
结论:
- 这些发现强调了保险聊天机器人强有力的数据安全策略的必要性.
- 了解与STRIDE相关的漏洞对于减轻聊天机器人部署中的风险至关重要.
- 这项研究为有关聊天机器人安全的知识贡献,帮助保险行业的安全专业人员和研究人员.
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