Modern AI infrastructure increasingly depends on autonomous runtime systems operating continuously across distributed environments. AI systems now: orchestrate cloud-native infrastructure automate operational workflows coordinate distributed runtime execution execute regulated compute actions operate continuously at machine speed Traditional security systems primarily: monitor runtime activity analyze telemetry after execution inspect logs retrospectively respond after operat

Modern autonomous infrastructure increasingly operates through machine-speed execution systems. AI runtimes now: coordinate distributed workflows orchestrate cloud-native infrastructure automate operational decisions execute regulated compute actions manage continuous runtime processes Traditional security systems primarily: observe activity collect telemetry inspect logs after execution respond after runtime activation That model no longer establishes sufficient runtime inte

Modern infrastructure still largely depends on implicit runtime trust assumptions. Systems frequently assume: workloads are trustworthy once deployed runtime environments remain un compromised orchestration layers enforce sufficient control infrastructure visibility equals governance execution can safely occur before validation Autonomous compute systems invalidate those assumptions. AI systems increasingly execute: infrastructure actions orchestration workflows financial ope

Autonomous systems are rapidly expanding across: AI inference environments financial execution infrastructure healthcare compute systems distributed cloud orchestration defense autonomy platforms edge and multi-cloud runtime environments Modern infrastructure increasingly allows machines to: initiate execution coordinate workflows access regulated systems orchestrate compute resources trigger operational actions influence physical and digital environments This creates a criti