検索キーワード： 著者 "Congressional Research Service" すべてのカテゴリー

Electricity is essentially the lifeblood of the modern, technological society that we enjoy, for without it, the devices and machinery that enable our economy would not be able to function. The US grid is aging, and one of the tools for modernizing the system is the incorporation of computerized systems that allow for the rapid monitoring and control of the equipment used in the grid. Connecting these systems to the internet has increased the ability to remotely control aspects of the grid, but this has come with increasing concern for the grid’s cybersecurity.

Ransomware attacks — the use of malicious software to deny users access to data and information systems to extort ransom payments from victims — are prevalent. A recent notable example is the May 2021 ransomware attack that temporarily shut down the Colonial Pipeline Company’s network, affecting gasoline availability and prices. This attack is but one of many; in 2020 alone, the Federal Bureau of Investigation (FBI) received nearly 2,500 ransomware complaints with losses exceeding $29 million.

The vast US network of natural gas, crude oil, and refined product pipelines is integral to US energy supply and also has vital links to other critical infrastructure such as power plants and airports. This network is vulnerable to cyberattacks. Pipeline companies employ technologies which enable them to achieve business and operational efficiencies, but these technologies are susceptible to cybersecurity risks - and these risks have been growing. The May 2021 ransomware attack against the Colonial Pipeline, which disrupted gasoline supplies throughout the East Coast.

Since the public release of Open AI’s ChatGPT, Google’s Bard, and other similar systems, some Members of Congress have expressed interest in the risks associated with “generative artificial intelligence (AI).” Although exact definitions vary, generative AI is a type of AI that can generate new content—such as text, images, and videos—through learning patterns from pre-existing data.