Enterprise AI becomes more governable when model calls, prompt defenses, and agent API permissions are enforced in the live request path

Runtime AI controls are most useful when the same request path can govern model choice, prompt risk, spend, and what agents may do through enterprise APIs

Source: Fastly

Fastly has announced AI Runtime Control, AI Firewall, and expanded API Security capabilities. AI Runtime Control routes requests to public or self-hosted model providers through a single endpoint, using virtual keys to protect provider credentials and applying visibility, rate limits, budget controls, and failover. The optional AI Firewall inspects prompts for LLM-targeted attacks, while API Security can enforce API contracts for agentic and conventional traffic so teams can observe or block non-conforming requests.

Why this matters: The value is the control pattern, not a reason to adopt a specific edge platform. Put an enforceable policy point between applications and model providers so that routing, provider credentials, spend limits, and availability decisions are visible and reviewable. Separately define how prompt-attack matches are tested, logged, overridden, and improved. For agent actions, map each permitted API operation to an identity, scope, business purpose, and denial path; then validate that the policy point receives enough context to distinguish an approved workflow from a broadly authenticated request.