How to Ensure LLM Outputs Comply With Internal Policy
Enterprise teams asking how to guarantee that LLM-generated recommendations and actions stay within internal policy bounds before execution.
Das Problem
LLMs are probabilistic — the same prompt can produce different outputs across runs. Internal policies are deterministic — a 25% discount cap means exactly 25%, every time, without exception. You cannot rely on the LLM to self-enforce policy because its outputs are not guaranteed to be consistent. The answer is a runtime enforcement layer that intercepts every LLM-proposed action and evaluates it against compiled policy before execution. Corules sits in this path: the LLM proposes, Corules evaluates, only compliant proposals execute. Policy is expressed in CEL (Common Expression Language), compiled at publish time, and evaluated in milliseconds — no LLM in the enforcement path.
So löst Corules es
Corules's policy runtime evaluates structured context against compiled CEL expressions — returning ALLOW, BLOCK, or ESCALATE with a reason and audit ID.
Richtlinienbeispiel
// LLM proposes: { discount_pct: 0.35, customer_tier: "standard" }
// Corules evaluates against compiled policy:
discount_pct <= params.max_discount_by_tier[context.customer_tier]
// → 0.35 <= 0.25 → false → BLOCK
// LLM proposal does not execute.Frequently Asked Questions
Why can't we just prompt the LLM to follow policy?
Prompt-based policy enforcement is probabilistic — the model may interpret the policy differently across runs, hallucinate policy details, or be overridden by adversarial user inputs. Runtime enforcement with CEL is deterministic and not subject to prompt injection.
Does this require changes to our LLM integration?
Your LLM integration needs one additional API call: POST /v1/validate before any proposed action executes. The LLM's structured output becomes the decision payload. No changes to the model, prompts, or reasoning pipeline.
What happens if the LLM produces a malformed output?
Missing required fields escalate. Unrecognized fields are rejected per tenant configuration. Malformed payloads never silently pass — the safe default is always ESCALATE or BLOCK.