"When we get to a down-select from grants that are some really promising research projects, we sort of do a deep dive on the [principal investigators] in the grant that says … do they have direct links to the [Chinese Communist Party] leadership, or do they have links to Russian oligarchs?"
Neil Chaudhry on using AI to vet grant recipients
Neil Chaudhry, the Transportation Department’s senior advisor for AI, described in a public panel how his agency has developed "some really robust AI tools" to analyze recipients of federal research grants. Speaking at the SAP Public Sector Summit in Washington, D.C., Chaudhry said the capability is used when the department "gets to a down-select" among promising proposals and performs a "deep dive" on principal investigators and organizations tied to those awards.
How the Transportation Department says the tools work
Chaudhry framed the tools as aimed at tracing connections that might present risk — for example, whether investigators or organizations have ties to "the [Chinese Communist Party] leadership" or "Russian oligarchs." He emphasized two guardrails in the department’s approach: first, the intent is not to "go on a fishing expedition"; second, the screening is "not based on the color of their skin and their nationality or something," but on identifiable factors such as whether a person or entity may be “receiving funding from questionable sources.”
Chaudhry offered a concrete example of the kind of funding chains the tools are meant to flag: "If it's a company that's funded by a [venture capital firm] in Silicon Valley, that VC is probably funded by another VC in the Middle East or in Malaysia, and that VC is then wholly funded by the Chinese industrial bank," he said. "And then when that startup goes out of business, we don't know what just happened to that IP that the U.S. government spent money developing."
Audit-ready is a season. It shouldn't be.
Evidence in spreadsheets, controls drifting between audits, frameworks multiplying on flat headcount. Nubivance runs continuous compliance on Rapid7 Cyber GRC - SOC 2, HIPAA, ISO 27001, PCI, CMMC.
End the scramblePublic transparency: inventory omission and a department response
Despite Chaudhry’s public description, the Transportation Department’s latest AI use-case inventory, published earlier this year, does not list a research grant–focused screening capability. Reporters seeking additional detail were unable to obtain a comment from the department before publication: "Transportation did not respond to a request for comment by the time of publication," the reporting notes.
Related federal activity: GAO findings on SBIR/STTR vetting
The department’s work is contemporaneous with a broader federal effort to harden protections around federally funded research. A November 2024 Government Accountability Office report reviewed how agencies were complying with requirements to better identify foreign risks in the Small Business Innovation Research and Small Business Technology Transfer programs. The GAO wrote that the programs "face risks of foreign actors seeking to illicitly acquire federally funded research and technologies."
GAO also cataloged steps some agencies were taking: for example, it found that the Air Force, the National Science Foundation and NASA were adopting automation or otherwise supporting the vetting of applicants — including automating the processing of due diligence disclosure forms — to identify associated risks.
What this means for research institutions, federal grant officials, and venture-funded startups
- Research institutions and small businesses: Expect deeper scrutiny when applying for Transportation research grants. Chaudhry’s description signals that vetting will extend beyond immediate personnel and applications to financial and ownership chains that involve venture capital and other backers.
- Federal grant officials and agencies: Transportation’s use of AI — described publicly but not reflected in its published AI inventory — raises questions for counterparts about how to document, disclose and coordinate similar capabilities across agencies, especially given GAO attention to SBIR/STTR risks.
- Venture-funded startups and investors: The specific funding-chain example Chaudhry gave suggests backers and portfolio companies may be assessed as part of a grant applicant’s risk profile; entities whose funding traces through multiple layers of VCs could find those links subjected to closer inspection.
The Transportation Department’s public account, delivered by Chaudhry at a sector summit, makes clear the agency is experimenting with AI to trace opaque funding and relationship networks tied to research awards. At the same time, the capability’s absence from the department’s published AI inventory and the lack of an on-the-record departmental response leave outstanding questions about how the tools will be governed, documented and coordinated with existing vetting practices GAO has already scrutinized. Will the department publish more detail on methods, limits and safeguards? That is a question the public record from the summit and the department’s inventory has not yet answered.
