We use “data slices” to evaluate our cybersecurity ML systems for the asset attribution task at Palo Alto Networks. For us, data slices are the dual of feature explanations. By segmenting our data into subunits with known properties, we can verify that improvements to address model blindspots actually succeed, we can detect model regression, and we can characterize differences between models.

I described our approach in a paper on using data subsets to evaluate the ML internet asset attribution problem, which was accepted to the NeurIPS Data-Centric AI (DCAI) Workshop held on 14 December 2021. The DCAI workshop focused on practical tooling, best practices, and infrastructure for data management in modern ML systems. The paper discusses two themes: (1) data slices, and (2) their application to our asset attribution task in cybersecurity.