Understanding AI workloads on Linux

Hi tejaskashyap, in the environments I usually see, AI workloads on Linux often show up as Python services, containerized inference APIs, GPU-enabled batch jobs, or Jupyter-style experimentation hosts. From a security angle, the common gaps are package provenance, exposed notebooks, weak secrets handling, and containers running with more privilege than needed. It would help admins if Defender surfaced GPU/process context, model-serving ports, container image lineage, and unusual package activity around ML frameworks.