Nuclear Regulators Want Machines to Monitor Cyberattacks on Power Plants
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Summary:
Federal nuclear regulators want external researchers to demonstrate how artificial intelligence (AI) and machine learning (ML) can pinpoint cyberattacks against the nation’s nuclear power plants. This request comes almost a year since the Nuclear Regulatory Commission (NRC) asked for feedback regarding how AI and ML technologies are used in nuclear power operations—and will be used down the line. For its latest work, the NRC wants to collaborate with an entity that can provide existing personnel, equipment, and facilities to implement a test case for full assessment. A technical report documenting outcomes will be produced from the effort. The NRC noted that potential partners must have a range of capabilities to meet their criteria, including assets to simulate nuclear power plants and cyberattacks on them, resources to complete the work in 16 months, and more. (NR Notice: NRC-FFR-RES-2022-0001)
Additional Discussion:
As advanced algorithms—often referred to as artificial intelligence technologies—are finding utility in just about every sector, federal nuclear regulators wants to know how AI and machine learning tools can improve the reliability and safety of nuclear energy production.
The Nuclear Regulatory Commission posted an information request in the Federal Register in April 2021, soliciting public input on how AI and ML technologies are currently being used in nuclear power operations, as well as “future trends” on the horizon.
Specifically, the Commission wanted feedback on “the state of practice, benefits, and future trends related to the advanced computational tools and techniques in predictive reliability and predictive safety assessments in the commercial nuclear power industry.”
The request for comment includes 11 questions designed to help the Commission understand where and how these tools are being used and the benefits—including potential cost savings—and risks:
- What is status of the commercial nuclear power industry development or use of AI/ML tools to improve aspects of nuclear plant design, operations, or maintenance or decommissioning? What tools are being used or developed? When are the tools currently under development expected to be put into use?
- What areas of commercial nuclear reactor operation and management will benefit the most, and the least, from the implementation of AI/ML? Possible examples include, but are not limited to, inspection support, incident response, power generation, cybersecurity, predictive maintenance, safety/risk assessment, system and component performance monitoring, operational/maintenance efficiency, and shutdown management.
- What are the potential benefits to commercial nuclear power operations of incorporating AI/ML in terms of (a) design or operational automation, (b) preventive maintenance trending, and (c) improved reactor operations staff productivity?
- What AI/ML methods are either currently being used or will be used in the near future in commercial nuclear plant management and operations? Example of possible AI/ML methods include, but are not limited to, artificial neural networks, decision trees, random forests, support vector machines, clustering algorithms, dimensionality reduction algorithms, data mining and content analytics tools, gaussian processes, Bayesian methods, natural language processing, and image digitization.
- What are the advantages or disadvantages of a high-level, top-down strategic goal for developing and implementing AI/ML across a wide spectrum of general applications versus an ad-hoc, case-by-case, targeted approach?
- With respect to AI/ML, what phase of technology adoption is the commercial nuclear power industry currently experiencing and why? The current technology adoption model characterizes phases into categories such as: the innovator phase, the early adopter phase, the early majority phase, the late majority phase, and the laggard phase.
- What challenges are involved in balancing the costs associated with the development and application of AI/ML tools, against plant operational and engineering benefits when integrating AI/ML into operational decision-making and workflow management?
- What is the general level of AI/ML expertise in the commercial nuclear power industry, e.g. expert, well-versed/skilled, or beginner?
- How will AI/ML effect the commercial nuclear power industry in terms of efficiency, costs, and competitive positioning in comparison to other power generation sources?
- Does AI/ML have the potential to improve the efficiency and/or effectiveness of nuclear regulatory oversight or otherwise affect regulatory costs associated with safety oversight? If so, in what ways?
- AI/ML typically necessitates the creation, transfer, and evaluation of very large amounts of data. What concerns, if any, exist regarding data security in relation to proprietary nuclear plant operating experience and design information that may be stored in remote, offsite networks?
Most recently, the NRC issued NRC-FFR-RES-2022-0001, "Sources Sought Synopsis" as a means of conducting market research or as a market survey to determine the availability of potential qualified vendors (including educational institutions) with the technical capability to provide all management, supervision, administration, and labor for the requirement entitled, “Characterizing Nuclear Cyber Security Using Artificial Intelligence/Machine Learning.” A description of requirements/activities as well as required capabilities is included as Attachment No. 1 to this notice. Services are to be provided to the NRC in Rockville, Maryland. The applicable North American Industry Classification System (NAICS) code assigned to this procurement is 541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology). This is continued through February 19, 2022.
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