TIGARS guidance on diversity and defence in depth for ML

Overview

Discusses diversity in sensors, models, toolchains, training data and architecture to reduce reliance on a single ML component. It cautions against assuming independent failures and recommends considering failure correlation alongside added complexity and attack surface.