ATM: Automated Threat Modelling for Enterprise AI-enabled Assets

在当今的人工智能时代，大多数公司使用的人工智能资产结合了机器学习和深度学习模型。在这种情况下，人工智能帮助企业进行决策。构建和实施人工智能应用程序的估计成本平均为5万美元。据报道，到2030年，人工智能对全球经济的贡献将达到15.7万亿美元。由于人工智能革命，人们越来越依赖人工智能基础设施，特别是在银行和金融、零售、航空、自主工业、保险、机器人等行业。由于人工智能技术现在几乎存在于每个行业，如果不加以保护，人工智能失控的可能性将造成灾难性的影响。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，而不考虑安全需求。因此，近年来基于人工智能的攻击变得越来越普遍。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。对AI/ML最常见的攻击是数据中毒、模型劫持、对抗性和迁移学习攻击。为了解决这些威胁可能对客户资产造成的负面影响，他们必须知道他们的设备是否容易受到攻击。在这种情况下，企业执行威胁建模以了解其基础设施的脆弱性。人工脆弱性评估是最常见的方法；然而，它既不实用也不准确，因为它不能提取和理解人工智能算法和数据。因此，自主ai辅助威胁建模不仅可以帮助设计全面准确的威胁模型，还可以帮助做出适当的响应。然而，据我们所知，目前还没有自主的ai支持的威胁建模解决方案来深入分析针对ai支持资产的威胁。此外，现有的脆弱性评估方法并不能保证客户数据的保密性。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以减轻发现的威胁，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 era, most companies use AI assets incorporating machine learning and deep learning models. In this context, AI assists enterprises in their decision-making process. The estimated cost of building and implementing an AI application is **$50k on average**. It is reported that AI will contribute up to **$15.7 trillion** to the global economy **by 2030**. Due to the AI revolution, there has been an increased dependency on AI infrastructures, especially in sectors like Banking and Finance, Retail, Aviation, Autonomous Industries, Insurance, Robotics, etc. Since AI-enabled technologies are present in almost every sector nowadays, the chances of AI going rogue would create a catastrophic impact if not protected.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 are _**Data Poisoning, Model Hijacking, Adversarial, and Transfer Learning Attacks**._To address the negative impacts such threats can cause on client assets, they must know if their device is vulnerable. In this context, enterprises perform threat modelling to understand the vulnerability of their infrastructures. Manual vulnerability assessment is the most common approach; however, it is neither a practical nor accurate method for analysing AI-enabled assets' vulnerabilities as it cannot extract and understand AI algorithms and data. Hence, 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. However, 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.