Why Runtime Matters
Agentic AI systems introduce risk at the moment of execution. A model can receive a clean prompt, retrieve hostile context, choose a risky tool, and produce an unsafe action within the same workflow. Runtime security gives teams policy decisions at the point where business impact can still be prevented.
What to Inspect
Security teams should evaluate user prompts, retrieved content, tool inputs, tool outputs, memory, model responses, and final actions. Treat the agent as a workflow, not as a single model endpoint, and preserve evidence across every step.
Policy Design
Effective policies combine identity, application, data class, retrieval source, tool severity, and business context. Low-risk summarization may proceed automatically, while account updates, payments, data exports, or system changes should require stronger controls or human approval.
Operating Metrics
Measure blocked prompt attacks, sensitive data events, tool-call denials, approval rates, latency, and remediation time. These metrics help security leaders explain AI risk in operational terms instead of abstract model behavior.