ATM: Automated Threat Modelling for Enterprise AI-enabled Assets

在当今的人工智能和数字时代，大多数公司都在使用、部署或正在使用包含机器学习/深度学习模型的人工智能资产。在这种背景下，人工智能帮助企业进行决策。然而，支持AI的资产制造商主要专注于设计、培训和部署基于AI的解决方案，而没有考虑安全需求。因此，基于人工智能的攻击在最近几年变得常见。这些攻击可能会以人类无法察觉的方式操纵人工智能资产的决策。最常见的针对AI/ML建模、数据中毒和迁移学习的攻击。虽然自主的AI辅助威胁建模不仅有助于设计全面和准确的威胁模型，而且还有助于做出适当的响应。据我们所知，目前还没有自主的启用人工智能的威胁建模解决方案来深入分析针对启用人工智能的资产的威胁。此外，现有的脆弱性评估方法不能确保客户数据的机密性。在这方面，我们建议开发一个人工智能辅助的自动威胁建模(ATM)系统，该系统将通过生成威胁模型、提供对策并对它们进行优先排序来缓解已发现的威胁，从而帮助检测与人工智能启用的资产相关的所有威胁。

In today's AI and Digital era, most companies work with, deploy, or are using AI assets incorporating machine learning/deep learning models. In this context, AI assists enterprises in their decision-making process. However, AI-enabled asset manufacturers mainly focus on designing, training and deploying AI-based solutions without considering security requirements. As a result, AI-based attacks have become common over recent years. These attacks can potentially manipulate the decision-making of AI-enabled assets in a way that would be imperceptible to humans. The most common attacks on AI/ML Modelling, Data Poisoning and Transfer Learning Attacks.Although autonomous AI-assisted threat modelling can facilitate not only the design of a comprehensive and accurate threat model but also assist with making an appropriate response. To the best of our knowledge, there is no autonomous AI-enabled threat-modelling solution to analyse threats against AI-enabled assets in great depth. Moreover, existing vulnerability assessment approaches do not ensure the confidentiality of clients' data.In this regard, we propose to develop an AI-assisted Automated Threat Modelling(ATM) System that will help detect all the threats related to the AI-enabled asset by generating a Threat Model, providing countermeasures, and prioritising them to mitigate discovered threats.