Runtime Policy is the emerging security layer that enforces dynamic, context-aware rules during AI agent execution.
Traditional security policies are defined at design or deployment time. Once an agent is running, these policies become outdated the moment the agent discovers new tools or changes its approach.
Runtime Policy is a security model that continuously evaluates and enforces rules during the execution of AI agents, rather than relying solely on pre-defined static policies.
It acts as a real-time decision engine that inspects every proposed action against current context, risk thresholds, compliance requirements, and organizational intent before execution.
They can take paths that were never anticipated during policy creation. Static policies cannot adapt to novel behavior.
Regulated industries need evidence that every action was evaluated against policy at the time it occurred, not just at deployment.
As agents gain more tools and autonomy, the potential damage from a single compromised or misbehaving agent increases dramatically.
One-time authorization is insufficient. Trust needs to be re-evaluated with every significant action an agent takes.
This domain owns the language of the next major security layer in AI infrastructure. As enterprises move from experimental agents to production systems, Runtime Policy will become a foundational requirement.
Serious inquiries from teams building the next generation of AI infrastructure are welcome.