NIST Center for AI Standards and Innovation (CAISI)

government

Credibility Rating

5/5

Gold(5)

Gold standard. Rigorous peer review, high editorial standards, and strong institutional reputation.

Rating inherited from publication venue: NIST

CAISI is a key U.S. government institutional actor in AI safety governance; relevant for those tracking federal AI policy, standards development, and government-industry coordination on AI risk evaluation.

Metadata

Importance: 62/100homepage

Summary

CAISI is NIST's dedicated center for AI security standards and innovation, serving as the primary U.S. government liaison with industry on AI safety and security measurement. It develops voluntary guidelines, conducts capability evaluations for national security risks (cybersecurity, biosecurity), and coordinates U.S. positions in international AI standards bodies.

Key Points

•Serves as primary U.S. government point of contact for industry collaboration on AI security standards and best practices.
•Conducts unclassified evaluations of AI capabilities posing national security risks, including cybersecurity and biosecurity threats.
•Assesses vulnerabilities in adversarial AI systems and develops measurement methodologies for AI security.
•Coordinates across federal agencies and represents U.S. interests in international AI standards processes.
•Produces voluntary guidelines rather than binding regulations, reflecting NIST's traditional standards role.

Cited by 2 pages

Page	Type	Quality
AI Evaluations	Research Area	72.0
Third-Party Model Auditing	Approach	64.0

Cached Content Preview

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Center for AI Standards and Innovation (CAISI) | NIST 
 
 
 
 

 

 
 
 
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 https://www.nist.gov/caisi

 
 

 

 
 
 
 
 

 

 

 
 
 
 

 
 

 
 

 

 
 
 
 Artificial intelligence 

 
 
 
 
 
 
 Center for AI Standards and Innovation (CAISI)

 
 
 
 
 

 
 
 

 
 
 
 
 
 
 

 

 
 
 

 
 
 
 
 
 
 
 
 
 
 
 The Center for AI Standards and Innovation (CAISI) will serve as industry’s primary point of contact within the U.S. government to facilitate testing and collaborative research related to harnessing and securing the potential of commercial AI systems. To that end, CAISI will:

 Work with NIST organizations to develop guidelines and best practices to measure and improve the security of AI systems, and work with NIST staff to assist industry to develop voluntary standards.
 Establish voluntary agreements with private sector AI developers and evaluators, and lead unclassified evaluations of AI capabilities that may pose risks to national security. In conducting these evaluations, CAISI will focus on demonstrable risks, such as cybersecurity, biosecurity, and chemical weapons.
 Lead evaluations and assessments of capabilities of U.S. and adversary AI systems, the adoption of foreign AI systems, and the state of international AI competition.
 Lead evaluations and assessments of potential security vulnerabilities and malign foreign influence arising from use of adversaries’ AI systems, including the possibility of backdoors and other covert, malicious behavior.
 Coordinate with other federal agencies and entities, including the Department of Defense, the Department of Energy, the Department of Homeland Security, the Office of Science and Technology Policy, and the Intelligence Community, to develop evaluation methods, as well as conduct evaluations and assessments.
 Represent U.S. interests internationally to guard against burdensome and unnecessary regulation of American technologies by foreign governments and collaborate with NIST staff to ensure U.S. dominance of international AI standards.
 Read the statement from Secretary of Commerce Howard Lutnick about the Center for AI Standards and Innovation.

 

 

 
 
 
 
 CAISI Research Blog

 
 
 
 
 

 
 
 

 

 
 
 
 
 In December, CAISI published a write-up on how AI models can cheat on agentic evaluations, including lessons from our experience building and using AI-enabled

 
 
 
 
 
 
 

 
 
 
 
 Building gold-standard AI systems requires gold-standard AI measurement science – the scientific study of methods used to assess AI systems’ properties and

 
 
 
 
 
 
 

 
 
 
 
 AI evaluations a

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Resource ID: 94173523d006b3b4 | Stable ID: MWZhNTI1ZT