How MDA Uses C3 AI to Accelerate Missile Defense Threat Modeling
The U.S. Missile Defense Agency partnered with C3 AI to deploy a generative AI platform for missile threat modeling and simulation. The solution delivers a 1000x increase in available threat data and reduces data generation time from weeks to minutes. This capability enables MDA to stress-test missile defense systems at unprecedented scale in secure, classified environments.
Impact
1000x
Increase in available threat data per scenario
99%
Reduction in time to generate threat data
26 billion
Data points used to train threat kinematic ML model
Challenge
MDA's physics-based threat modeling approach produced insufficient volumes of data, leaving missile defense systems inadequately stress-tested and unable to meet growing simulation demands. The agency needed a scalable way to generate high-fidelity threat data quickly within a secure, classified environment.
Solution
C3 AI deployed its Agentic AI Platform and the C3 AI Parametric Threat Generative Modeling application within MDA's classified environment, using ML models trained on 26 billion data points to rapidly generate large-scale synthetic missile threat data packages.
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Full Story
The U.S. Missile Defense Agency (MDA) is a major research, development, and acquisition organization within the Department of Defense, responsible for developing and fielding reliable defenses against current and evolving missile threats. Its systems span ground, sea, and space-based sensors, interceptor missiles, and command and control infrastructure — all of which depend heavily on accurate modeling and simulation to guide design, architecture, and operational planning.
Live missile defense testing is extraordinarily expensive and logistically complex, making high-fidelity simulation an essential substitute for real-world exercises. However, MDA's existing approach to generating threat missile data relied on physics-based models that produced only a limited volume of data — just enough to meet minimum test coverage thresholds. This left critical gaps: components and architectures were inadequately stress-tested, system design envelopes were sparsely defined, and the agency struggled to meet growing demand for threat data as testing requirements evolved.
To overcome these limitations, MDA partnered with C3 AI to develop and deploy the C3 Agentic AI Platform alongside the C3 AI Parametric Threat Generative Modeling application within MDA's secure, classified environment. The solution uses machine learning trained on 26 billion data points to generate synthetic threat kinematic models at massive scale. A proof of concept was demonstrated with a representative threat object, with a full threat definition slated for completion by the end of the fiscal year.
The results were transformative. The new AI-powered approach delivers a 1000x increase in the volume of threat data available for any given scenario, while cutting the time required to generate that data by 99% — from weeks down to minutes. The C3 Agentic AI Platform achieved Authority to Operate (ATO) accreditation for its V8 platform within MDA's secure environment and is now globally accessible across all MDA sites with appropriate connectivity.
Looking ahead, MDA plans to begin populating its threat library in FY26 with new threat definitions for use by stakeholders. Beyond threat data generation, the C3 Agentic AI Platform is positioned to serve as a shared Enterprise AI toolset across MDA development teams, accelerating the adoption of state-of-the-art AI techniques across the agency's broader missile defense mission.
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