From Shadow AI to Detection and Response: Closing the Visibility Gap at Machine Speed

AI technologies have rapidly integrated into various applications, altering the traditional infrastructure landscape by introducing ephemeral, abstracted, and indirectly triggered components that evade conventional security tools. This evolution, termed "shadow AI," poses significant security challenges due to its ability to operate at machine speed and remain undetected by traditional methods, leading to potential risks like unauthorized data access and misuse. Stream's AI workload discovery addresses these challenges by capturing and correlating AI components with their executing workloads, identifying anomalies such as new model invocations from non-AI workloads or unapproved MCP server connections, and linking them to potential security incidents. By mapping these detections to the MITRE ATLAS framework, Stream enables real-time response actions that contain threats before they escalate, offering a comprehensive approach to AI detection and response within cloud environments. As AI becomes integral to every workload, ensuring visibility and control over AI operations is crucial to mitigate risks associated with prompt injection, tool abuse, and data exfiltration.