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Sprint projectSep 15, 2025

Navigating Safety Measures for Nuclear Nonproliferation: AI-Enabled Early Warning Systems & Governance System For Detecting Nuclear Enrichment

Ifeoma Ilechukwu, Marie-Louise Thurton, Victor Uchenna Jonah, Valmik Nahata and Saahir Vazirani · Team Supa Mavvis Team

Submitted to CBRN AI Risks Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Navigating Safety Measures for Nuclear Nonproliferation: AI-Enabled Early Warning Systems & Governance System For Detecting Nuclear Enrichment

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The Independent Atomic Energy Agency (IAEA) has always sought ways to create a balance between nuclearuse for peaceful purposes and the prevention of weapons proliferation. Yet challenges persist in the form ofclandestine uranium enrichment, covert plutonium processing and illicit trade in sensitive technologies.Recent advances in the integration of Artificial Intelligence into nuclear Command, Control andCommunication (NC3) systems and procedures could help reduce errors from being made in crisis scenarios,enhance situational awareness, improve surveillance and increase operational efficiency. However, theintroduction of AI systems would also amplify existing challenges and create room for adversarialmanipulation where proliferators intentionally feed misleading signals to evade detection systems and reducefacility footprints. This brief critically examines the vulnerabilities and limitations in existing safeguards,recent case studies such as Iran’s suspension of IAEA cooperation and a multimodal AI-enabled monitoringapproach for early detection of nuclear enrichment activities. It concludes with governance approaches toreduce nuclear related misuse and policy measures to align AI capabilities with non-proliferation norms.

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Does the project meaningfully address the theme of CBRN risks and AI misuse? Does the project clearly connect to a CBRN-related threat (chemical, biological, radiological, nuclear)? Does it build on or challenge existing literature or prior work in the space? Does it offer a novel framing, tool, or evaluation that could inform future research or policy? Is it grounded in real-world relevance rather than hypothetical misuse?

Does this project advance the field of AI safety in a clear and valuable way? Does it help identify or mitigate risks specific to powerful AI systems? How well does the project engage with the unique challenges of AI alignment, misuse, or system evaluation? Could the output contribute to scalable safety mechanisms or evaluation pipelines? Does it generalize to broader dual-use or frontier AI concerns?

Is the project thoughtful, usable, and technically sound? Is the technical implementation solid and well-scoped for a research sprint? Is the code or method clearly documented and reproducible? Does the project demonstrate sound methodology, clear assumptions, and an honest treatment of limitations? Would the tool or prototype be useful for future research, governance, or evaluation work?

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Cite this project

@misc{ilechukwu2025navigating,
  title = {{Navigating Safety Measures for Nuclear Nonproliferation: AI-Enabled Early Warning Systems \& Governance System For Detecting Nuclear Enrichment}},
  author = {Ifeoma Ilechukwu and Marie-Louise Thurton and Victor Uchenna Jonah and Valmik Nahata and Saahir Vazirani},
  year = {2025},
  month = sep,
  note = {Submitted to CBRN AI Risks Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/navigating-safety-measures-for-nuclear-nonproliferation-ai-enabled-early-warning-systems-governance-system-for-detecting-nuclear-enrichment-1z7e}},
  url = {https://apartresearch.com/sprints/projects/navigating-safety-measures-for-nuclear-nonproliferation-ai-enabled-early-warning-systems-governance-system-for-detecting-nuclear-enrichment-1z7e}
}
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