Zero-Day Delivery: Hacking Risks and the Use of Machine Learning for Military Logistics

Neural network symbolizing machine learning

Christopher Mohr analyzes the opportunities and hacking and other risks of incorporating machine learning into military logistics and identifies legal and security gaps that make incorporation difficult.

Mohr begins by providing background on artificial intelligence and machine learning and describes how these tools can improve military logistics. Mohr also identifies types of cyberattacks that states and non-state actors can use to interfere with such tools.

Mohr then analyzes gaps in the civil legal framework, focusing on the Defense Federal Acquisition Regulations and Defense Trade Secrets Act, which create civil liability for some hackers and require defense contractors to implement cybersecurity measures. However, Mohr notes how both regimes are not adequate for guarding against attacks against machine learning systems.

Criminal legal frameworks are analyzed, primarily the Electronic Communication Privacy Act and Computer Frad and Abuse Act. Mohr recognizes gaps in the regime that would make deterring, prosecuting, and investigating hacks of machine learning systems challenging.

The article concludes with recommendations for changes that Congress can make to the existing civil and criminal frameworks to prepare for the use of machine learning in military logistics.

By Christopher Mohr

Georgetown University Law Center, J.D., 2024; Tufts University, B.A., 2018.

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