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The NIST Cybersecurity Framework (CSF) is a set of guidelines developed by the U.S. National Institute of Standards and Technology (NIST) to help organizations manage and mitigate cybersecurity risks.
The Risk Management Framework (RMF) is a United States federal government guideline, standard, and process for managing risk to help secure information systems (computers and networks). The RMF was developed by the National Institute of Standards and Technology (NIST), and provides a structured process that integrates information security ...
In February 2022, Hendrycks co-authored recommendations for the US National Institute of Standards and Technology (NIST) to inform the management of risks from artificial intelligence. [ 5 ] [ 6 ] In September 2022, Hendrycks wrote a paper providing a framework for analyzing the impact of AI research on societal risks.
eMASS is a service-oriented computer application that supports Information Assurance (IA) program management and automates the Risk Management Framework (RMF). [1] The purpose of eMASS is to help the DoD to maintain IA situational awareness, manage risk, and comply with the Federal Information Security Management Act (FISMA 2002) and the Federal Information Security Modernization Act (FISMA ...
NIST Special Publication 800-37 Rev. 1 was published in February 2010 under the title "Guide for Applying the Risk Management Framework to Federal Information Systems: A Security Life Cycle Approach". This version described six steps in the RMF lifecycle. Rev. 1 was withdrawn on December 20, 2019 and superseded by SP 800-37 Rev. 2. [1]
Experts react to report from Congress's bipartisan task force on artificial intelligence (A.I.), which discusses how the U.S. can safeguard the nation against emerging threats associated with A.I ...
In August 2021, the Partnership on AI submitted a response to the National Institute of Standards and Technology (NIST). The response provided examples of PAI’s work related to AI risk management, such as the Safety Critical AI report on responsible publication of AI research, the ABOUT ML project on documentation and transparency in machine ...
This framework seeks to leverage AI for benefits such as data analysis, investment strategy formulation, customer service improvements, automated risk assessment, and fraud prevention.