SBIR-STTR Award

SEAMLESS: Secured and Assured Machine Learning Systems
Award last edited on: 8/30/2022

Sponsored Program
SBIR
Awarding Agency
DOD : AF
Total Award Amount
$999,856
Award Phase
2
Solicitation Topic Code
AF212-D002
Principal Investigator
Adam Langdon

Company Information

EDAptive Computing Inc

1245-G Lyons Road
Dayton, OH 45458
   (937) 433-0477
   info@edaptive.com
   www.edaptive.com
Location: Multiple
Congr. District: 10
County: Montgomery

Phase I

Contract Number: N/A
Start Date: 12/15/2021    Completed: 6/15/2023
Phase I year
2022
Phase I Amount
$1
Direct to Phase II

Phase II

Contract Number: FA875022C0050
Start Date: 12/15/2021    Completed: 6/15/2023
Phase II year
2022
Phase II Amount
$999,855
ECI's SEAMLESS solution represents an end-to-end enterprise framework and tool set that will allow users to perform security vulnerability and risk assessment for a machine learning system. The SEAMLESS solution utilizes an integrated data analysis testing environment and machine learning development framework to perform analysis on the different componenets of a machine learning (ML) system. As a result, users can identify vulnerabilities and generate a risk matrix for assessment and mitigation of the risks to minimize asset loss and improve warfighter safety. The SEAMLESS solution will incorporate and leverage ECI capabilities to create a user-friendly front end to highlight the vulnerable componenets of a user-provided ML system and associated requirements. It will display flagged results and different mitigation options to the user to mitigate the key risks highlighted from the vulnerability assessment of the ML system.