🤖 AI Summary
Traditional enterprise security models, reliant on static perimeters, struggle to address the dynamic risks introduced by production-grade AI agents operating within authorized workflows. This work proposes the first five-plane reference architecture for runtime governance of AI agents—spanning inference, network, identity, endpoint, and data—and introduces core primitives including arbitrary-point interception, composite subjects with capability decay, and structured audit evidence. By extending policy enforcement from atomic subjects to decay-aware composite subjects, the framework defines six interruption primitives and four correctness invariants. Evaluated across five real-world workflows, it successfully mitigates seven threat classes, achieves microsecond-scale policy decisions, and validates correctness of capability decay, audit reconstructability, and tamper resistance, thereby filling a critical gap in dynamic governance for agent-driven workflows.
📝 Abstract
Enterprise security was built to govern data boundaries: the protected surface was data at rest and in transit, and the controls -- access control, data-loss prevention, perimeter inspection -- governed crossings of that boundary. Production AI agents dissolve this assumption. An agent reads context, calls tools, invokes connectors, and modifies systems of record on an enterprise's behalf, so risk moves inside the workflow, into sequences of individually-permitted actions that may transform a business process no one authorized. Existing policy engines do not extend to this regime: they evaluate request-time decisions against atomic principals, where agentic systems require stateful evaluation against composite principals whose authority attenuates through delegation chains.
We present a reference architecture for the runtime governance of production agents, built from four composable primitives: a five-plane decomposition (a reasoning plane that adjudicates intent, and four enforcement planes -- network, identity, endpoint, data -- that realize the decision), stop-anywhere mediation, composite principals with capability attenuation, and audit as a structured evidence substrate. We define a taxonomy of six interruption primitives that generalize allow and deny, state and argue for four correctness invariants, and demonstrate the foreclosure of seven production-agent threats across five concrete workflows. A reference implementation of the policy-engine core supplies measured evidence: attenuation correctness and evidence reconstructability hold on every trial, adjudication runs in single-digit microseconds, and the audit substrate's tamper-evidence behaves exactly as designed. We are explicit about scope: the architecture governs delegated action, not model behavior, and a full-system evaluation against a live agent benchmark is the invited next step.