2333126
Project Grant
Overview
Grant Description
SBIR PHASE I: SAIFE: TRUSTED AI WITH HARDWARE SECURITY ENFORCEMENT -The broader impact of this Small Business Innovation Research (SBIR) Phase I project is centered on elevating economic and societal well-being by significantly enhancing the security posture of artificial intelligence (AI) and machine learning (ML) hardware and systems, which are increasingly ubiquitous and used in safety/security-critical applications.
As this project analyzes hardware attacks and pioneers new defenses, it ensures a more reliable foundation for AI/ML technologies that society relies upon for healthcare, finance, and national security. The commercial potential is substantial; as developers deploy these fortified systems, they mitigate the risk of costly breaches, fostering trust and accelerating adoption.
Economic benefits also extend to a reduction in expenditure related to cyberattacks and an increase in market competitiveness for secure AI/ML products. Furthermore, by deepening understanding of hardware vulnerabilities and defense mechanisms, the project pushes the frontiers of scientific knowledge in cybersecurity. As a result, the innovations from this project are poised to reinforce critical infrastructure against hardware-centric threats, thereby safeguarding the digital economy and reinforcing the United States' leadership in secure technological advancements.
This Small Business Innovation Research (SBIR) Phase I project conducts a transformative approach to addressing the acute problem of securing AI/ML hardware systems against emerging hardware attacks such as side-channel and fault injection attacks. Recognizing the vulnerability of these systems to hardware exploitation, the project aims to comprehensively analyze the attack vectors and devise innovative defense mechanisms.
The proposed research is set to employ a multi-layered methodology that integrates cutting-edge cryptographic techniques and novel machine-learning algorithms to enhance hardware security. Through rigorous experimentation and validation, the anticipated technical results include the development of trusted hardware modules, the establishment of a benchmarking framework for hardware threat assessment, and the creation of adaptable, resilient defense architectures. This will significantly advance scientific understanding of hardware security in the context of AI/ML, potentially setting a new standard for industry practices, while addressing a critical vulnerability in modern computing infrastructure.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. - Subawards are not planned for this award.
As this project analyzes hardware attacks and pioneers new defenses, it ensures a more reliable foundation for AI/ML technologies that society relies upon for healthcare, finance, and national security. The commercial potential is substantial; as developers deploy these fortified systems, they mitigate the risk of costly breaches, fostering trust and accelerating adoption.
Economic benefits also extend to a reduction in expenditure related to cyberattacks and an increase in market competitiveness for secure AI/ML products. Furthermore, by deepening understanding of hardware vulnerabilities and defense mechanisms, the project pushes the frontiers of scientific knowledge in cybersecurity. As a result, the innovations from this project are poised to reinforce critical infrastructure against hardware-centric threats, thereby safeguarding the digital economy and reinforcing the United States' leadership in secure technological advancements.
This Small Business Innovation Research (SBIR) Phase I project conducts a transformative approach to addressing the acute problem of securing AI/ML hardware systems against emerging hardware attacks such as side-channel and fault injection attacks. Recognizing the vulnerability of these systems to hardware exploitation, the project aims to comprehensively analyze the attack vectors and devise innovative defense mechanisms.
The proposed research is set to employ a multi-layered methodology that integrates cutting-edge cryptographic techniques and novel machine-learning algorithms to enhance hardware security. Through rigorous experimentation and validation, the anticipated technical results include the development of trusted hardware modules, the establishment of a benchmarking framework for hardware threat assessment, and the creation of adaptable, resilient defense architectures. This will significantly advance scientific understanding of hardware security in the context of AI/ML, potentially setting a new standard for industry practices, while addressing a critical vulnerability in modern computing infrastructure.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. - Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding Agency
Place of Performance
Raleigh,
North Carolina
27606-3517
United States
Geographic Scope
Single Zip Code
Mithrilai was awarded
Project Grant 2333126
worth $272,773
from in February 2024 with work to be completed primarily in Raleigh North Carolina United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: SaiFE: Trusted AI with Hardware Security Enforcement
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is centered on elevating economic and societal well-being by significantly enhancing the security posture of Artificial Intelligence (AI) and Machine Learning (ML) hardware and systems, which are increasingly ubiquitous and used in safety/security-critical applications. As this project analyzes hardware attacks and pioneers new defenses, it ensures a more reliable foundation for AI/ML technologies that society relies upon for healthcare, finance, and national security. The commercial potential is substantial; as developers deploy these fortified systems, they mitigate the risk of costly breaches, fostering trust and accelerating adoption. Economic benefits also extend to a reduction in expenditure related to cyberattacks and an increase in market competitiveness for secure AI/ML products. Furthermore, by deepening understanding of hardware vulnerabilities and defense mechanisms, the project pushes the frontiers of scientific knowledge in cybersecurity. As a result, the innovations from this project are poised to reinforce critical infrastructure against hardware-centric threats, thereby safeguarding the digital economy and reinforcing the United States' leadership in secure technological advancements.
This Small Business Innovation Research (SBIR) Phase I project conducts a transformative approach to addressing the acute problem of securing AI/ML hardware systems against emerging hardware attacks such as side-channel and fault injection attacks. Recognizing the vulnerability of these systems to hardware exploitation, the project aims to comprehensively analyze the attack vectors and devise innovative defense mechanisms. The proposed research is set to employ a multi-layered methodology that integrates cutting-edge cryptographic techniques and novel machine-learning algorithms to enhance hardware security. Through rigorous experimentation and validation, the anticipated technical results include the development of trusted hardware modules, the establishment of a benchmarking framework for hardware threat assessment, and the creation of adaptable, resilient defense architectures. This will significantly advance scientific understanding of hardware security in the context of AI/ML, potentially setting a new standard for industry practices, while addressing a critical vulnerability in modern computing infrastructure.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
CA
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 6/10/24
Period of Performance
2/15/24
Start Date
1/31/25
End Date
Funding Split
$272.8K
Federal Obligation
$0.0
Non-Federal Obligation
$272.8K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2333126
Additional Detail
Award ID FAIN
2333126
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
RNWUGVLUN7M5
Awardee CAGE
9GWB7
Performance District
NC-02
Senators
Thom Tillis
Ted Budd
Ted Budd
Modified: 6/10/24