January 01, 2024
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Awarding Agency: US Army
Contract Number: W5170125CA232
Project Description: The U.S. Army’s modernization strategy emphasizes decision speed and operational readiness, yet current long-range communication systems are ill-suited to handle the expanding streams of sensor data from advanced vehicles and weapon platforms. Predictive Logistics, which enables commanders and maintainers to anticipate equipment needs, has demonstrated value but remains limited by data transfer constraints outside motorpools.
Forward Edge-AI, in partnership with Verizon, is developing a secure wireless data transfer solution leveraging Isidore Quantum®. Operating at Layer 2 (MACSec), Isidore Quantum® is protocol-agnostic, hardware-agnostic, and compliant with the CNSA Suite 2.0, making it resistant to quantum computing threats today. It is also significantly faster to implement and more cost-effective than alternative encryption methods, while remaining non-CCI, easing adoption and sustainment.
The solution architecture integrates Isidore Quantum® with Verizon’s cellular and satellite backhaul capabilities to transmit vehicle sensor data securely to Army Vantage dashboards in both CONUS and OCONUS environments. Phase I will establish technical feasibility through five objectives: defining data parameters, determining integration feasibility, developing a TRL 5 proof-of-concept prototype, evaluating network performance in Denied, Degraded, Intermittent, and Limited (DDIL) environments, and assessing user adoption.
The solution addresses critical gaps in military sustainment by combining quantum-resistant encryption and secure 5G/satellite backhaul with the power of agentic AI. This integration enables predictive logistics systems to shift from reactive monitoring to autonomous, proactive decision-making—automating supply management, optimizing logistics scenarios, performing predictive maintenance, and adapting in degraded communication environments. The result is a resilient, future-proof logistics framework that enhances operational efficiency, reduces human burden, and delivers commanders decision superiority, creating a self-healing, adaptive sustainment network that strengthens mission readiness in an era of global uncertainty.
Moreover, integrating blockchain with Isidore Quantum® encryption and Agentic AI creates a secure, autonomous, and transparent predictive logistics framework that strengthens both military and commercial supply chains. Blockchain provides an immutable, decentralized ledger that guarantees trusted data provenance and traceability, while Isidore Quantum® ensures quantum-resistant, zero-trust security for data in transit. Building on this secure foundation, Agentic AI leverages blockchain-verified records to autonomously forecast needs, optimize logistics, and execute maintenance or resupply through smart contracts with minimal human intervention. Together, these technologies deliver decision superiority, operational resilience, and future-proof protection against emerging threats, ensuring logistics operations remain trustworthy, adaptive, and mission-ready.
This Phase I effort lays the foundation for Phase II, where prototypes will be refined and scaled toward full operational deployment, ensuring the Army remains resilient against emerging cyber and quantum-era threats.
Key Words
Artificial Intelligence, Drones, MRO, Sustainment, Supply, CANBUS, body worn sensors, Post quantum cryptography (PQC), predictive logistics, secure wireless communications, Army Vantage, Isidore Quantum®, Verizon 5G, satellite backhaul, MACSec, CNSA Suite 2.0, DDIL environments, tactical networks, future-proof security, non-CCI cryptography, data integration, military logistics.
Phase III SBIR - Business Transformation at the Department of the Treasury/IRS
Awarding Agency: Department of the Treasury/Internal Revenue Service (IRS)
Contract Number: 2032H5-24-P-00097
Relevant Phase I/II Award Numbers: AN 1938135, AN 2028451, Award #2327618 | Agency: National Science Foundation
Description: Forward Edge-AI is working with the agency's Information Technology executives, staff, and other stakeholders to provide data analytics support to the TBM program. The work includes analysis of TBM data to identify trends, patterns, and anomalies that can provide insights into the organization’s IT spending.
Forward Edge-AI is also leveraging data forensic techniques derived from a National Science Foundation SBIR to determine data sources and ownership for Servers, Data Centers, Storage, Applications and Services and other information to determine lineage and provenance of data sources.
