Managing Risk From Autonomous AI Agents in the Enterprise
CISOs and risk officers evaluating the operational and regulatory risks of deploying autonomous AI agents without deterministic controls.
El problema
Autonomous AI agents create three categories of enterprise risk: execution risk (the agent takes an action outside permitted bounds), audit risk (you cannot reconstruct or defend the decision), and regulatory risk (the action violates a compliance obligation). All three can be addressed at the architecture level. Execution risk is eliminated by a runtime enforcement layer that evaluates every proposed action before it executes. Audit risk is eliminated by an immutable decision log with policy version and actor identity. Regulatory risk is reduced by designing the enforcement layer to satisfy framework requirements (EU AI Act Article 9–14, NIST AI RMF) by construction. Corules addresses all three at the infrastructure level — not as a post-hoc control.
Cómo lo resuelve Corules
Corules's policy runtime evaluates structured context against compiled CEL expressions — returning ALLOW, BLOCK, or ESCALATE with a reason and audit ID.
Ejemplo de política
// Autonomous agent cannot act outside policy bounds:
// Agent proposes action → Corules evaluates → only ALLOW proceeds
// BLOCK: action violates policy → agent receives reason, does not execute
// ESCALATE: action requires human authority → routed to human reviewer
// ALLOW: action within bounds → executes, logged to immutable ledgerFrequently Asked Questions
What is the difference between 'human in the loop' and 'human on the loop'?
Human-in-the-loop means every decision requires human approval — the AI cannot act without a human sign-off. Human-on-the-loop means the AI executes within defined bounds autonomously, with humans monitoring and intervening only on exceptions (escalations). Corules enables human-on-the-loop: deterministic enforcement within bounds, with escalation for ambiguity.
How do we prevent AI agents from being manipulated through adversarial inputs?
Actor identity is established from signed JWT claims — not from user-supplied text. Policy evaluation is CEL, not an LLM — it cannot be prompt-injected. The enforcement layer is independent of the AI agent and its inputs.
Does every agent type need to be individually governed?
No. The enforcement layer is centralized. Any AI agent that calls the same API inherits the same policy. Adding a new agent type does not require new governance infrastructure — it calls the existing enforcement layer.