Douglas L. Van Bossuyt, Britta Hale, R. Arlitt, N. Papakonstantinou
{"title":"Zero-Trust for the System Design Lifecycle","authors":"Douglas L. Van Bossuyt, Britta Hale, R. Arlitt, N. Papakonstantinou","doi":"10.1115/1.4062597","DOIUrl":null,"url":null,"abstract":"\n In an age of worsening global threat landscape and accelerating uncertainty, the design and manufacture of systems must increase resilience and robustness across both the system itself and the entire systems design process. We generally trust our colleagues after initial clearance/background checks; and systems to function as intended and within operating parameters after safety engineering review, verification, validation, and/or system qualification testing. This approach has led to increased insider threat impacts; thus we suggest moving to the “trust, but verify” approach embodied by the Zero-Trust paradigm. Zero-Trust is increasingly adopted for network security but has not seen wide adoption in systems design and operation. Achieving the goal of Zero-Trust throughout the systems lifecycle will help to ensure that no single bad actor -- whether human or machine learning / artificial intelligence (ML/AI) -- can induce failure anywhere in a system's lifecycle. Additionally, while ML/AI and their associated risks are already entrenched within the operations phase of many systems' lifecycles, ML/AI is gaining traction during the design phase. For example, generative design algorithms are increasingly popular but there is less understanding of potential risks. Adopting the Zero-Trust philosophy helps ensure robust and resilient design, manufacture, operations, maintenance, upgrade, and disposal of systems. We outline the rewards and challenges of implementing Zero-Trust and propose the Framework for Zero-Trust for the System Design Lifecycle. The paper highlights several areas of ongoing research with focus on high priority areas where the community should focus efforts.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"63 9","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computing and Information Science in Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062597","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 2
Abstract
In an age of worsening global threat landscape and accelerating uncertainty, the design and manufacture of systems must increase resilience and robustness across both the system itself and the entire systems design process. We generally trust our colleagues after initial clearance/background checks; and systems to function as intended and within operating parameters after safety engineering review, verification, validation, and/or system qualification testing. This approach has led to increased insider threat impacts; thus we suggest moving to the “trust, but verify” approach embodied by the Zero-Trust paradigm. Zero-Trust is increasingly adopted for network security but has not seen wide adoption in systems design and operation. Achieving the goal of Zero-Trust throughout the systems lifecycle will help to ensure that no single bad actor -- whether human or machine learning / artificial intelligence (ML/AI) -- can induce failure anywhere in a system's lifecycle. Additionally, while ML/AI and their associated risks are already entrenched within the operations phase of many systems' lifecycles, ML/AI is gaining traction during the design phase. For example, generative design algorithms are increasingly popular but there is less understanding of potential risks. Adopting the Zero-Trust philosophy helps ensure robust and resilient design, manufacture, operations, maintenance, upgrade, and disposal of systems. We outline the rewards and challenges of implementing Zero-Trust and propose the Framework for Zero-Trust for the System Design Lifecycle. The paper highlights several areas of ongoing research with focus on high priority areas where the community should focus efforts.
期刊介绍:
The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications.
Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping