SBIR Phase I: SaiFE: Trusted AI with Hardware Security Enforcement

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响集中在通过显着增强人工智能（AI）和机器学习（ML）硬件和系统的安全状态来提升经济和社会福祉，这些硬件和系统越来越普遍，并用于安全/安全关键应用。由于该项目分析硬件攻击并开拓新的防御，它确保了社会依赖于医疗保健，金融和国家安全的AI/ML技术的更可靠的基础。商业潜力巨大；随着开发人员部署这些强化系统，他们减轻了代价高昂的破坏风险，促进了信任并加速了采用。经济效益还扩大到减少与网络攻击相关的支出，并提高安全AI/ML产品的市场竞争力。此外，通过加深对硬件漏洞和防御机制的理解，该项目推动了网络安全科学知识的前沿。因此，该项目的创新将加强关键基础设施，抵御以硬件为中心的威胁，从而保护数字经济，加强美国在安全技术进步方面的领导地位。这个小企业创新研究（SBIR）第一阶段项目采用了一种变革性的方法来解决保护AI/ML硬件系统免受新兴硬件攻击（如侧信道攻击和故障注入攻击）的严重问题。认识到这些系统对硬件攻击的脆弱性，该项目旨在全面分析攻击向量并设计创新的防御机制。拟议的研究将采用多层方法，集成尖端的加密技术和新颖的机器学习算法，以提高硬件安全性。通过严格的实验和验证，预期的技术成果包括可信硬件模块的开发，硬件威胁评估基准框架的建立，以及适应性强、弹性强的防御体系结构的创建。这将极大地促进对人工智能/机器学习背景下硬件安全的科学理解，可能为行业实践设定新的标准，同时解决现代计算基础设施中的一个关键漏洞。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 Small Business Innovation Research (SBIR) Phase I project is centered on elevating economic and societal well-being by significantly enhancing the security posture of Artificial Intelligence (AI) and Machine Learning (ML) hardware and systems, which are increasingly ubiquitous and used in safety/security-critical applications. As this project analyzes hardware attacks and pioneers new defenses, it ensures a more reliable foundation for AI/ML technologies that society relies upon for healthcare, finance, and national security. The commercial potential is substantial; as developers deploy these fortified systems, they mitigate the risk of costly breaches, fostering trust and accelerating adoption. Economic benefits also extend to a reduction in expenditure related to cyberattacks and an increase in market competitiveness for secure AI/ML products. Furthermore, by deepening understanding of hardware vulnerabilities and defense mechanisms, the project pushes the frontiers of scientific knowledge in cybersecurity. As a result, the innovations from this project are poised to reinforce critical infrastructure against hardware-centric threats, thereby safeguarding the digital economy and reinforcing the United States' leadership in secure technological advancements.This Small Business Innovation Research (SBIR) Phase I project conducts a transformative approach to addressing the acute problem of securing AI/ML hardware systems against emerging hardware attacks such as side-channel and fault injection attacks. Recognizing the vulnerability of these systems to hardware exploitation, the project aims to comprehensively analyze the attack vectors and devise innovative defense mechanisms. The proposed research is set to employ a multi-layered methodology that integrates cutting-edge cryptographic techniques and novel machine-learning algorithms to enhance hardware security. Through rigorous experimentation and validation, the anticipated technical results include the development of trusted hardware modules, the establishment of a benchmarking framework for hardware threat assessment, and the creation of adaptable, resilient defense architectures. This will significantly advance scientific understanding of hardware security in the context of AI/ML, potentially setting a new standard for industry practices, while addressing a critical vulnerability in modern computing infrastructure.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.