I-Corps: Securing the Machine Learning Lifecycle and Providing Artificial Intelligence Assurance

这个I-Corps项目的更广泛影响是开发一个框架，以安全地开发人工智能/机器学习（AI/ML）。随着AI/ML生命周期的脆弱性变得越来越明显，依赖于AI/ML一致性和完整性的组织面临着确保AI/ML安全性的艰巨任务。除了将零信任概念调整到此类环境中所涉及的挑战外，还需要快速完成确保安全性的工作。开发和实施单个AI/ML模型的成本可能达到数十万美元，而恶意修改AI/ML模型、模型特征和有效毒害AI/ML的训练数据可能会抵消这笔巨额投资的回报。这种投毒攻击是一种新生的威胁，随着恶意行为者获得技术能力，预计将变得普遍。由于AI/ML已成为关键任务防御和情报能力的核心，因此AI/ML对中毒攻击的敏感性构成了严重的国家安全问题。这个I-Corps项目是基于使用区块链技术的生命周期管理工具的开发。拟议的创新将使用户能够存储、训练和部署数据、AI/ML模型和模型特征，用于每个交易或尝试交易。这些事务将被不可变地记录下来，以提供审计跟踪，确保存储在区块链上的任何内容都不会被秘密地更改。这些项目的目标是确定区块链技术的使用如何增强AI/ML生命周期的安全性。该技术可用于分析包括情报机构、学术界、商业企业和机器学习工程师在内的利益相关者的AI/ML生命周期安全问题和焦点。该项目还将帮助确定工程师是否存在切实可行的解决方案，以确保机器学习生命周期的安全，检查经济模型，告知安全性，成本效率，虚拟结构意识的权衡，因为它与“零人工智能信任”的概念有关，并测试机器学习生命周期管理工具的可用性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this I-Corps project is the development of a framework to securely develop Artificial Intelligence/Machine Learning (AI/ML). As the vulnerability of the AI/ML lifecycle becomes increasingly apparent, organizations that rely on the consistency and integrity of their AI/ML are faced with the difficult task of assuring AI/ML security. Along with challenges involved with tailoring the concept of zero trust into such environments, assuring security also needs to be accomplished quickly. The cost to develop and implement a single AI/ML model can reach hundreds of thousands of dollars, and the malicious alteration of AI/ML models, model features, and training data that effectively poison AI/ML can negate the return on this substantial investment. Such poisoning attacks are a nascent threat that is expected to become widespread as malicious actors gain technical ability. This susceptibility of AI/ML to poisoning attacks poses a severe national security concern as AI/ML has become central to mission-critical defense and intelligence capacities. This I-Corps project is based on the development of a lifecycle management tool utilizing blockchain technology. The proposed innovation will enable users to store, train, and deploy data, AI/ML models, and model features, with each transaction or attempted transaction. These transactions will be immutably logged to provide an audit trail, ensuring that nothing stored on the blockchain can be surreptitiously altered. These project goals are to determine how the use of blockchain technology may enhance the security of the AI/ML lifecycle. The technology may be used to analyze the AI/ML lifecycle security concerns and foci of stakeholders including intelligence agencies, academia, commercial businesses, and machine learning engineers. The project will also help determine if practical solutions exist for engineers to secure the machine learning lifecycle, examine economic models that inform the tradeoff of security, cost efficiency, awareness of virtual structure as it relates to the concept of “Zero AI Trust”, and test usability in machine learning lifecycle management tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.