Ensuring Compliance Integrity in AI ML Cloud Environments: The Role of Data Guardianship

Sohel Rana
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Abstract

Artificial intelligence (AI) has become ubiquitous across various industries, including security, healthcare, finance, and national defense. However, alongside its transformative potential, there has been a concerning rise in malicious exploitation of AI capabilities. Simultaneously, the rapid advancement of cloud computing technology has led to the emergence of cloud-based AI systems. Unfortunately, vulnerabilities inherent in cloud infrastructure also pose security risks to AI services. We recognize the critical role of maintaining the integrity of training data, as any compromise therein directly impacts the effectiveness of AI systems. In response to this challenge, we emphasize the paramount importance of preserving data integrity within AI systems. To address this need, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cybersecurity framework. Leveraging blockchain technology and smart contracts presents a suitable solution for addressing integrity challenges, given their features of shared and decentralized ledgers. Smart contracts enable automated policy enforcement, facilitate continuous monitoring of data integrity, and help mitigate the risk of data tampering.
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确保人工智能 ML 云环境中的合规完整性:数据监护的作用
人工智能(AI)已在各行各业无处不在,包括安全、医疗、金融和国防。然而,在发挥其变革潜力的同时,恶意利用人工智能能力的现象也在不断增加,令人担忧。与此同时,云计算技术的快速发展也导致了基于云的人工智能系统的出现。不幸的是,云基础设施固有的漏洞也给人工智能服务带来了安全风险。我们认识到维护训练数据完整性的关键作用,因为其中的任何漏洞都会直接影响人工智能系统的有效性。为了应对这一挑战,我们强调在人工智能系统中保持数据的完整性至关重要。为了满足这一需求,我们提出了一个以美国国家标准与技术研究院(NIST)网络安全框架为指导的数据完整性架构。区块链技术和智能合约具有共享和去中心化分类账的特点,因此利用区块链技术和智能合约是应对完整性挑战的合适解决方案。智能合约能够自动执行政策,促进对数据完整性的持续监控,并有助于降低数据被篡改的风险。
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