AI Security

AI workloads are dynamic, abstracted, and deeply embedded into modern applications, making them hard to track with traditional security tools. This creates a growing “shadow AI” problem where models, agents, and tools operate without oversight. Our AI workload discovery solves this by continuously analyzing cloud-native logs and runtime signals to identify all AI components in your environment—including hosted services, models, tools, and MCP servers. Each component is correlated to the workload executing it, the identity behind it, and its behavior over time. By baselining this activity, security teams can quickly spot anomalies such as new models, unauthorized integrations, or unusual usage patterns. More importantly, this visibility feeds directly into detection and response, enabling full attack storylines that include the AI layer.

Stream Security now supports native ingestion of OpenAI Platform audit logs, extending real-time threat detection and response to your AI control plane. Security teams gain immediate visibility into sensitive administrative activity across OpenAI Platform Organizations including API key lifecycle events, service account creation, identity and role changes, project configuration updates, and security control modifications like IP allowlists and SCIM. The integration is agentless, easy to enable, and provides out-of-the-box detections so teams can investigate and respond faster from a unified cloud security platform.

Stream’s red-team tests have uncovered a way to manipulate the AI triage process by injecting misleading information into cloud metadata.