The FDA's January 2025 AI-Enabled Device guidance identifies seven AI-specific cyber threats. Only one of them, model evasion, lives at runtime on the device. The other six attack the pipeline: training data, model registries, evaluation logic, and deployment.
Most testing programs today only cover the deployed model. That means they are addressing one-seventh of what the FDA is explicitly asking about. Data poisoning, model inversion and stealing, data leakage, overfitting, model bias, and performance drift all sit upstream of the running model.
Our approach expands the digital twin to include the full AI pipeline as an associated system, then structures testing to cover the whole lifecycle. Runtime testing stays focused and purpose-built. Every test case maps back to a specific FDA-identified threat, executed against real pipeline components, producing defensible evidence for premarket and postmarket.
This is not "test the model." It is "test everything that touches the model."
Start with one device. We build the twin from documentation your quality system already produces, run AI discovery remotely, and show you the graph: the handful to fix, and the evidence for everything else.
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