Information Trust Technology
This project develops defensive cyber technology to ensure that data traversing the network remains verified and has not been modified through unauthorized means. Project enhances system access without affecting personnel authentication processes, enhances awareness of user actions and intent within the network, and maintains information provenance from originator to consumer. It will also integrate zero trust principles where access to resources is granted based on continuous risk assessments. Work in this project complements Program Element (PE) 0603457A (C3I Cyber Advanced Development) / Project 8CY (Information Trust Advanced Technology). Work in this project is performed by Command, Control, Computers, Communications, Cyber, Intelligence, Surveillance, and Reconnaissance (C5ISR) Center.
FY2027 planned work Details provided under separate cover.
FY2026 to FY2027 change Funding decrease in details provided under separate cover.
FY2026 plans — current year Will design and develop techniques for protecting data-in-use; will continue to develop solution for uniquely identifying NPE's; will continue to design and develop solutions utilizing risk adaptive access control approach to adjust for graceful degradation of access based on Indicators of Compromise (IoC's); will continue to design and develop suitable adversarial machine learning techniques.
FY2025 accomplishments Will investigate novel methods and techniques for uniquely identifying non-personnel entities (NPE's) (e.g., systems, applications, devices,?robotic process automation (RPA) & services) where Public Key Infrastructure (PKI) certificates are not feasible, (ie. Physical Unclonable Functions (PUF's), Fast Identity Online (FIDO2), etc.) and provide the ability to map them to the Master Device Record (MDR); investigate novel methods and techniques for providing protections of Data in Use; investigate advanced ways to provide graceful, degraded access of resources based on indicators of compromise; research and investigate novel adversarial machine learning methods and techniques.