AI Audit Trail for Enterprise Decisions

The answer

Every decision evaluated by Corules produces an immutable audit entry containing: policy set version, normalized input hash, actor identity (from signed claims, not self-report), outcome (ALLOW/BLOCK/ESCALATE), and the specific rule path that determined the outcome. Decisions are replayable: given the same policy version and normalized input, evaluation produces identical output. This means an auditor can take any historical decision, reproduce it exactly, and verify that the policy in effect at the time produced the stated outcome.

How it works

Corules's policy runtime sits in the enforcement path between your AI agent and the action it wants to take. The agent sends a structured context payload to /v1/validate. Corules evaluates the context against a compiled CEL policy set and returns a structured decision — ALLOW, BLOCK, or ESCALATE — with a reason and audit ID.

Every decision is recorded in an immutable audit ledger. You can replay any past decision by providing the policy_set_version and the normalized input hash — the result will be identical.

Policy example

Policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated in microseconds at request time.

// Every evaluation produces an audit record:
{
  "audit_id": "aud_01J...",
  "policy_set_version": "pset_v3.2.1",
  "input_hash": "sha256:4a7b...",
  "actor_id": "user_01J...",
  "outcome": "BLOCK",
  "violation": "discount_pct > max_discount_by_tier['standard']",
  "evaluated_at": "2026-02-23T08:14:22Z"
}

Frequently Asked Questions