AI Infrastructure Security

AI infrastructure security addresses the unique challenges of securing the compute, storage, networking, and orchestration systems that power machine learning workloads. Unlike traditional IT infrastructure, AI systems require specialized hardware (GPU clusters, TPUs), massive data pipelines, experiment tracking platforms, model registries, and serving infrastructure — each introducing attack surface that conventional security tools were not designed to protect.

GPU clusters represent high-value targets for attackers. A single NVIDIA H100 GPU costs tens of thousands of dollars, and organizations often run clusters worth millions. Cryptojacking, unauthorized training runs, and GPU memory side-channel attacks are real threats. ML pipeline security is equally critical — tools like Kubeflow, MLflow, Airflow, and custom training pipelines handle sensitive data and model artifacts, often with insufficient authentication, authorization, and audit logging.

Model serving infrastructure exposes trained models as API endpoints, creating attack surface for model extraction, denial of service, and adversarial input attacks. Secrets management is particularly challenging in ML environments where API keys, cloud credentials, and data access tokens are frequently embedded in notebooks, configuration files, and container images. Securing AI infrastructure requires adapting DevSecOps practices to MLOps while addressing the unique requirements of GPU workloads, large-scale data movement, and model lifecycle management.