The observability collection links measurements to the questions people need to answer. Follow an AI operation across model calls and tools, explain token consumption without hiding uncertainty, and collect security events without unnecessary sensitive content. Distinguish performance measurements, policy decisions, and confirmed execution outcomes. Delayed or missing evidence should remain visible rather than being silently interpreted as a clean result. Use these guides to review event fields, access permissions, alert ownership, and the ability to reconstruct a small synthetic investigation from beginning to end.
API incident response: a practical playbook from signal to recovery
Prepare a calm response to suspicious API activity with clear authority, scoped containment, and evidence-based recovery.
AI token budgets: control consumption before the bill arrives
Design per-request, workflow, and tenant budgets with explicit estimates, reconciliation, and safe stopping behavior.
AI API monitoring: connect latency, usage, and policy decisions
Build an observability plan that follows the whole AI workflow instead of stopping at a successful HTTP response.
API security logging: useful evidence without a second data leak
Build decision-focused security events that support investigations without copying production secrets into your logs.