AI/ML at the Edge for Federal Deployments

Running machine learning models directly on edge hardware allows federal deployments to make faster decisions without shipping sensitive data back to a central cloud. Our plan focuses on validating model integrity before deployment, monitoring for model drift and adversarial manipulation in the field, and ensuring that training data and inference logs are handled in accordance with federal data governance requirements. This lets agencies benefit from real-time AI/ML capabilities without introducing new, ungoverned attack surfaces.