Falco Feeds enhances Falco’s capabilities by providing open-source companies with expert-written rules that adapt to new threats. AI and machine learning workloads, though rooted in traditional software engineering, come with unique operational challenges. These workloads run on compute, storage, and networking platforms, making them compatible with familiar infrastructure delivery models. The focus here is on AI applications deployed within Oracle Cloud Infrastructure (OCI) and Oracle Kubernetes Engine (OKE), which are gaining traction due to their security, cost efficiency, and compliance features. The article outlines the expanding attack surface in AI, which includes everything from physical GPUs to models and APIs. Tools like Kubeflow and MLflow manage the lifecycle of machine learning models, while inference engines like TensorRT-LLM operate with significant privileges. Vulnerabilities can arise at any layer, leading to issues like model theft or data breaches. Understanding OCI's shared responsibility model is vital; Oracle manages the control plane, while customers must secure their applications and data operations. This division of responsibilities highlights the need for organizations to take application security seriously. Recent incidents underscore the increasing sophistication of attacks targeting AI systems. Vulnerabilities from July 2025 to January 2026 include unauthorized access to AI pipelines and container escapes. Many of these attacks exploit weaknesses in supply chains or zero-day vulnerabilities. Protecting AI workloads requires real-time monitoring and a robust security posture to detect abnormal behaviors. Strategies like CI/CD vulnerability management and runtime detection can mitigate risks before they escalate. Sysdig’s approach to AI workload protection emphasizes real-time visibility and proactive measures to secure applications throughout their lifecycle.