Weapon System Assurance through AI Technologies

To address the growing cybersecurity challenges in Army aviation and missile systems, DESE Research proposes leveraging Large Language Models (LLMs) to enhance System Security Engineering (SSE) practices. The current Mission-Based Cyber Risk Assessments (MBCRAs), such as Weapon System Assurance (WSA), are resource-intensive and limited in scalability. Alternative approaches, including monolithic LLMs or generic AI-driven tools, carry risks such as inefficiency in processing diverse SSE data, over-reliance on computational resources, and lack of adaptability to dynamic mission requirements. These risks can lead to incomplete risk assessments, excessive costs, and reduced operational readiness.DESE addresses these challenges by developing a modular LLM system tailored for SSE, designed to reduce MBCRA execution costs by 30%, improve vulnerability detection accuracy, and accelerate risk assessment timelines. This modular approach optimizes processing across multimodal data types, including engineering designs, intelligence reports, and software vulnerability scans, ensuring comprehensive and precise risk assessments. The solution is engineered to comply with Department of Defense Responsible AI principles, emphasizing transparency, auditability, and scalability for military environments.The Phase I effort focuses on designing and validating this innovative architecture, with tasks including dataset curation, modular LLM design, prototype selection, and feasibility demonstration. Expected outcomes include a transformative LLM framework that integrates seamlessly into Army SSE operations, mitigating the risks of alternative approaches and significantly enhancing cybersecurity efficiency and efficacy across the Army and beyond.