Topic

Runtime-Policy

Runtime policy refers to AI governance rules and controls that are enforced during the actual execution of AI systems and models in production environments. These policies govern model behavior, data handling, output validation, and resource usage while the system is actively operating, rather than during development or deployment stages. Runtime policies are critical for enterprise AI governance because they enable real-time monitoring, prevent harmful outputs, ensure compliance with regulations, and allow organizations to intervene immediately if models behave unexpectedly or violate business or ethical guidelines.

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Corporate PolicyGlobal2026-06-02

Delegated Authority in Agentic AI Requires Formal Runtime Boundaries, Palo Alto Networks Guidance Argues

Palo Alto Networks has published a governance guide framing agentic AI oversight as a problem of delegated authority, arguing that enterprises must define explicit runtime boundaries, scoped tool permissions, and task-level autonomy limits before deploying AI agents. The guide introduces a structured distinction between human-in-the-loop and human-on-the-loop control models, tying each to task criticality. It also calls for pre-deployment impact assessments as a baseline governance requirement for agentic systems.

agentic-ai human-oversight runtime-policy tool-permissions delegated-authority