US Public Data LabU.S. public data, made readable
Data areas
Federal Register Congress bills CISA KEV / NVD SEC EDGAR USAspending Defense contracts Trade Economy Energy Places FDA recalls AI Research Grants NIH research grants Fiscal & national debt
Tools
Search Recent Rankings Compare Money flows Timeline Compare indicators Topics
Profiles
Company profiles Agency profiles Glossary Contact
EN▾
日本語 English
$1,942,880 CyberAICorps SFS

The same technology serving both defence and attack: the motive a call sets out

New Jersey Institute of Technology ・New Jersey ・Started Aug 2026

About $1.94 million to the New Jersey Institute of Technology. The description states that while AI strengthens defence, it also enables more sophisticated attacks.

Grant overview (primary data)

  • Award amount$1,942,880
  • RecipientNew Jersey Institute of Technology (New Jersey)
  • ProgramCyberAICorps SFS
  • Period2026-08-01 〜 2029-07-31
  • FunderU.S. National Science Foundation (NSF) / NSF

Key points

  • About $1,943,000 to the New Jersey Institute of Technology, fully obligated.
  • The description sets out both sides of AI: strengthening defence and enabling more sophisticated attack.
  • Named on the attack side are adversarial machine learning, data poisoning, AI-assisted vulnerability discovery and autonomous exploitation.
  • It is stated as building on more than a decade of CyberCorps Scholarship for Service work.

1Two motives set side by side

The previous article gave as its motive that AI is entering government systems. This description goes a step further and states that the same technology works in both directions.

Amount of this awardabout $1.94 millionfully obligated
RecipientNew Jersey Institute of Technologystated as building on a decade of SFS experience
Period1 August 2026 to 31 July 2029identical to the previous article

2The two sides the description names

AspectWhere AI works for defenceWhere AI works for attack
Handling of datalarge-scale data analysisadversarial machine learning
Assumptions about traininganomaly detectiondata poisoning
Where automation pointsautomated responseAI-assisted vulnerability discovery
How far it can reach—autonomous exploitation of systems

Defenders gaining tools and attackers gaining tools happen at the same time. The description frames the national need as urgent precisely because both are true.

3Built on something already running

The award states that it builds on more than a decade of the university CyberCorps Scholarship for Service work. Rather than constructing a new framework, it widens the scope of a running one toward AI. Recruiting students, training them and placing them in government cybersecurity roles is unchanged.

The next article takes up an award that designs the degree pathway itself.

Why it matters

Organisations adopting AI have to assume improved defence and more advanced attack at the same time. This award states both sides as workforce requirements.

FAQ

What is adversarial machine learning?
A family of techniques that craft inputs to derail a model judgement. This description lists it among the attacks AI enables.
What is data poisoning?
Mixing crafted material into training data to bend a model behaviour in an intended direction.
How does this differ from the existing programme?
By the description, it extends an existing Scholarship for Service track into the intersection with AI rather than replacing it.

Related profiles

Related explainers

Sources (primary)

Source: NSF Award Search (U.S. National Science Foundation, public domain). Amounts are the obligated amount. For privacy, we do not handle principal investigator names.

#NSF awards#Cybersecurity#Adversarial machine learning#AI workforce#New Jersey

← Back to the AI research grants ranking

Disclaimer: This site independently summarizes and classifies information based on official data sources. Always verify the latest and accurate information with the official sources. Content on finance, health, legal, and security is information, not advice. This site is not an official website of the U.S. government.