Key Words:
AWS, Business Transformation, Data Analytics, Data Governance, Data and Digital Forensics, Forecasting, and Technology Business Management
Awarding Agency: National Science Foundation
Award Number: 2327618
Description: Develop a blockchain/DLT to ensure the provenance, integrity and quality of space-based Machine Learning algorithms and associated training data (counter adversarial Artificial Intelligence). The CASI-D project focuses on the development of a Blockchain solution to manage the governance and provenance of ML/AI algorithms and associated training data for CubeSats and other space infrastructure. Phase I addresses the growing concerns around adversarial AI. At the core of the CASI-D project is the development of an advanced a Blockchain to support its governance framework, including automated auditing, risk assessment, and compliance monitoring. By integrating these technologies, the project enhances the ability to detect and address potential issues in real-time, ensuring the security of ML/AI models developed for on-orbit systems.
The CASI-D project complements the Space COMSEC project by establishing robust AI governance frameworks ensure the lineage and provenance of Isidore Quantum’s ML models and training data. Together, Isidore Quantum® and CASI-D creates a secure, transparent, and sound environment for space communications, balancing technological innovation with governance.
Key Words
Adversarial AI AAI), AI, Blockchain/DLT, Counter Adversarial Artificial Intelligence, CubeSat, Cybersecurity, Data Drift, Deep Learning, DevSecOps, Electronic Discovery, Forensics, Internet, Physics-Based Models, Model Drift, Space, Space-Based Manufacturing, Training Data Provenance, Trustworthy Artificial Intelligence, Identity Management (ICAM), IPV6, Poison Data
Awarding Agency: National Science Foundation
Award Number: 2335995 (Subcontract to the University of Texas San Antonio)
Description: The Decentralized Autonomous Organization Collaboration Hub (DACH) is a groundbreaking national collaboration initiative to foster continuous innovation in the digital asset ecosystem. In an era where digital assets, such as cryptocurrencies, have attained over a trillion dollars in market value, the need for public and private collaboration is not just important, but essential.
Key Words
Not Applicable (this project is not an SBIR or STTR)
Awarding Agency: National Science Foundation
Award Numbers: AN 1938135, AN 2028451
Description: We developed a method for storing data in a blockchain in a high-performing manner, to ensure quality training of ML/AI models, training data (data governance) and production level inference. The innovation addressed the challenge of data drift and adversarial AI attacks where the provenance of the data is unknown. The blockchain also facilitates electronic discovery throughout the complaint filing and litigation process.
Key Words:
AWS, Android, Artificial Intelligence (AI) (CNN/RNN), AI Test and Evaluation, Blockchain/Data Governance, Data Analytics, Data Lake, Counter Adversarial AI, Deepfakes, Enterprise Knowledge Graph, Machine Learning, Microsoft Azure, Natural Language Processing (NLP), Large Language Models (Generative AI (GAI)), Honeypots, Intelligent Automation, Predictive, iPhone, Robotic Process Automation, Semantic Reasoning, Sentiment Analysis, Taxonomy
Cybersecurity, Digital Transformation, Financial Systems/Fintech, Electronic Discovery, Fraud Prevention, Misinformation, Disinformation, Malinformation, Identity Management (ICAM), Telecommunications, Trusted AI, Adversarial AI
Ethical AI, Trustworthy AI, Responsible AI, Data Engineering, Data Governance, Training Data, Validity and Reliability, Safety, Security and Resiliency, Accountability and Transparency, Explainability and Interpretability, Privacy, Fairness with Mitigation of Harmful Bias, Model Drift, Data Drift
Awarding Agency: Air Force
Contract Number: FA864921P1006
Description: In the Phase I SBIR, Forward Edge-AI, Inc. augmented a miniature, Commercial-Off-The-Shelf (COTS) Raman instrument with Machine Learning/Artificial Intelligence (ML/AI), new signal processing techniques, and Surface-Enhanced Raman Scattering (SERS) for quick stand-off identification of trace chemicals and microbes (e.g., COVID) in a field setting, thereby bringing laboratory grade science to the edge. Included a Blockchain and Distributed Ledger Technology to manage the governance of Raman Spectra and award crypto currency to researchers who contribute Raman spectra.
Key Words:
Artificial Intelligence (CNN/RNN), Android ATAK, Blockchain, Federated Learning Marketplace, Machine Learning, Microsoft Azure, signal processing (Wavelet and Fourier transformation), Raman Spectroscopy
Chemical, Biological, Radiological, and Nuclear (CBRN) detection, explosives detection, viruses, bacteria, Expeditionary Environments, field, standoff
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