How to secure AI in container workloads

The report outlines security challenges introduced by AI in containerized environments—such as expanded attack surfaces, prompt injection, model manipulation, and unmonitored automation—and provides actionable controls: inventory and visibility of AI workloads, thorough threat modeling, hardening and least privilege, runtime behavioral baselining and anomaly detection, integration of container-native controls into DevSecOps, and AI governance. It highlights the value of ML-BOMs to enhance AI/ML supply chain visibility and notes that container dynamism amplifies risk, citing “nullifAI” as an early example of emerging AI supply chain attacks.