AI is still a workload: A practical guide to securing AI workloads

This article outlines best practices for securing AI/LLM workloads across three scenarios—employees using SaaS LLMs, customer-facing chatbots, and self-trained models—by detailing key risks (data leakage, prompt injection/jailbreaks, model poisoning, credential theft/LLMJacking, and denial-of-service) and prescribing mitigations such as input/output sanitization, strong access controls and MFA, secrets management, network segmentation, rate limiting, encryption, DSPM/CSPM/CIEM/CDR-driven monitoring and detection, and adherence to privacy and compliance standards.