Virtual Library

Our virtual library is an online repository of all of the reports, papers, and briefings that IST has produced, as well as works that have influenced our thinking.

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Reports

Information Sharing in the Ransomware Payment Ecosystem: Exploring the Delta Between Best Practices and Existing Mechanisms

Zoë Brammer

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Memo

Roadmap to Potential Prohibition of Ransomware Payments

Ransomware Task Force Co-Chairs

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Reports

Unlocking U.S. Technological Competitiveness: Evaluating Initial Solutions to Public-Private Misalignments

Ben Purser, Pavneet Singh

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Reports

Public Private Partnerships to Combat Ransomware: An inquiry into three case studies and best practices

Elizabeth Vish, Georgeanela Flores Bustamante

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Reports

Unlocking U.S. Technological Competitiveness: Public-Private Misalignments in Biotechnology, Energy, and Quantum Sectors

Ben Purser, Pavneet Singh

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Reports

Effects of Electromagnetic Pulses on Communication Infrastructure: An IST Primer

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Reports

How Does Access Impact Risk? Assessing AI Foundation Model Risk Along a Gradient of Access

Zoë Brammer, along with contributors from the AI Foundation Model Access Working Group

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We also welcome additional suggestions from readers, and will consider adding further resources as so much of our work has come through crowd-sourced collaboration already. If, for any chance you are an author whose work is listed here and you do not wish it to be listed in our repository, please, let us know.

SUBMIT CONTENT

Preemptive Discussions: The Potential Implications of Integrating Deep Learning into Early Warning Systems

Alisha Anand, Liza Arias, Belen Bianco, Fabian Hoffmann, Artur Honich, Natasha Karner, Niels Renssen, Elisabeth Suh, Lydia Wachs, Alexa Wehsener

SUMMARY

Early warning systems (EWS) are a critical part of the global nuclear command, control, and communications (NC3) enterprise. As nations begin to modernise these systems, discussion of further integration of artificial intelligence (AI) and machine learning (ML) approaches into various aspects of NC3 systems has publicly (and presumably privately) emerged via international expert communities. AI and ML are concepts that have become ‘buzz words’ but are often discussed without reference to the exact meaning and context in which they could be applied. This paper seeks to explore the areas in which EWS could be subject to the integration of novel machine learning (ML) techniques, particularly deep learning (DL) — an integration which is presumably already occurring in the intelligence, surveillance, and reconnaissance (ISR) spheres of the nuclear realm. In doing so, the authors hope to both raise awareness of this ML technique given its rise in popularity across various sectors, as well as provide an example of the way in which novel technologies could be discussed in the nuclear context. In order to assess the consequences of such integration, it is necessary for stakeholders to both understand the technology and discuss its significance in open fora. Although knowledge is limited and security concerns remain, such discussions are vital to encouraging transparency and risk reduction as well as mitigating negative implications. The following analysis offers actionable processes for stakeholders regarding the potential opportunities and risks associated with the potential integration of DL into EWS in order to mitigate risks and increase crisis stability.

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